Calculate the weighting factors

In the example used, there are more female respondents than male respondents, and the instructions show how to change the sample proportions to represent half the respondents as female and half as male, without changing the current base.

1. Click to display the Analysis Definition dialog for a table.
2. In Analysis, enter the variable that you want to weight. Build the table and note the totals for each code.

3. Calculate the Weighting Factors for each code. To calculate the value, divide the target figure (384÷2=192) by the actual figure.
   • Code 1, Male = 192 ÷ 152 = 1.263
   • Code 2, Female = 192 ÷ 232 = 0.828

Create the weight

1. Click to display the Weights window.
2. Click to add a new weight and specify the Weight Details as follows:
   • Name: WT1
   • Label: Weight male/female
   • Decimal places: 3
   • Number of codes: 2

3. Click to save the weight.

Use the weight in an analysis

1. Click to redefine the table or to display a new Analysis Definition dialog.
2. In Analysis, enter the variable that you want to weight.
3. Specify the Weight as WT1 (Q12), which tells Snap to use weight WT1 based on the results for Q12.
4. Select the Cells tab. In the Accuracy section set the Calculations d.p. to 3 decimal places to avoid rounding errors.
5. Click OK to build the table. Check that the table shows the Weighted Values correctly, in this example there should be 103 males and 103 females. Any errors are due to rounding errors or the incorrectly calculated weighting factors.

The table shows the Unweighted Base and the Weighted Base. You can exclude the Unweighted Base in the Tailor | Analysis option.

The weighting can be applied to any other tables and charts. However, there are 2 limitations that multiple-response variables cannot be weighted and you can only use one weight in an analysis.

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Open this dialog by clicking Fill in the analysis Map Control Editor.

Select the drop down to insert the {value} field at the current cursor position.

Set the number of decimal places for the calculated values displayed in the legend.

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Open this dialog by clicking Fill in the analysis Map Control Editor.

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Open this dialog by clicking Fill in the analysis Map Control Editor.

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Open this dialog by clicking Fill in the analysis Map Control Editor.

Check Visible to display the legend on the analysis map. Select a radio button to position the legend.

If you select Custom location, you must specify the dimensions of the legend box. For all other locations, the box is auto-sized. The number of items displayed in the legend is the same as the number of data points defined in the shading.

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Open this dialog by clicking Fill in the analysis Map Control Editor.

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The Designer tab contains the tools to load and edit an analysis map.

First, you need to load a map control or an image into the map control. The image used for the map control can be one of the following:

• Snap XMP map control
• image
• HTML image map

Snap Surveys provides a range of rating scales that you can download and use.

After you have loaded the images into the map control, you are able to

• define clickable areas of the image with different shapes
• click Fill to change the shading, legend location, legend backdrop, legend font and legend labels
• assign areas of the image to the question codes
• zoom the map control
• save the map control for future use

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Enter an expression describing the table cell data to insert the current table cell data into the report field, when you run the report.

The expression is defined as the table name, and an expression identifying the cell to be inserted. You can use the appropriate operators within the expression, e.g., A21 R1C1 – A22 R1C1 where both table cells contain quantities.

The cell referred to can be

• A row label description: tablename RNlabel.
• A column label description:  tablename CNlabel.
• A data cell described by its row and column number: tablename RNCN.
• A statistic value described by its row and statistic number, where the statistic number is the position in the list of statistics, S1 being the top, given by tablename RNSN.

You can also use the expression tablename empty to test if a specific table contains any data.

You can choose to modify the case and set the number of decimal places to display.

The post New Cell Value Field dialog appeared first on SnapSurveys.

Statistic	Description
Count	The number of data cases
Mean	This is often called the average. It is defined as the sum of the items divided by the number of items. For example, for ten responses Mean = (1 + 2 + 3 + 4 + 3 + 4 + 5 + 4 + 6 + 2) = 34 10 = 3.4
Mode	The mode of a distribution is the most frequent or most popular item. If two values tie for the mode, Snap chooses the lower. With the same ten responses: 1, 2, 2, 3, 3, 4, 4, 4, 5, 6 Mode = 4, since 4 is the most frequently occurring value (three occurrences).
Quartile 1	25% through a range of values
Median	The midpoint or 50% through a range of values. To calculate the median, the items of the distribution are arranged in order of magnitude starting with either the smallest or the largest, then: if the number of items is odd, the median is the value of the middle item. if the number of items is even, the median is the mean of the two middle items. 1, 2, 2, 3, 3, 4, 4, 4, 5, 6 Median = (3 + 4) ÷ 2 = 3.5
Quartile 3	75% through a range of values.
Sum	The sum is calculated by adding all the values of a distribution. Sum = 1 + 2 + 3 + 4 + 3 + 4 + 5 + 4 + 6 + 2 = 34
Minimum	The minimum is the smallest value of the distribution. Minimum = 1
Maximum	The maximum is the largest value of the distribution. Maximum = 6
Range	The range shows the spread of the distribution and is calculated by subtracting the smallest value (minimum) from the largest value (maximum). Range = 6 – 1 = 5
Standard Deviation	The standard deviation is a measure of dispersion of values in a distribution. It gives an indication of how much the values deviate from the mean. Thus, a distribution with a large range would have a larger standard deviation than one with a small range. The standard deviation is calculated as: where xi is each value in the distribution, is the mean of the values and n is the number of cases. For the sample in question: Standard Deviation = 1.428286
Variance	The variance is another measure of dispersion of values in a distribution and is used in the calculation of the standard deviation: Snap calculates the standard deviation and variance by assuming the data represents a sample rather than an entire population.
Standard Error of the Mean	The standard error of the mean is calculated by dividing the standard deviation by the square root of the number of items in the sample. It is defined as the standard deviation of the distribution of the sample mean and gives an indication of how far individual scores deviate from the mean score shown. The larger the sample, and/or the closer the individual scores are to the mean score, the smaller the standard error. Standard Error of the Mean = 1.428286 ÷ √10 = 0.451664
Skewness	A distribution that is not symmetrical but has more cases toward one end of the distribution than the other is called skewed. The measures of central tendency (mean, mode and median) can vary considerably. If the mean is larger than the mid point of the range (the median) and the most frequently occurring value (the mode), the sample is said to be positively skewed. If the mean is smaller than the mid point of the range (the median) and the most frequently occurring value (the mode), the sample is said to be negatively skewed. A small skewness value (close to 0) indicates that the data is evenly distributed about the mean. With this type of distribution it would be expected that the values for mean, mode and median be similar. The skewness of the example is 0.098843 indicating a small positive skewness.
Kurtosis	Kurtosis also gives an indication of the shape of a distribution in the form of the extent to which, for a given standard deviation, the data clusters around a central point. A positive value for kurtosis indicates a distribution that is more peaked than usual. A distribution of this type would typically have most of the values clustered around a central point. A negative value for kurtosis indicates a flatter or more widely dispersed distribution. The kurtosis for the example is -0.75202
Average Absolute Deviation	The average of the absolute deviations. It is a and tends to ignore distant outliers. It is a summary statistic of statistical dispersion and would normally only be displayed if specifically requested
Sample Standard Deviation	An estimate of the population standard deviation based on the sample.
Sample Variance	An estimate of the population variance based on the sample.

The post Descriptive Statistics tab appeared first on SnapSurveys.

This table shows the meaning of the options which appear when a given statistic is selected. These options specify how the statistic is calculated and displayed. The default options are set in the Analysis tailoring dialog.

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Area	Description
Title	Defines the title for table window and text report. This defaults to a summary of the analysis.
Insert	Insert an Image, Variable field, Survey field, Date/Time field, HTML field, Analysis field or Cell value field at the current cursor position.
Chart Axis titles	Specify the titles for the chart axes
Analysis	Defaults to the analysis definition as title
Break	Title for the x-axis (not for pies or doughnuts) Defaults to the break definition
Value	Title for the y-axis (not for pies or doughnuts)
Use Defaults	Set the chart axis titles back to the default values.
Text style area	Specifies the font typeface, size, colour and formatting used in notes.
Insert	Insert an Image, Variable field, Survey field, Date/Time field, HTML field, Analysis field or Cell value field at the current cursor position.the note
Notes panel	Enter text for more information about the current analysis. Text entered here can be viewed and edited in a text panel below the window displaying the result (visible by clicking in the display window toolbar). It will be included in exports and printed results.

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It can also be used to produce an analysis in which the proportion of respondents in your sample is adjusted to match more closely to the proportion in the target population.

For example, if you wished to weight your samples so that they were 50% male and 50% female, and also 20% in each of five age brackets, the algorithm would calculate the correct weighting that needed to be applied to each table entry (combining age and gender).

RIM weighting works best for single response variables where there is no missing data, and the counts or percentages are similar to the existing data responses.

It is not a good idea to use rim-weighting if:

• Your variables are related to each other (for example, income bracket and dwelling size).
• The values vary enormously (for example, you have 96 males and four females, and you are attempting to balance it to 50:50).
• You are applying a very large number of weights.

Creating a RIM Weight

1. In the Survey Overview window, open the survey.
2. Click Analysis Variables on the Snap XMP Desktop toolbar. This opens the Analysis Variables window which displays a list of the analysis variables.
3. Click New Analysis Variables Item on the Analysis Variables toolbar. This displays a menu of analysis variables to choose from.
4. Click New RIM Weight. This opens the RIM Weight window. Note there is an initial error in the status bar as there are no variables references yet.

5. In Name, enter a name which describes the RIM weight.
6. In Label, enter a description of the RIM weight.
7. In Target total, select the required option.
8. In Missing data, there are two choices: exclude partial cases or include partial cases. Excluding the partial cases provides the most accurate result as the partial cases have missing data in their responses. The default is exclude partial cases.
9. In Filter, enter a filter expression to create the RIM weight for a subset of the response data.
10. Click Add Variable to open the Select variable dialog.

11. Select a variable from the Name drop-down as the weighting variable.

12. Click OK. This displays default values for the RIM weight in the grid . The grid shows the ratio, expected count and percentage as well as the actual count and percentage.
13. Click in the Ratio column to change the ratio or target number depending on the Target total option selected.
14. Repeat for all the variables you wish to add.
15. If you wish to remove variables, select a variable in the grid then click Delete Variable to delete the variable.
16. Click Save to save the RIM weight.

Target Totals

There are three options for setting the Target total: Valid cases, From targets and Custom.

Valid cases

Valid cases bases the RIM weight on the valid data responses in the survey. The default ratio is 1 to give an equal distribution for all variable codes. The Ratio column can be changed with a proportion or target number for each variable code. For a target number to be used the total numbers in the Ratio column (per variable) must add up to the number of valid cases, otherwise a proportional ratio is used. The Expected column shows the target counts. The total number of valid cases is available next to the drop-down.

From targets

From targets sets a ratio or target number for each code within a variable. This is set in the Ratio column. The target number is available next to the drop down and is the count of the target number for each variable code in the Ratio column. The Expected column shows the target counts for each variable code.

Custom

Custom allows an overall target number to be entered. Selecting this option enables the field next to the drop-down where you can enter the target number. The default ratio is 1 which shows an equal distribution of the target number split across all the codes within each variable. The target number can be edited in the Ratio column. The Expected column shows the target counts for each variable code.

Note: The RIM weighting in Snap XMP Desktop works out whether the number entered in Ratio is a percentage, proportion or target number and does not require a percentage sign (%) to be entered.

Tailor the RIM Weight

You can customise the decimal places, maximum iterations and match threshold for the RIM weighting.

1. Click Tailor on the RIM Weight toolbar where you can edit the number of decimal places, maximum iterations and match threshold for the RIM weighting calculation.

2. Click OK to save.

Assess the RIM Weight

Assessing the RIM Weight gives statistics of how efficiently the RIM weight will meet the target.

1. Click Assess RIM Weight on the RIM Weight toolbar to assess the RIM weight.

2. A summary shows the build information. Click OK to close the summary.

Errors

If there is an error in the RIM weight the status bar will show an error message. Clicking Assess RIM Weight also shows an error message. For example, when not enough target values are set, the assessment shows an error message and the status also displays this message.

Build the RIM Weight

The status of the RIM Weight displays in the status bar at the bottom of the RIM Weight window. When the RIM Weight is created the status shows as Not built.

1. Click Build RIM Weight on the RIM Weight toolbar to build the RIM weight.
2. The Status changes to show Built. If there is an error in the RIM weight the status bar will show an error message and the RIM weight is not built.
3. Click Save to save the RIM weight.

Using the RIM Weight

The RIM weight can be included when creating analyses.

1. Click the required analysis icon on the Snap XMP Desktop toolbar. This opens the Analysis Definition window.
2. In Analysis and Break, enter appropriate break and analysis fields.
3. In Weight, enter the name of the RIM weight.

4. Click OK to build the rim-weighted analysis.

The post RIM weighting appeared first on SnapSurveys.

Chart styles

When creating an analysis chart, you need to select the type or style of the chart. Snap XMP provides a large number of pre-defined chart styles. These styles define the layout and colors used to display the chart. Some styles define only the color scheme which is useful when you need a specific set of colors, such as for your organisation’s branding. The pre-defined styles can be edited using the Chart Designer.

Style naming convention

The chart styles that are supplied with Snap XMP Desktop follow a naming convention that can be used to help set up the analysis definition.

Chart styles that contain a layout and colors start with the chart name, such as, Bar, Horizontal Bar, Line and Pie.

If the chart style name contains the word:

• Counts then select Counts in the Show Options section
• Percent then select one of the percent options in the Show Options section
• Transposed then select Transpose otherwise clear Transpose

For example, for the chart style Horizontal Bar Percent Transposed, select the one of the percent options, such as Analysis Percents, and select Transpose.

Chart styles that contain only a color scheme start with the word Color and are followed by a description of the color scheme. For example, Color – 5 point Green to Red Stacked.

Create a simple bar chart

When an analysis, including a chart, is created, two windows are shown to help define the analysis:

• Analysis Definition window used to define what response data is shown, the chart style and default text used in the chart.
• Analysis Display window used to define how the chart is displayed.

Usually, the first step is to decide which type of chart you are going to use, although the chart style can be changed at any time. In this example, the chart style selected is Bar Counts. This is a bar chart where the bars show the number of respondents that selected each response.

Instructions

1. In Snap XMP Desktop, open the survey.
2. Click Analysis Chart    on the Snap XMP Desktop toolbar. This opens the Analysis Definition window where you can create a new chart. This also opens the Analysis Display window which is blank for a new chart before the chart style is selected.
3. In the Analysis Definition window, click the Select button. This opens the Select Analysis Style dialog. A thumbnail image is shown for each predefined chart style, helping you choose the style you want. Click on the image to select the chart style. The style selected in the example is Bar Counts. Click OK to select the style.

4. The next step is to define the response data that is shown in the chart. A simple example of a bar chart is to show the responses to a single question. In Analysis, enter Q4, which is a question asking about the items ordered in a café.
5. Click Apply to show the chart display in the Analysis Display window.

6. The bar chart shows all the responses to the question Q4 with each bar showing the number of respondents who ordered each item. The legend shows which color corresponds to each question response. The chart title defaults to the Q4 label and the bars are arranged in the order each choice is asked in the question.

Some default settings for an analysis are set up in Analysis Tailoring and these defaults may vary in your organisation. This is opened by clicking Tailor | Analysis.

Resizing the chart and chart elements

Resize the chart by hovering over the move the cursor to any edge or corner of the window until a double-headed arrow appears. When this arrow appears, click and drag to make the window larger or smaller. The chart resizes to fill the chart plot space available. You can see how your chart will look in different display sizes.

Some chart elements can be resized by clicking on the chart element to select it. Hover the cursor over the black selection markers until a double-headed arrow appears. When this arrow appears, click and drag to make the element larger or smaller.

Drag and Drop

Some items in the chart can be moved using drag and drop. This includes the title, legend, chart plot, footnote and datapoint labels.

1. Click the item in the chart display. The area is shown with small black squares around the selected area.
2. Click in the selected area and drag to the new position. The chart display changes around the repositioned item.

Opening the chart designer

The Chart designer helps you customise the style of your chart. Open the Chart Designer by clicking the Edit Style button   in the toolbar of the Analysis Display window.

The Chart Designer shows the chart elements on the left-hand panel in the Chart Designer. Selecting a chart element displays tabs that allow you to change different aspects of that element. Some of the chart elements have sub-elements that can be accessed by clicking on the   symbol.

You may want to change an item but not know what it is called. In this case, double-click on the item in the chart. The Chart Designer opens with the item selected.

Saving or cancelling changes

1. As you make changes to the chart elements in the Chart Designer, click on Apply to see the effect they make. Once you have clicked Apply the Cancel button will not reset the changes.
2. To abandon your changes, as long as you have not clicked on Apply, click on Cancel.
3. To confirm your changes, click on OK. These updates are made to the Analysis Display window but are not permanently saved as part of the analysis chart.
4. To save the changes permanently go to the Analysis Display Window. If you wish to cancel all the changes made, click the Cancel icon on the toolbar. To save the changes, click the Save icon on the toolbar.

Transpose to show labels on axis not legend

In the Bar chart shown you can change the legend so that the labels are shown on the axis underneath.

• In the Analysis Definition window, select Transpose. This removes the legend, and the labels show underneath each bar.

Excluding No replies

Not all respondents complete every question when they submit a response to the survey. Often, in the analysis, the responses containing, no reply, are not relevant and you do not want to see them in the chart.

Note: The default setting to show or hide No reply responses is set in the analysis tailoring. The analysis tailoring dialog opens from the Tailor | Analysis menu. Your organisation may already exclude no reply responses.

To exclude the no replies:

1. In the Analysis Definition window, click the Base/Labels tab.
2. In the No reply drop down, select Exclude.

3. Click Apply. Check that there are no replies responses shown.
4. In the Analysis Display window, click Save to save the changes to the chart.

Ordering

You can order the bars in several ways

• Default. This is the order that the choices appear in a question.
• Analysis Label. This is the alphabetical order of the choice labels in a question.
• Analysis Base orders by the number or percent of responses in descending order.
• Column counts
• (Score) orders by any statistics on the table e.g. mean

1. If the Analysis Definition window is not open, click on the Properties icon on the Analysis Display window.
2. In the Analysis Definition window, select Analysis Base in the Order by list.

3. Selecting Reverse order reverses the order of the items.

In addition, customised ordering is available in the Chart Designer.

1. Double-click on a bar to open the Chart Designer.
2. Click on Series above the selected bar.
3. Click on the Order tab.
4. Click the Up and Down buttons to order the bars, as required.
5. Click Apply to update the chart display.

Changing the chart title

You can customise the chart title by

• changing the font, font size, font style and font color
• changing the text
• changing the text alignment
• changing the title orientation
• changing the location
• changing the background color and pattern
• changing the title frame style and shadow effect
• adding a background image or replacing the title text with a logo or image

Double-clicking on the legend opens the Chart Designer.

1. Click the Text tab and enter a new title for the chart. Click Apply to update the chart.

2. Click the Font tab and increase the font size and make the text bold. Click Apply to update the chart.

The chart title is now more noticeable with a clearer description.

Changing the chart legend

You can customise the legend by

• changing the location
• changing the background color and pattern
• adding a background image or replacing the title text with a logo or image
• changing the font, font size, font style and font color

Double-clicking on the legend opens the Chart Designer.

• To make the legend text easier to read, click the Font tab and increase the font size. Click Apply to update the chart.

Changing bar colours

The chart style chosen determines whether the bars in a series are different colors or all the same color.

1. When you click on the bar that needs changing, this selects all the bars in the series with the same color. This shows you which bars will be updated.
2. Double-click on the bar to open the Chart Designer at the series (bar color) settings.
3. Click the plus + to expand the bar settings and select Datapoint defaults.
4. In the Fill tab, select the Pattern, Fill color or Pattern color required.

5. Click Apply to update the chart display.

The post Creating a simple chart appeared first on SnapSurveys.

Step 1: Creating the filters to use in Snap XMP Online

The external filters and contexts are defined from the Analyses or Reports windows. Once defined, the filter or context can be applied to reports and analyses in Snap XMP Desktop and Snap XMP Online.

1. Click Analyses or Reports  on the Snap XMP Desktop toolbar.
2. Click on Define External Filter/Context  . This opens the Define External Filter/Context dialog.
3. Select the Filter tab to enter a filter or the Context tab to enter a context.
4. Click Add to add a new filter or context variable to the list. Select from the list of variables in the selected survey and click OK.

5. Use Move Up and Move Down to change the order of the list.
6. Click OK to save the filters.

Step 2: Checking the filter in Snap XMP Desktop

External filters or contexts allow you to filter the cases used in an analysis or report without changing the analysis or report definition. This is useful for testing the filters available in Snap XMP Online.

1. Click Analyses on the Snap XMP Desktop toolbar.
2. Double click on an analysis to display it in the Analysis Display dialog.
3. Click on Apply External Filter/Context  . This opens the Apply External Filter/Context dialog. If this is disabled, then select Allow additional filters on the Analysis Definition dialog.
4. In the Apply External Filter/Context dialog, select the Filter or Context tab.
5. Select the code(s) in the selected variable(s) in the list to apply a filter or context. In this example, the location Bristol UK is selected.

6. If you have applied a mask to the filter, the filter codes displayed will depend on the mask settings.
7. Click OK to apply the filters to the analysis. This will show the data responses filtered for the Bristol location.

Step 3: Using the filters in Snap XMP Online

Any analyses or reports that include external filters can use these to view a filtered subset of the response data. To apply a filter to an analyses, use the following instructions:

1. In Snap XMP Online, select the survey from Your work.
2. In the Summary tab, click the Analyze link. The reports and analyses are available here.

3. Select Tables and Charts in the side menu then select the analysis you want to view.

4. To apply a filter to the selected table or chart, select Filter & context in the side menu.
5. Click Add variable   to add a filter rule.
6. In the Select a variable list, select a variable to use as the filter then click Next.
7. Select the answers to use in the filter and click OK.

8. Click Apply changes to update the analysis of your response data for the selected filter.

Turning external filters on and off in Snap XMP Desktop

Settings in the analyses can determine whether other users can apply the external filters to an analysis in Snap XMP Online by using settings in Snap XMP Desktop. You can also control whether they can see an individual analysis.

1. In Snap XMP Desktop, open your survey and click Analyses to open the Analyses window.
2. Open the analysis that you want to control and click Properties   on the Analysis Display toolbar to display the analysis definition.
3. In the Definition tab, select Allow additional filters to enable filters for this analysis in Snap XMP Online. (Clear this to disable external filters in Snap XMP Online.)

4. Check that the Available field is clear or says Yes. (Enter No in this field to stop the analysis being available in Snap XMP Online. Enter a condition in this field to allow the analysis to be available in Snap XMP Online if the condition is true.)

You can set Available conditions on reports in the report definition.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Setting up filters for Snap XMP Online analysis appeared first on SnapSurveys.

• the count (number of respondents who chose that response)
• the percentage (number of respondents as a percentage of the total).

This tutorial explains how to create a bar chart that shows both the counts and the percentage values on a single chart:

Note: To show either counts or percents on a bar chart, use the preconfigured chart styles supplied with Snap XMP Desktop, such as, Bar Counts Labelled or Bar percent labelled.

Bar chart styles

Bar charts are different from tables because you can only display one value for each bar (the height of the bar on the scale).

You can represent counts and percentage values together in a table.

For a bar chart, the height of the bar must be either the counts or the percentage. If you want to display both the counts and the percentage value, you need to pass in the counts and use the Chart Designer to calculate the percentages from the counts.

The following examples use the Bar Counts style to display an item in the Analysis field.

If you enter a term in the Analysis field, the bar chart styles identify the separate items by colour and use a key to tell you which bar is associated with which question or question code.

If you enter a term in the Break field, the bar chart styles identify the separate item by labelling the X-axis below the bars. (This also happens if you enter the term in the Analysis field and check Transpose.)

You can enter analysis terms in both the Analysis and the Break fields (as for a cross-tabulation) and see the results displayed as a bar chart.

If you are only using a single term, you can choose whether to have your bars identified by a colour key or labelled.

If you want your chart to give the exact values for each bar, you can display them on the chart.

Bar chart of counts using the Bar Counts Labelled style. The term is in the Analysis field and the Transpose box is checked.

Bar chart percentages

When you display a percentage, you need to know what it is a percentage of. The base percents shown in the table are calculated as the percentage of all the respondents that gave that response.

If you are charting a single-response question, the total number of responses is probably the same as the number of respondents. If you are charting a multi-response question, there will be more responses than respondents.

The Chart Designer can’t work out how many respondents there are from the number of responses, so it can’t calculate the percentages automatically. You must tell it.

1: Creating a table of the analysis

1. Open the Crocodile Rock Cafe survey supplied with Snap XMP Desktop.
2. Click Analysis Table  on the Snap XMP Desktop toolbar to create a table.
3. In Analysis, enter Q2.
4. Select Counts and Base Percents.

5. Click Apply to display your table. You can use this to confirm that you have displayed the correct values on the chart.

2: Creating the bar chart

1. Click Analysis Chart  on the Snap XMP Desktop toolbar to create a chart.
2. Set the style to Bar Counts and set the analysis to Q2.

3. Click Apply to display your chart. This shows a basic bar chart with no figures displayed on the bars.

4. Click Edit Style on the chart toolbar. This opens the Chart Designer where you can change the appearance of the chart.
5. In the chart elements list, click on Series then click on the Daily datapoint. Click on the Datapoint Label defaults.
6. In the Appearance tab, select Base to display the counts and percent at the top of the bar. In the Datapoint Label section select Automatic, Value and Percent.

7. In the Value Format tab, select Number and 0 to display whole numbers.

8. In the Percent Format tab, select As Percentage and 0% to display percentages.

9. Repeat this for all the datapoints.
10. Click Apply to update the chart.

11. Compare the percentage figures on the chart to the figures on the table. You will see that they do not match. This is because the figures on the chart are calculated automatically using the size of the largest bar. To make the figures display correctly, enter the base figure in the Chart Designer to use to calculate the percentages.
12. In the Chart Designer click the Y Axis section. The scale is set automatically. Clear the Automatic check box then set the Maximum to the respondent base for the survey: 385 in this example.

13. Click Apply to update the chart. The percentage values displayed on the chart are now correct.

3: Changing the labels on the Y-axis

Sometimes the chart appears with an unnecessarily precise Y-axis labelling. You can choose to not display the Y-axis, or you can change the display of the labels, so they do not show decimal places.

Hide the Y-axis

You can stop displaying the y-axis by clearing the Show box.

Changing the number of divisions on the Y-axis

Change the number of grid divisions to a factor of 385 (e.g., 7), so that no decimal places are needed. This is done by setting the value in the Major box under Divisions.

Format the Y-axis labels

1. Select Axis Labels under Y Axis in the chart elements pane.
2. Select the Format tab.
3. Select the Number category and 0 as the Format Code.

4. Click OK.

Saving the style

You can save the chart style that you have just created by right clicking the chart and selecting Save Style from the context menu.

Further information

To find out more about how the Chart Designer works, see Using the Chart Designer.

There is a worksheet on creating your own chart styles: Use Chart styles to add branding to your charts.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Putting counts and percentages on a bar chart appeared first on SnapSurveys.

For example, here are five rating questions with a five point scale.

You could use the responses to these questions to help you judge whether you should invest in a larger parking lot or better food.

This tutorial explains some of the issues about using the data as it stands and describes how to set up a three-column table and a simple bar chart to display whether people are generally satisfied or not.

Background

When you are looking at the responses, you need to be careful that you know what is being counted, and that you compare like with like.

For example, how do you compare people’s satisfaction with parking and cleanliness if forty people answer your questionnaire:

• ten people answer the parking question, of whom eight select Good
• forty people answer the cleanliness question, of whom twenty select Good.

To make the comparison useful, you could:

• ignore the people who haven’t responded, and compare the percentage of people who responded who think it is good (in this case 80% think parking is good while 50% think cleanliness is good).
• count the people who hadn’t responded as neutral, and then compare the percentages (20% of all respondents think parking is good while 50% think cleanliness is good)

You need to then display your conclusions clearly.

Here are some of the ways you could display the rating data in tables.

Look at the responses in detail with a grid table

This tells you how many people and what percentage of people, selected which answer to which question. You can find more information about creating grid tables.

Compare the questions as a whole

• You could use confidence boxes to group the Poor and Very poor responses together as Negative, and the Good and Very good responses together as Positive and see which question scored highest on Negative or Positive responses. You can find more information about creating a satisfaction scale tables here.

• You could work out a value for all the responses for a question and see which question did best on average. One of the clearest ways of showing this is to give the responses a score ranging from negative to positive. You can find more information about creating a scoring system here.

• You can reduce the table to three columns using derived variables, to make it easy to see the general trends. This worksheet describes how to do this.

This tutorial uses the Crocodile Rock Cafe survey data supplied with Snap XMP and shows you how to create derived variables from the rating questions, so you can display the ratings in a three-column table, as Positive, OK, or Negative. The rating scale questions in the Crocodile Rock Cafe survey are questions Q6.a to Q6.e.

Step 1: Create derived variables for the three columns

You need to create a new variable with 3 codes that represent each five-point rating scale to a three-point rating scale: Positive, OK and Negative.

Create the new variable

1. Click Variables  on the Snap XMP Desktop toolbar to open the Variables window.
2. Click New Variable  on the Variables window toolbar to create a new variable.
3. Set the Name to ThreePointV6a to remind you that it is a three-point rating scale derived from Q6.a.
4. Set the Type to Derived. (The values in this variable are derived from the answers to the rating scale questions.)
5. Set the Label to Speed of service.
6. Set the Response to Single.

Create the code list

1. Click in the Label area for code 1 and type Negative. This is going to be the heading of the column in your analysis.
2. Click Tab to move to the Values column.
3. Enter an expression to select the Poor or Very poor responses to the rating question. In this example, these are the responses 1 (Very poor) and 2 (Poor). The expression that selects these responses to the appropriate question (Q6a in this example) is Q6a=(1,2). I.e., if the answer to Q6a is either 1 or 2, this variable will have an answer of Negative.

4. Click Tab to move to the Label column for code 2 and type OK.
5. Click Tab to move to the Values column and type Q6a=3 (i.e., the answer to Q6a is code 3, OK).
6. Click Tab to move to the Label column for code 3 and type Positive.
7. Click Tab to move to the Values column and type Q6a=(4,5)(i.e., the answer to Q6a is code 4 or 5, good or very good).

8. Click  on the Variables window toolbar to check the number of responses for the parts of your new variable. This displays the correct counts.
9. Click Save  to save your variable.
10. Highlight the variable that you have just created in the Variables window and click Clone Variable  to create a copy.
11. Edit the new cloned variable. Change the variable Name to ThreePointV6b, the Label to Cleanliness and change the question number used in the code values from Q6a to Q6b.
12. Click Save  to save your variable.
13. Repeat cloning and editing the variable for Q6c (Parking), Q6d (Quality of food) and Q6e (Choice of food).

Step 2: Create an analysis table from the derived variables

You can now create analyses based on the new derived variables.

1. To create a table, click Analysis Table  on the Snap XMP Desktop toolbar. This opens the Analysis Definition window for a table.
2. In Analysis, enter ThreePointV6a ~ ThreePointV6e as the analysis expression. (If the variable names are not in sequence, enter them individually, in the format V6a, V6b, V6c, V6d, V6e.)
3. Select the Analysis Percents box, so you can see what percentage of people chose each answer.

4. Click Apply to update the table.

5. Click Save  to save the analysis table.

Step 3: Create a stacked bar chart from the derived variables

Although the table displays all the information you need, it might be easier to see it in a chart. This step shows creating a stacked bar chart to display the same results.

1. Click Analyses  on the Snap XMP Desktop toolbar to open the Analyses window.
2. Click the analysis table that you have just created in the Analyses window and click Clone Analysis  to create a copy. This opens the Analysis Definition window.
3. In Type, select Chart.
4. Set the Style to Horizontal Stacked Bar Counts Transposed.
5. Select Counts and clear Analysis Percents.
6. Select Transpose.

7. Select the Notes/Title tab and type Satisfaction ratings as the title of your chart. Clear any values for the Analysis and Break chart axis titles.

8. Click Apply to display the chart. (You may need to drag the definition window away from the chart window.)

9. Click Save  to save the analysis chart.

The chart shows the satisfaction ratings as a single bar for each question. From the chart, it is easy to see that people are most dissatisfied with Service and most satisfied with Choice of food. The number of responses for each rating is given on that section of the bar.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Working with rating scale questions appeared first on SnapSurveys.

When you share the survey with an analyst or researcher in Snap XMP Online, you can also set the context for that user. The report or analysis available to the shared user is tailored according to the context.

The survey needs to include a single-response variable that can be used as the context. For example, this could be a location or a job function.

The reports or analyses are created using information that changes according to the context, that is filtered according to the context or both. In Snap XMP Online, you can share the survey with another user with any context or filter values set on the survey.

Step 1: Create context-sensitive reports and analyses in your survey

You need to put context-sensitive information in your reports and analyses. If you want to set up the report in your own version of Crocodile Rock Cafe survey, the instructions are given below.

This section describes briefly how to change the Crocodile Rock Cafe survey provided with Snap XMP to create the context sensitive report. It consists of the following stages

• 1: Set up a derived variable to compare all data to the current context data
• 2: Create a bar chart comparing the amount spent in the specified location with the amount spent elsewhere using the comparison variable
• 3: Create a comments list filtered on context
• 4: Create your report including the comparison bar chart and filtered comment list

Set up a derived variable to compare all data to the current context data

1. Click    to open the Variables window.
2. Open Q0 and change its Name to Location to make it more obvious what it is. This is the context variable.
3. Click   on the Variables window toolbar to add a new variable.
4. Specify the Variable details:
   • Name: CComparison
   • Label: Compare context to all
   • Type: Derived (the variable will derive its data from Location, the existing location question).
   • Response: Multiple
5. Double click in the first code label and click the Insert button. Select Variable Field from the menu.

6. Select Location as the Variable, and Context as the Aspect. This will give the selected location as the code label. Click OK to return to the variable definition.

7. Press Tab to move to the Values column. Enter Location=Location@context. The code used will be the code that equals the location specified by the context.
8. Press Tab to move to the next Label field and enter Other sites. Then enter NOT(Location=Location@context) as the Value. This code will be used when the response does not equal the location specified by context.
9. Press [Tab] to move to the next Label field and enter All sites. Then enter True as the Value.

10. Click  to save the variable.

Create a bar chart comparing one restaurant to the others using the comparison variable

1. Click Analysis Chart   to display the Analysis Definition dialog for a chart.
2. Select the chart style Bar Counts from the drop-down list.
3. Type CComparison (your derived variable) into the Analysis field.
4. Check the Transpose box.
5. Select Means & Significances from the Calculate list and enter Q5 (the amount spent) as the variable to use.

6. Select the Notes/Titles tab and click in the Title field.

7. Enter the title of your chart, using the Insert button to open the Variable field dialog and insert the Name aspect of Q5. Click OK and repeat to insert the Context aspect of Location in the title.

8. Clear the Chart Axis titles.
9. Select the Base/Labels tab.
10. Clear the Reports Include options for Description and Notes if set.
11. Click Apply to update the chart while keeping the analysis definition displayed.

12. There will be no data for the title and subject as no context has been set.
13. Click Save  to save your chart.

Add a comments list filtered on context

1. Click  to create a list. The picture below shows a list consisting of the variable (Q9) with comments plus the displayed location.
   Apply a filter of Q9 ok and Location=Location@context to filter the comments according to the current location. Q9 ok tests that an answer has been supplied to Q9 to strip out empty comments. Location=Location@context filters the comments according to the selected location.

2. Select the Base/Labels tab and clear the Reports Include options for Description and Notes if set.
3. Click OK to create the new list. It will have no content as no context has been set.
4. Click Save  to save your list.

Create your report including the comparison bar chart and filtered comment list

1. Click   on the Snap XMP Desktop toolbar to open the reports window.
2. Click   to create a new report and give it a label describing it.
3. Click   on the report dialog and select Information to add a piece of text to your report.
4. Enter the title for your report in the text pane. Leave the Title field blank.
5. Click OK to add the Information instruction to the report.
6. Click   on the report dialog and select Execute.
7. Click  on the Execute dialog and select the comparison chart (AN14) from the list.
8. Click OK to add the Execute instruction to the report.
9. Click  on the report dialog and select Information to add text after the comparison chart.
10. Enter some text describing the comments list. Use Insert to insert the Context Aspect of the Location variable to insert the label of the current location.
11. Type AN18 empty in the N/A field. This tests if there is currently any data in the comments list (analysis AN18). If there is no data, the report will not include the instruction.
12. Click OK to add the Information instruction to the report.
13. Click  and select Execute from the drop-down menu.
14. Select the comments list (AN18) from the list of analyses.
15. Click OK to add the Execute instruction to the report.

16. Click Save   to save your report.

Step 2: Sharing the survey with a context

Other Snap XMP Online users are given permission to share a survey through the Shares tab in Snap XMP Online.

1. Log in to Snap XMP Online to show Your work. If you are already using Snap XMP Online, click Home to return to Your work, where the Summary tab displays by default.
2. Select the survey to share, then select the Shares tab, which lists the users who have shared access to the survey.

4. Click the Add user button to enter the details of the user you want to share the survey with. The user must have a Snap XMP Online account.

5. In the Add user dialog enter the user’s email address that they use to access their Snap XMP Online account.
6. Next set the Permissions for the user. Further information on the permissions is available at Sharing overview.
7. Type the required context in the Context field (Location=1 in this example). Note that the context will not be checked here. It is only checked when it is used, and it is only used when the client logs in and looks at the analyses or reports.
8. Set Enabled to Yes for the user to have access to the shared survey or survey template.
9. Set Manage shares to No if you do not want the user to share the survey with other account holders or set to Yes if you want the user to be able to share the survey with other Snap XMP Online account users.
10. When you have entered the user details, click Save to add the user. The user has access to the shared item. The Shares list includes the new user.
11. Select the Show contexts/filters to view the context and filter columns.

12. The shared icon shows that the survey or survey template is shared.

Step 3: Testing the context

1. Log into Snap XMP Online using the shared user’s login details.
2. Select the survey with the filter value you have just set up.
3. In the Summary tab, click the Analyze link
4. Click Reports or Tables & charts in the side menu then select a report or analysis.

5. Confirm that the results show the context as you would expect. If you have made an error when applying the context, you will now see a message. Look at any other results to confirm that the context applies to all of them.
6. Click Log out.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Tailored reports and analyses in shared surveys appeared first on SnapSurveys.

This worksheet shows how to:

• categorise responses as positive, neutral or negative
• create a horizontal bar chart using the new categories
• exclude the neutral and negative categories, leaving only the positive category

Step 1: Categorise responses as positive, neutral or negative

In this example, participants rate aspects of service on a 5 point scale ranging from Very Good to Very Poor. Derived Variables are used to sort Very Good and Good responses into a new Positive category, OK responses into a new Neutral category, and Poor and Very Poor responses into a new Negative category.

1. On the Snap XMP Desktop toolbar, click Variables  to open the Variables Window.
2. Click New Variable   to add a new variable.
3. Specify the Variable details as follows:
   • Name: VQ6a
   • Label: Quality of product (this will be displayed on the chart)
   • Type: Derived (the variable will derive its data from other existing variables)
   • Response: Single (each participant will only fall into one of the new codes)

8. Create the derived values for the Negative, Neutral and Positive ratings. For example, the Negative category is given by points 1 and 2 on the scale (Very Poor and Poor) and the Value is Q6a=(1,2).
9. Click Save  to save the variable and close the Variable Details window.
10. On the Variables Window toolbar, click Clone Variable to copy the derived variable you have just created. Change Q6a to Q6b throughout, and update the label.
11. Click Save  .
12. Repeat for the other questions in the grid.

Step 2: Create a horizontal bar chart using the new categories

Now you can use the new derived variables to create a stacked bar chart of positive, neutral, and negative results.

1. On the Snap XMP Desktop toolbar, click Analysis Chart to create a new chart.
2. In the Style list, select Horizontal Stacked Bar Percent Transposed.
3. In the Analysis field, enter the derived variables.
4. Select the options Analysis Percents and Transpose.

5. Click Apply. Your chart shows the three derived categories.

6. Click Save  to save the chart.

Step 3: Show only the positive ratings

To create a chart showing only the positive responses, the negative and neutral values need to be excluded. You could also exclude the positive and neutral values to create a negative chart. This is achieved using the Chart Designer.

1. In the Analysis Chart window, double-click the Negative bar to open the Chart Designer. It will open with Series selected on the left-hand side.
2. Select Negative in the Series list and select Exclude Series in the options.

3. Select Neutral in the Series list and select Exclude Series in the options.
4. Click OK to close the Chart Designer. Your chart now shows only the positive ratings.

The post Create a chart of positive responses to rating scale questions appeared first on SnapSurveys.

This tutorial shows how to apply a new colour scheme to an existing chart, how to change the colour for individual chart categories, and how to save your custom chart style to use again.

Step 1: Apply a predefined colour scheme

In this example, a new colour scheme is applied to a horizontal bar chart of responses to a question grid.

1. On the Snap XMP Desktop toolbar, click Analysis Chart to create a new chart.
2. In the Style list, select Horizontal Stacked Bar Percent Transposed.
3. In the Analysis field, enter the grid questions.
4. Select the options Analysis Percents and Transpose.

5. Click Apply. The chart will look like this:

6. In Style, select a new Colour style from the list, for example Color – 5 Point Red to Green Labelled Stacked

7. Click OK. Your chart will now look like this:

This example uses a 5 point scale; however there are colour schemes specifically designed for horizontal bar charts with 3 or 7 points, on both a positive to negative, and a negative to positive scale. There are black and white styles designed specifically for print, and styles with colours replicating those found in various versions of Excel. Colour styles can be applied to any chart, not just horizontal bar charts.

Step 2: Customise individual category colours

Category colours for any type of chart can be easily changed. In this example, the same colour style, Color – 5 Point Red to Green Labelled Stacked, is applied to a 4 point scale, which also includes a ‘Don’t Know’ option. In this context, the ‘traffic light’ colour labelling would be misleading. Changing the Don’t Know category colour from green to grey can show that this category is not included in the ratings. The same steps can be followed to change category colours in any style of chart.

1. Double-click the area of the chart you would like to change, in this case the Don’t Know category. This opens the Chart Designer.

2. The Chart Designer contains a number of options to change the appearance and content of your chart. Select Datapoint Defaults then select a new colour from the Fill section:

3. Click OK to save the changes. The chart shows the new category colour.

Step 3: Save a style to use again

There are two options. You can either save the colour scheme, enabling you to apply the same colours to another chart in the future, or you can save the chart style including layout, background and colour scheme.

1. Right-click anywhere in the chart area and select Save Style. This opens the Save Style As dialog.
2. Choose a folder location and enter a file name for the chart style.
3. In Style parts, select the parts of the custom style to include.
   • To save just the colour scheme, select Series and clear Layout and Background
   • To save the complete chart style, select Layout, Background and Series

4. Click Save to finish. You can now browse to select your saved chart style or colour scheme in the Analysis Definition.

The post Change category colours in charts appeared first on SnapSurveys.

This tutorial shows how to:

• Build a horizontal bar chart of rating scale responses
• Create an ‘Overall’ Group Variable to combine responses from all questions
• Add the ‘Overall’ category to the Horizontal bar chart

This example uses the Crocodile Rock Cafe survey supplied with Snap XMP Desktop. Alternatively, use any survey which includes one or more question grids.

Step 1: Build a horizontal bar chart of rating scale responses

Start by building a horizontal bar chart to summarise the responses to a typical question grid.

1. On the Snap XMP Desktop toolbar, click Analysis Chart to create a new chart.
2. In the Style list, select Horizontal Stacked Bar Percent Transposed.
3. In the Analysis field, enter the grid questions.
4. Select the options Analysis Percents and Transpose.

5. Click Apply. The chart will look like this:

6. Click Save  to save the chart. Note the name of the chart as you will need to retrieve it in a later step.

Step 2: Create an ‘Overall’ Group Variable to combine responses from all questions

1. On the Snap XMP Desktop toolbar, click Analysis Variables  to open the Analysis Variables window.
2. Click  and select New Group Variable.

3. In Name, enter GV1 as the Group Variable name.
4. In Label, enter Overall as the description.
5. In Source, enter the list of questions to be grouped into the source field.

6. Click Save  to save the group variable.

Step 3: Add the ‘Overall’ category to the Horizontal bar chart

Now you can add the group variable to your horizontal bar chart.

1. On the Snap XMP Desktop toolbar, click Analyses  to retrieve the saved bar chart.
2. On the Analysis chart toolbar, click Properties   to open the Analysis Definition details.
3. In Analysis, type GV1 followed by a comma then the grid questions:

4. Click OK. Your chart now includes an extra category, Overall, showing the group variable.

What else could I do with group variables?

Create a chart to summarise multiple grids

If your survey contains more than one question grid, try repeating the steps above to create a group variable for each one (GV1, GV2, GV3 etc). Then, build a Horizontal bar chart including the group variables only.

Create an overall satisfaction index

Create a new group variable including questions contained in multiple question grids, for example type ‘Q8a to Q8d, Q9a to Q9d, Q10a to Q10d’ into the source field (if the grid questions are sequential, you could also type Q8a to Q10d). This will produce an overall score for all grid questions– perfect for a top level summary in your report.

The post Summarise rating scale with Group Variables appeared first on SnapSurveys.

Crosstabs are also known as contingency tables (or pivot tables). This tutorial shows you how to produce and order crosstabs then convert them to charts.

A crosstab is a table where the analysis field defines the rows and the break field gives the columns. This can be reversed by selecting the Transpose option.

The data in each cell shows the figure for the participants who fit into both the column and the row. The type and order of data displayed is given by the Show options.

You can order the rows by:

• the order the codes appear in the questionnaire, used for rating scales. This is the default order.
• alphabetical order by label
• popularity (sorted by quantity of responses)
• statistics you have included

Creating a simple crosstab

This example shows how to break down responses by gender. It uses data in the Crocodile Rock Cafe survey supplied with Snap XMP Desktop.

1. On the Snap XMP Desktop toolbar, click Analysis Table  to display the Analysis Definition dialog.
2. Type Q11 into the Analysis field (the age variable)
3. Press OK to build a simple table showing the ages of the participants.

4. Click  on the Analysis table toolbar to display the Analysis Definition dialog.
5. Type Q12 into the Break field (the gender variable)
6. Press Apply. The table now shows the values of Q11 as the row labels and the values of Q12 as the column labels across the top of the table.

7. In the Analysis Definition dialog, select Break Percents, which shows each answer as a percentage of the column totals for the break variable (Q12).

8. Click Apply to update the table. The percentages are shown below the counts.

9. Click Save  to save the table.

Creating an ordered crosstab

This example shows how to set out data so it’s easy to see which choices were most popular. The analysis shows which items were ordered (with a breakdown by age), and sorts them according to popularity.

1. On the Snap XMP Desktop toolbar, click Analysis Table  to display the Analysis Definition dialog.
2. Type Q4 into the Analysis field (the food ordered)
3. Type Q11 into the Break field (the age variable)
4. Press OK. You will see a crosstab with the values of Q4 as the row labels on the left-hand side of the table and the values of Q11 as the column labels across the top of the table. The default row order is the order that the items were listed in the questionnaire. To make it easy to see which choice was the most popular, you can order the rows by the analysis value.

5. Click  on the table toolbar to open the Analysis Definition dialog again.

6. In the Order by list, select Analysis Base then click Apply. Your table will update, with the rows sorted according to the number of responses. You can see that the French fries row has moved to the top.

7. Click Save to save the table.

You can easily display your data in chart form. Open the Analysis Definition dialog and change the Type to Chart. The example below shows it with a style of Horizontal Bar Counts. You can see that the items ordered have been sorted by the Analysis base, with the most popular at the top.

Quickly building multiple crosstabs in a report

You can create a report of separate crosstabs using the same break (for example, break all the responses down by age or gender or both) with a single instruction in a report. The example below breaks down the questions by age.

1. Click  to display the Reports window.
2. Click  to create a new report. Set the labels to a description of your report and select Skip Empty to omit any empty analyses.

3. Click  to create a new instruction and select Table in the Instruction List to display a Report instruction dialog.
4. Complete the fields in the Definition tab:
   1. Analysis: Q2,Q4,Q6a TO Q6e
   2. Break: Q11
   3. Set the Order by dropdown to Default. Ordering is not appropriate for rating scales.
   4. The instruction will generate one crosstab for each question that you put in the analysis field. Literal, date and quantity questions have been excluded.
5. Click OK to save the instruction.
6. Click  on the Report window toolbar to open the Report execution dialog. Click Yes to save the report.
7. Click Printer to choose a printer to print the report or choose to save the document to a PDF file or similar.
8. Click OK to run the report and create the set of crosstabs.

Producing crosstabs filtered by demographic

You can also break down the data in your tables by using filters. This example shows how to produce a set of age tables for males and a set of age tables for females.

1. Create a new report or duplicate the one you created in the previous step.
2. Change the label of your new report to Analysis of all questions by age filtered by gender.
3. Click  to create a new instruction and select Table in the Instruction List to display a Report instruction dialog.
4. Enter Q12 (gender) in the filter field.

5. Click OK to save the instruction.
6. Click  to open the Report execution dialog. Click Yes to save the report.
7. Click OK to run the report and create the set of crosstabs.
8. You will have two versions of all the question tables, one for each gender in Q12.

You can convert all the tables to charts by changing the instruction type and style.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Analysing data with crosstabs appeared first on SnapSurveys.

These features include:

• selecting how many items are displayed
• specify the size variation between different words
• choose the colours used
• decide whether to categorise literals as separate words or as complete responses
• create clouds from open responses or from the labels of coded responses
• automatically categorise open responses using a stop list to stop common words such as “and” or “the” being included in the word cloud

This worksheet shows how to create a word cloud automatically from participants’ comments. It also describes how to change the number of items displayed in your word cloud and tells you how to remove words from a word cloud.

This worksheet uses the Crocodile Rock Cafe survey supplied with Snap XMP Desktop.

Step 1: Creating a word cloud from a literal question

This step describes how to display comments as a word cloud by using auto coding.

1. In Survey Overview, open the Crocodile Rock Cafe survey.
2. Click Analysis Cloud   on the Snap XMP Desktop toolbar. This opens the Analysis Definition window to create a cloud.
3. In Analysis, enter Q9. This question asks the participant to give any comments about their visit.
4. Click Apply to display the word cloud.

Step 2: Changing the number of entries in your word cloud

If you wish to have more items in your word cloud, change the code limit.

1. In the Analysis Definition, select the Auto Coding tab. This tab shows how your data will be coded. These settings may be different, depending on the tailoring defaults set up. Unless you have patterns applied to literals, you would normally use Words for literals and Values for everything else. If you use Values for literals, the complete response is used rather than individual words.

2. Change the value in Limit codes to 20.
3. Click Apply to see the change in your word cloud.

Step 3: Tailoring your word cloud to hide unwanted words

There may be some words collected in the responses that are not helpful or needed in your cloud. You can adjust which words are displayed for an individual cloud by creating a new variable that you can edit then analyse.

1. Click Analysis Variables   on the Snap XMP Desktop toolbar. This opens the Analysis Variables window showing the list of analysis variables. The dialog below shows two automatically created analysis variables, AV.Q9 and AV.Q9.a. In the name, AV shows that this is an auto category variable and the next part contains the variable name it comes from. The last part makes the variable unique. The dialog shows the variables created for the two word clouds for Q9.

2. Select the variable AV.Q9.a and click Clone   on the toolbar to make a copy of it. This is the variable used for the twenty code word cloud. (You can find out which variable was created automatically for an analysis by using the Sources and Dependents button   on the Analysis Variables window toolbar.)
3. The new variable should open automatically. Click Choose codes . This displays a new Include column.

4. The first twenty items in the list will be used in a word cloud. Clear the Include box to remove “bit” from the word cloud.
5. If you remove “bit” from the list of visible items, the item at number 21 will replace it. If you do not want this item to be used clear the Include box next to it.
6. Clear Include for “1” and “24”. Any excluded words are added to the Stop words.
7. Click Stop words to show the excluded words. Removing a word from the list will add it to the included list again. Click OK.

8. Click Save  to save your new variable.

Step 4: Displaying your tailored word cloud

You now need to edit your analysis to use your new variable.

1. Open the Analysis definition for your word cloud.
2. In Analysis, enter the  new auto category variable created in Step 3
3. Click Apply to see the result. The words “1”, “24” and “bit” have been replaced.

Displaying a multiple response variable as a word cloud

This tutorial has shown you how to display comments as a word cloud. You can also display other types of questions as word clouds. For example, it’s easy to see which type of food is most popular at the cafe.

If you would like to find out more about word clouds, see the topic Overview of word clouds. This covers creating clouds from any type of variable. To find out about changing the word cloud’s appearance, see the topic Word cloud appearance.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Using word clouds to display your results appeared first on SnapSurveys.

To do this, you need to find out what the minimum and maximum value responses are, and use this information to divide the range into equal bands. You can display the minimum and maximum values in a table of descriptive statistics. Once you have decided on the bands, you must sort the responses into those ranges, so they can be used in charts and tables. This is done by creating a derived variable.

Step 1: Decide on the band ranges

In order to split the quantity into bands of equal size, create a table to find the maximum and minimum values and the range. This tutorial uses the Crocodile Rock Cafe survey supplied with Snap XMP Desktop.

1. Click Analysis table  on the Snap XMP Desktop toolbar. This opens the Analysis Definition dialog to create a table.
2. In Analysis, enter Q5, the question that asks for the “Amount spent”.
3. In Break, select Statistics table.

4. Click the Descriptive Statistics tab to select the information that will be shown in the table.

5. Use the < button to remove all entries from the Used column apart from the Minimum, Maximum and Range statistics. (You can select multiple entries to move at the same time.)
6. Click OK to create your table.

The table shows the top and bottom limits of the bands. For a coarse grain banding, you could split the data into bands of 1 – 21.33 (Low spend), 21.34 – 41.66 (Medium spend) and 41.67 – 62 (High spend). Deciding which band ranges to use can depend on your survey, such as the status of the survey or the intended audience of the analyses and reports that use the banded data. If the survey is live then you may wish to take into account that any new responses may contain data outside the current ranges and extend the bands. If the analysis is used in a report you may want to use whole numbers to make the chart easier to read, for example using 1 to 20 rather than 1-21.33.

Step 2: Create the derived variable

1. Click Variables  on the Snap XMP Desktop toolbar. This opens the Variables window.
2. Click New Variable  on the Variables toolbar to create a new variable.
3. Enter a Name and Label to describe the variable
4. Set the Type to Derived.
5. Set the Response to Single.
6. Click Toggle Definitions to display the variable definition.
7. In Initial Value, enter Q5. This means that the data will be derived from the response in Q5.

8. Click in the empty Code Label field. Enter the bands as shown, using the Tab key to move to the next field and to enter a new code.

9. Click Save  to save the new variable.

Step 3: Analyse the quantity data using the bands

1. Click Analysis Chart  on the Snap XMP Desktop toolbar. This opens the Analysis Definition dialog to create a chart.
2. In Style, select Bar Percent Labelled. Set the analysis value to the name of your new variable, AmountSpent, and select the Transpose box.

3. Click OK to display the bar chart. You can see that nearly all the responses are in the lowest band. For further detail, you will need to redefine your variable or create a new variable with narrower bands.

4. Open the new variable and update the label and values as shown. Click Clone Answer Code  to duplicate the code, if required.

5. Click Save  to save your changes.
6. If your chart is still open it will now have Out of Date on the title bar. Click Rebuild  on the toolbar to update the chart.

Further analysis of the low end values

The previous graph shows that most people spent less than twenty pounds in the shop, with the other bands being roughly equal.

You could further analyse the low end spending in a separate graph by cloning the derived variable, AmountSpent and creating bands ranging from 1 to 20. The bar chart analysis is shown.

You can see from this that the most popular lower end spend is between one and five pounds.

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com

The post Banding quantity variables for analysis appeared first on SnapSurveys.

Snap XMP Desktop provides filters that are available in a number of areas.

• viewing responses in the Data Entry and Questionnaire windows
• adding a filter to an analysis in the Analysis Definition window
• adding a filter to a report in the Report window
• adding a filter to certain types of report instruction
• creating external filters for use in reports, analyses and shared users in Snap Online

In the same way, you can filter the response data.

You can filter data by:

• the answers to one or more of the questions
• participants who did or did not reply to certain questions, for example, selecting all participants who wrote a comment
• value of a question response or on system data provided by Snap XMP Desktop

The way you filter may be different depending on the type of question.

• filter multi-response values on which codes or how many codes a participant has selected
• filter quantity questions by whether the value is larger, smaller, or equal to another value
• filter by the date or time, or on parts of them such as month, day or hour
• filter text questions on whether the text has been entered, or whether it contains particular words or parts of words
• filter cases by case number

You can also combine filters together using logic operators, such as AND, or use a derived variable to create complex filters.

Filtering responses in a chart showing satisfaction ratings

This example shows how to create a chart showing the satisfaction rating before and after a promotion by applying a filter to analyse the data over different months. The sample survey Crocodile Rock Cafe is used for this example.

1. Click on Analysis Chart  on the Snap XMP Desktop toolbar. This opens the Analysis Definition window for a chart.
2. In Style, select Horizontal Stacked Bar Counts Transposed. This creates a bar chart showing the proportions of people who selected each response.
3. In Analysis, enter Q6.a TO Q6.e. This grid question shows the different levels of satisfaction with different aspects of the service.
4. Select Transpose to display the responses by question rather than by value.
5. In Filter, enter Q1a MONTH=(7 OR 8). This filter uses Q1a which is a question asking the date that the participant visited. MONTH is a date function which returns the month of a date.

6. Click the Notes/Titles tab and enter a title such as Satisfaction ratings for July and August.

7. Click OK to create the chart. It will look something like this:

8. Click Save  on the chart toolbar to save your chart.
9. The filter can be changed to different months and the chart can be updated with the new results for comparisons.
10. Entering a filter for the months September and October, Q1a MONTH=(9 OR 10), produces a new set of result where you can see that the levels of satisfaction have improved.

Creating a list of comments

This example creates a list of comments, together with the demographics of the participant making the comment. In this situation, only the responses that contain comments are needed. These can be identified by using OK in the filter. Note that you can use capital or lower case letters for question numbers in the Analysis and Break fields.

1. Click Analysis List   on the Snap XMP Desktop toolbar. This opens the Analysis Definition window for a list.
2. In Analysis, enter the questions or variables that will be shown in the list. In this example, enter case, Q9, Q12, Q11 in the Analysis field. The list will show the case number, comment, gender and age of the participant.
3. In Filter, enter Q9 OK. This filters the responses where a comment has been entered. (You could also use NOT Q9 NR to specify cases which do not have a “No reply” to Q9.)

4. Click OK. The list is displayed. You can see from the case numbers that responses without a comment have been omitted.

Further information can be found at Filtering the data responses and Filter expressions.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Using filters to find a subset of response data appeared first on SnapSurveys.

• Auto category variables
  • These categorise open response variables into codes. You can then analyse the resulting codes and display them as clouds, frequency tables etc.
• Group variables
  • Group variables allow you to treat a group of similar variables as a single variable: for example grid questions. The variables must share a code list.
• Factor analyses
  • Factor analyses summarises the responses to several variables as a composite variable. It allows you to look at underlying patterns in data.
• Cluster analyses
  • Cluster analyses analyse factor analyses, and create variables based on how the responses are clustered together.

Button	Menu Option	Description
	Edit | New	Create new auto category variable, group variable, factor analysis or cluster analysis
	Edit | Clone	Clone variable or analysis and show details.
	Edit | Delete	Confirm then delete selected item
	Edit | Modify	Show details of selected item.
	View | Sources and Dependents	Show the Sources/Dependencies dialog.
	Tailor | Variables	Set the default options for the variables.
	File | Print Report	Print the analysis variables report
	Edit | Copy	Copy selection information on the Clipboard.
	Edit | Paste	Create a group or analysis from information on the Clipboard.

The post Introduction to the analysis variables window appeared first on SnapSurveys.

All of the source data should be at least ordinal by nature, and ideally of interval or ratio type. That is, if a single response variable is used, it should be one where the codes show an ordered increase or decrease in response. Thus, an ordered age variable would be acceptable but a gender or geographical region variable would not.

Cluster analysis is an exploratory technique designed to identify patterns in data that may not be immediately obvious. Its object is to sort cases into groups, known as clusters, so that the members of a particular cluster are similar to each other but members of different clusters are dissimilar.

It is not a classification technique as it makes no assumptions about the nature of the groups or clusters prior to the analysis being carried out. The groups are constructed based on the data cases provided with each case being assigned to the cluster that it is most like, and each cluster being defined by the characteristics of its members.

The algorithm for the Cluster Analysis used in Snap XMP Desktop, known as k-means clustering, is as follows.

1. The user specifies how many distinct clusters are required and which variables are to be used in the analysis.
2. Each cluster is then assigned a value for each variable. Typically, this will be done arbitrarily taking into account the range of values for each variable. As an example, if only two variables are specified, they could be plotted on a two-dimensional scatter graph with the cluster centres represented by points on the graph.
3. Having assigned the initial cluster centres, Snap then considers each case in turn and calculates which cluster centre it is closest to. The case is then assigned to that particular cluster.
4. Once all cases have been considered, and allocated membership of one of the clusters, the cluster centres are recalculated as the mean value of all the members of that cluster.
5. A consequence of recalculating the cluster centres is that some of the cases may now be in the wrong cluster. That is, the centre of the cluster of which it is a member may have moved further away from them, while the centre of a nearby cluster may have moved closer.
6. Snap repeats the previous step, assigning each case to the cluster whose centre is closest, until convergence is reached.

Initially there is likely to be considerable movement between clusters, however convergence is quickly achieved in most cases. Typically, successive iterations will generally see fewer cases move from one cluster to another, meaning that the cluster centres do not change so much and hence there will be less movement in the next iteration.

Standardised values

Snap uses Standardised Data Values to perform the Cluster Analysis and allows the user to see the standardised values in the results if desired. The Standardised Data Values are calculated by applying a transformation to the initial data set, creating a set of values with a mean of 0 and a standard deviation of 1.

This is an essential process as the source variables may have very different orders of magnitude. For example, consider two quantity variables, Age and Salary, included in the source data. It is probable that the values for Salary will of a different order of magnitude (tens of thousands) from that of Age (tens). If the Cluster Analysis used the actual data values, differences in salary would be given considerably more importance that differences in age. The resulting clusters would be determined predominantly by differences in salary. Standardising the data sets creates a “level playing field” so that all source variables are compared on equal terms.

When the results are reported, it is often unhelpful to use the standardised values as these have no units and are therefore have limited use for interpretation. Snap XMP Desktop by default reports the actual data values.

Snap measures the distance between cases and cluster centres using the Euclidean method. That is, the straight line distance between the two points on a graph.

Standardised Data Values are calculated by subtracting the mean value of the entire data set and dividing the result by the standard deviation.

Running means

The specification for Cluster Analysis includes an option to use Running Means. By default, this is not selected. If the Running Means option is switched on, then the calculation of the cluster centres takes place every time a data case is allocated to a new cluster, rather than waiting until all cases have been evaluated.

Creating a cluster analysis

1. Open the survey.
2. Click the button on the toolbar to open the Analysis variables overview window. The overview window shows all the Group and Auto-category variables and Factor and Cluster analyses currently set up in the current survey.
3. Click the button and select New Cluster Analysis… from the menu. The Cluster Analysis Details window appears.

4. Specify a name and descriptive label as required.
5. Specify the list of variables from which clusters are to be extrapolated in the Source field. For example, specify Q5, Q2 to have clusters evaluated for those two variables. Use range definitions if the variables fall into a consecutive range, for example Q6a to Q6e would include all variables between and including Q6a and Q6e.

Once the source variables have been specified, the clusters will be determined and results may be reviewed or further qualified by clicking on the appropriate tab.

Initial centres

If clusters are not clearly defined, the initial centres selected may have an effect on the clusters produced. As an extreme example, if you have data that naturally looks like two clusters and you ask for three clusters, the third cluster does not naturally have a suitable centre, so its final position will be affected by its starting position.

Look at the centres and the scatter plot to see if you think the clusters are well spaced for the data – the F-values help with this too.

The Initial centre options are:

Zero (default) – start at zero, so the clusters move away from zero sequentially.

First cases – the n cluster centres are set to the first n cases. This provides some real positions as initial centres. It is rather open to influence from what people have answered.

Evenly spread – for each source variable, find the minimum and maximum values. The first cluster centre starts at the minimum values and the last cluster starts at the maximum values. The other clusters are evenly spaced in a ‘line’ between these extremes. This will favour results ranging from generally good to generally poor

Examining a cluster analysis

The Cluster Analysis dialog allows you to examine the defined clusters in different views by selecting different tabs.

1. Create a new Cluster Analysis based on the Crocodile Rock Cafe survey with
   • Name: CL1
   • Label: Cluster Analysis CL1
   • Clusters: 2
   • Source: Q5, Q2, Q8

2. Click on the Results tab to show details of the cluster centres for each variable and the count of respondents in each cluster.

3. By default, actual values will be shown (as indicated by the Show setting Actual Values. Under this setting, values for quantity variables will reflect the actual answers; values for categorical (single response) variables will reflect the code values.
4. Change the Show setting to Code Labels to show code labels for categorical variables. Results for quantity variables will still reflect the actual values of those variables.

5. Change the Show setting to Standardised to show standardised results for all variables.

6. Use whichever of the Show settings is appropriate for determining a description for each of the clusters. A descriptive label can be allocated to each cluster in either the Results tab or the Setup (previous) tab.
7. Click the Centre Distances tab to see the table of cluster centre distances. The distances are shown between clusters either as Actual Values (with the Show setting as either Actual values or Code Labels) or Standardised Values.

8. To see how cluster centres move during the iterative calculation process, click on the Iteration drop-down. The default setting, Final, shows the result at the end of the last iteration.
9. For an alternative view of the movement of cluster centres, click the Iteration History tab to show the change in each centre during the iterative calculation process.

10. Click on the Anova tab to show the results of the Analysis of Variance for the current cluster solution. The Mean Square values show the average (mean) squared distance between each of the cluster centres (Between Clusters) and between each case and the centre of the cluster to which it belongs (Within Clusters). The F-value is a statistical measure of how distinct the cluster groups are; a high F-value will indicate highly distinct sub-groups.

11. The F-Values tab shows a summary of the F-values for several different cluster solutions, with the current solution highlighted. Generally speaking, high F-values indicate that the members of each cluster group are homogeneous, and that the cluster groups are highly distinct from one another.

12. The Scatter Plot tab shows a plot of data case locations and cluster centres. Each cluster centre is automatically allocated a unique colour. The points representing each case are coloured to indicate the corresponding cluster they have been allocated to. The plot is of one variable against another. If there are more than two variables in the source, drop-down boxes enable you to specify which two should be plotted.

Although F-Values can be used as an indicator of how many cluster groups should be specified, it is not advisable to rely exclusively on this measure. The solution with the highest F-Value will not necessarily be the ideal solution, and users should rely on their own knowledge of the customer base.

The post Introduction to cluster analysis appeared first on SnapSurveys.

Typically, a survey might contain a series of questions asking respondents to express an opinion on different aspects of the product or service being evaluated. There may be dozens of questions that all require a response using, for example, a 7-point rating scale. Spotting trends in such a long list of questions can be difficult, so Factor Analysis is used to reduce the list to one of a more manageable length.

In Snap XMP Desktop, the Factor Analysis technique looks at correlations between each pair of questions and combines variables that have a high correlation with each other. These groups of variables are then combined in a particular way to form the factors. As the resultant factors take into account responses to several different source variables, the original list of variables can be reduced to a more manageable number, with each factor as a form of derived variable.

Typically each factor produced by the analysis will be heavily based on a subset of variables that are in some way similar. Since the resulting factors are stored within Snap and can be used as variables in subsequent analysis, knowing the source variables that have influenced the factor most heavily helps when giving the factors meaningful names.

Since the purpose of Factor Analysis is to reduce the number of variables to a more manageable level, it is likely that only a small number of the factors will be retained. The factors are listed in decreasing order of importance, allowing you to choose how many to retain for further analysis. The first factor will be the one that explains the highest amount of the total variance within the data (for the variables used in the source). The second factor will be the one that explains the highest amount of the remaining variance, and so on. The number of factors to be retained will depend on how many source variables are used, and the data for those source variables. This decision is, to some degree, arbitrary. There are theories that provide guidance on how many factors to take, such as ignoring factors with an Eigenvalue below a certain threshold, or taking sufficient factors to have a cumulative variability proportion of greater than a prescribed level.

An important application of Factor Analysis is as a precursor to Cluster Analysis.

Creating a factor analysis

1. Open the survey.
2. Click the button on the toolbar to open the Analysis variables window. The overview window shows all the Group variables, Auto-category variables, Factor analyses and Cluster analyses set up in the current survey.

3. Click the button and select New Factor Analysis from the menu. This opens the Factor Analysis Details window.

4. Provide a suitable name and descriptive label of the factor analysis.
5. In the Source field specify the list of variables for which factors are to be derived. Use range definitions if the variables fall into a consecutive range, for example Q6a to Q6e includes all variables between and including Q6a and Q6e. Specify Q6a to Q6e in the Source field. Separate variables with commas if they do not fall into a consecutive range, e.g. specifying Q6a, Q6b, Q6d.
6. Once the source variables have been specified, click in the table below and the factors will be calculated. Snap will derive the same number of factors as there are input (source) variables.

By default, Snap XMP Desktop uses the Jacobi algorithm for calculating factors. Selecting the Varimax option is an extra step which can make it easier to interpret the factors produced.

Understanding the factor analysis table

The results table shows for each factor:

Arranging factor loadings tables for easy interpretation

Transposing the table

To interpret the factor loadings, it is often easier to transpose the table. Select Transpose to display each factor as a column.

Re-ordering the table

The variable order is set by how the analysis was specified. You can sort the order by importance within each factor. This helps you see which variables have the greatest effect on a factor.

Click in the grey box containing a number at the top of the column or the end of the row containing the factor data. A triangle appears representing the sort order, and the variables re-order by the factor loading for that factor.

Column	Row
		Greatest first
		Smallest first
		(blank) Ordered by analysis specification

Reducing the number of factors

Factor Analysis is a data reduction technique, its purpose is to reduce the initial number of variables to a more manageable number by creating “composite” variables. By default, it produces the same number of factors as there are variables. In a long list of source variables it is likely that several factors will appear to be influenced by the same set of variables. The apparent duplication will usually occur in factors with small eigenvalues. These factors can usually be discarded as being relatively insignificant.

To discard values, you use the Cutoff settings.

Weighting in factor analysis

You can apply a weight to factor analysis as you can to other statistics.

For example, if the input variables are rating scale questions with 1 as Very Good and 5 as Very Poor, it is worth specifying a weight to the data so that very good scores have a high value and very poor scores have a low value in the resulting factors.

Applying a weight in the Scale box means:

• the weight will be applied to all multi-choice and grid questions before the factor analysis is performed
• the weight will NOT be applied to quantity questions

If all your source variables are rating scale questions, the weight will be applied to them all, and this will have no effect on the Eigenvalues or Factor Loadings that are produced.

If you have a mixture of variable types, the weight will only be applied to multi-choice and grid questions, and factor analysis results will differ from the same analysis without the Scale applied.

In the box labelled Scale, enter the name of a suitable weight, for example, one which weights codes 1 to 5 from -2 to +2.

The post Introduction to factor analysis appeared first on SnapSurveys.

Snap Interchange Format (SnIF) is used to transfer analyses, including tables, charts, word clouds, lists and maps between surveys. For example, if a number of surveys require the same set of analysis definitions, these could be created in one survey, exported in a SnIF format file and then imported into another survey.

In the following example the data is exported via the clipboard. For larger exports, it is recommended that you export to a file.

1. Click Analyses   on the Snap XMP Desktop toolbar. This opens the Analyses window.
2. Select the menu option File | Export to display the Analysis Export dialog.
3. Specify the Format as SNAP Xml Format (SnIF Xml).
4. Set the Destination as Clipboard.
5. By default, this will export all analyses in the survey. If you only need a selection, type the names of the ones required separated by commas, in Content.

6. Click OK to export the selected analyses.
7. Open the survey that you want to add the analyses to.
8. Click Analyses   on the Snap XMP Desktop toolbar. This opens the Analyses window.
9. Select the menu option File | Import to display the Analysis Import dialog.

10. Set the Source to Clipboard.
11. Click OK to import the analysis definitions. They will be added to the survey with all the original details.
12. If there are already analyses with the same name in the survey, an error message appears. Click Done then each analysis is displayed so that the name can be changed and the item saved.

Exporting an analysis to use in an external application

Analyses can be exported from Snap XMP Desktop by

• Using the clipboard
• By exporting to a file

You can then import them into a report; either as a picture in a word-processing package, or into a slide show generated in a presentation package. Tables and lists can also be exported in text format into a spreadsheet package, for you to carry out further calculations.

Using the Clipboard to paste an analysis as a picture

1. Click Analyses   on the Snap XMP Desktop toolbar. This opens the Analyses window.
2. Open the analysis in the Analysis Display window.
3. Click Copy   to place a copy of the analysis on the clipboard.
4. Switch to the word-processing or presentation package and choose the menu option Edit | Paste Special. Different packages may use different terminology.
5. On the Paste Special options select the picture (Enhanced metafile) option and click OK to insert the picture in the document.

Using the Clipboard to paste a table or list in text format

1. Click Analyses   on the Snap XMP Desktop toolbar. This opens the Analyses window.
2. Open the table or list analysis in the Analysis Display window.
3. Click Copy   to place a copy of the analysis on the clipboard.
4. Switch to a spreadsheet package and choose the menu option Edit | Paste. The table or list is inserted in text format, enabling further analysis to be carried out.

Exporting an analysis as a file

1. In the Analysis Display window showing the built analysis, such as a chart, select File | Export to display the Analysis Export dialog.

2. Specify the Format for the exported analysis. This will depend on the requirements of the package that the analysis is destined for. Comma separated and Tab separated are only suitable for table and list export.
   • Comma separated (CSV) each case comprises a series of fields, each separated by a comma. Fields corresponding to literal, date and time variables are surrounded by quote characters.
   • Tab separated (TSV) comprises a series of fields each separated by a tab.
   • Windows Bitmap (BMP) gives a pixel-based graphic.
   • Enhanced Metafile (EMF) gives a vector-based graphic.
   • Web format (HTML) gives an HTML export
   • Excel (CSV) each case comprises a series of fields, each separated by a comma for use in a spreadsheet.
3. The Export option is only available when CSV and TSV formats are selected.
4. Leave the Encoding as Automatic unless you know that the software you are importing to requires a different format (If there are problems, try UTF-8 or ANSI format.)
5. Specify the Destination by typing in a path and file name or click on Browse.
6. Click OK to export the analysis. This can then be inserted or opened in the appropriate package.

The post Exporting analyses appeared first on SnapSurveys.

The following example shows exporting weights from a survey to a file. The weights can also be exported via the clipboard.

1. Click   to open the Weights window.
2. Select File | Export to display the Weight Export dialog.

3. Specify the Format as SNAP Interchange Format (SnIF).
4. Set the Destination as Clipboard.
5. Specify the names of the weights you wish to export in the Content field. (e.g. Score). Leave the field blank to export all weights.
6. Click OK to export the selected weight.
7. A confirmation message is displayed when the export is complete. Click Done.
8. Open the survey to which you wish to add the weight or select File | New Survey.
9. Click   to open the Weights window and select File | Import to display the Weight Import dialog.
10. Set the Source to File, entering the file name from the export, and click OK to import the weight(s).

11. A confirmation message will be displayed when the import has completed successfully and the new weight(s) will be added to the survey.

The post Moving weights between surveys appeared first on SnapSurveys.

Defining external filters and contexts

The external filters and contexts are defined from the Analyses or Reports windows. Once defined, the filter or context can be applied to reports and analyses in Snap XMP Desktop and Snap XMP Online.

1. Click Analyses or Reports  on the Snap XMP Desktop toolbar.
2. Click on Define External Filter/Context  . This opens the Define External Filter/Context dialog.
3. Select the Filter tab to enter a filter or the Context tab to enter a context.
4. Click Add to add a new filter or context variable to the list. Select from the list of variables in the selected survey and click OK.
5. Use Move Up and Move Down to change the order of the list.
6. Select the variable that you wish to apply a mask to and enter the name of the mask variable in the Mask field (this must be in the format of variable@context).

Applying external filters and contexts

External filters or contexts allow you to filter the cases used in an analysis or report without changing the analysis or report definition. This is useful for testing the filters available in Snap XMP Online.

Apply a filter or context for an analysis by:

1. Click Analyses on the Snap XMP Desktop toolbar.
2. Open the analysis in the Analysis Display dialog.
3. Click on Apply External Filter/Context  . This opens the Apply External Filter/Context dialog. If this is disabled then select Allow additional filters on the Analysis Definition dialog.

Apply a filter or context for a report by:

1. Click Reports  on the Snap XMP Desktop toolbar.
2. Select the Report and click Execute/Check Report on the Reports toolbar.
3. Click the Filter/Context button. This opens the Apply External Filter/Context dialog.

In the Apply External Filter/Context dialog:

1. Select the Filter or Context tab.
2. Select the code(s) in the selected variable(s) in the list to apply a filter or context.
3. If you have applied a mask to the filter, the filter codes displayed will depend on the mask settings.

4. Click OK to apply the filters and contexts to the analysis or report.

The post Using external filters and contexts appeared first on SnapSurveys.

You can create cross-tabs and charts from category variables where one variable’s codes provide the columns and another variable’s codes provide the rows. It is possible to combine category variables in your analysis.

There are three ways of combining category variables in a cross-tab.

• Combining variables (WITH) where all the codes are treated as if they belonged to a single variable, with one column per code
• Combining all possible codes (PER) where every combination of codes available between the variables is given a column
• Combining variables with identical codes logically so you can see if either or both codes have been selected. (This is not dealt with in this worksheet)
  • The codes can be logically ANDed together so you get a count if both codes have been selected
  • The codes can be logically ORed together so you get a count if either code has been selected

Step 1: Creating a cross-tab showing missing responses

This shows you how to create four simple cross-tabs showing people who like cheese and chocolate by age and gender and then shows how to combine them into a single table. It also discusses missing answers. If you add percent scores to your cross-tabs, you need to be clear if you are displaying a percentage of all those who took the survey or all those who answered the question. You can include or exclude respondents who have not answered a question.

The survey example had 100 respondents. None of the questions has been answered by all of them.

1. Click to open the Analysis Tables dialog.
2. Enter Q1 in the Analysis field and Q3 in the Break field to give a table of people who like cheese broken by gender.
3. Check Break Percents, which shows each answer as a percentage of the column totals for the break variable (Q3).

4. Click OK to create the table then click on the table toolbar to save the table.
5. Create three more tables, Q2 broken by Q3; Q1 broken by Q4; Q2 broken by Q4.

Note that the Base used in the tables is different. This is because the analysis base consists of people who have answered both questions. This is because the analysis excludes no reply responses.

6. Click on the table toolbar for AN3 to open the Analysis Definition dialog for that table (liking cheese by age).
7. Click on the Base/Labels tab. Change the No Reply value of Exclude to Show for the Analysis and Break values and click Apply.

A new row and a new column are added to your analysis, showing the no replies, and the percentages are re-calculated as percentages of the new base. (You can change Exclude to Hide to re-calculate the percentages without including the row/column for the missing responses.)

Step 2: Showing several variables in the same cross-tab

1. Create a new table. Put Q1 WITH Q2 into the Analysis field. This puts the response counts to the cheese and chocolate questions in the table.
2. Put Q3 PER Q4 in the Break field. This puts every possible combination of the age and gender questions in the table as a separate column.
3. Check Base Percents. This uses the same base to calculate the percentages across the table.

4. Press OK to build the cross-tab. The rows show the responses to both the cheese and the chocolate question. The percentages of the base are calculated separately for each row. The columns show each possible combination of the age and gender questions. The percentages are calculated across all the columns.

5. Click to save the table.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Analysing several variables in a single table appeared first on SnapSurveys.

You can use the date and time information directly in analyses, or use a further derived variable to break it down into ranges defined by time intervals, for example, short journey, medium journey, and long journey. By using these two methods, you can present the same data in quantitative and qualitative form, allowing for different types of analyses.

Snap XMP Desktop provides functions to calculate the differences between dates and times. The differences between dates are given in days, and the differences in times are given in seconds.

This tutorial shows how to create time and date questions that are used to calculate the duration of a journey.

Step 1: Creating the time and date questions

1. Click Survey Overview on the Snap XMP Desktop toolbar to open the Survey Overview window.
2. Open the questionnaire in the Questionnaire window.
3. Click New Question to create the start date question.
   • Set the style to Open Series First.
   • Enter the question text “Please enter the date and time that you left home”.
   • Enter the grid label text “Date of leaving”.
   • Set the response type to Date.

4. Press Tab to create a new row in the question.
   • Enter the grid label text “Time of leaving”.
   • Set the response type to Time.
5. Repeat steps 3 and 4 to create the arrival date and time questions, changing the text as appropriate.

6. Click Save  .

Step 2: Creating derived variables for the dates and times

The date and time variables you have created use the question numbers as the name by default. The dates and times will be used in a formula and it is clearer to create derived variables using a name that explains the purpose of each date and time. This step describes how to create derived variables for the dates and times entered in the questionnaire.

1. Click Variables  on the Snap XMP Desktop toolbar. This opens the Variables window.
2. Click New Variable  to create a new variable. This opens the Variable Details window.
   • In Name, enter the name of the variable, LeaveDate.
   • In Label, enter the description, Date of leaving.
   • In the Code list, enter the question number, in this case Q1a, in the values column of the row OK.

3. Click Save  to save the variable.
4. Repeat steps 2 and 3 for
   • Q1b the Time of leaving with name LeaveTime and values Q1b
   • Q2a the Date of arrival with name ArriveDate and values Q2a
   • Q2b the Time of arrival with name ArriveTime and values Q2b

Note: You can change the question name but this is displayed in place of the question number. This could look confusing unless the question numbers are hidden.

Step 3: Creating the derived variable to calculate the difference

The next step creates a derived variable to calculate the difference between the time that the participant left home and the time they arrived at their destination.

1. Click Variables  on the Snap XMP Desktop toolbar. This opens the Variables window.
2. Click New Variable  to create a new variable. This opens the Variable Details window.
   • In Name enter the name of the variable journeyTime
   • In Label, enter Difference between leaving and arrival time in hours
   • In Type, select Derived
   • In Response, select Quantity.

3. In Code list, enter the formula in Values in the row OK.
   • The first part of the formula is working out the days spent on the journey and converting it to hours by multiplying it by 24.
     (ArriveDate-LeaveDate)*24
   • The second part of the formula is the difference between the two times. In Snap XMP Desktop, subtracting one time from another always produces a positive value. If the second time is before the first, it is assumed that one must be before midnight and one must be after midnight, and works out a time across midnight. This means that 10am – 9pm gives 13 hours rather than -11 hours. The time differences are in seconds. To convert the time difference to hours, you must divide it by 3600
     (ArriveTime-LeaveTime)/360
   • The last part of the formula adjusts for when the time of arriving is less than time of leaving, for example when a journey begins at 9pm on one day and finishes at 10 am on the next day. This gives the logical expression (ArriveTime < LeaveTime). The num function is used to convert the true or false answer to a numeric value, with 1 for true and 0 for false. Then multiply this result by 24, gives the right number of hours to take away, 0 if ArriveTime isn’t smaller than LeaveTime, and 24 if it is.(24 *num(ArriveTime < LeaveTime))
   • So the whole formula reads: ( ArriveDate – LeaveDate)*24 + (ArriveTime – LeaveTime)/3600 – 24*num(ArriveTime < LeaveTime)
4. Click Save  to save the variable.

Step 4: Creating bands for time duration ranges used in analysis

1. Click New Variable  to create a new variable. This opens the Variable Details window.
   • In Name enter the name of the variable journeyType
   • In Label, enter Banded journey lengths
   • In Type, select Derived
   • In Response, select Single.
2. In the Code list enter the values:
   • For code 1, enter the label Short journey, and the value as journeyTime<=2. All cases where the time taken is less than or equal to two hours are stored as Quick trips.
   • For code 2, enter the label Medium journey, and the value as journeyTime <=6 as the value. All cases where the time taken is 2 to 6 hours are stored as Medium trips.
   • For code 3, enter the label Long journey, and the value as journeyTime >6 as its values. All cases where the time taken is greater than six hours are stored as Long trips

You can now perform analyses using either the journeyType or the journeyTime variable. For example:

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com

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Gap analysis shows the difference between how important attributes or services are to your respondents and how satisfied they are with those attributes or services. It is a useful way of comparing the results from your satisfaction and importance questions and can be used as a tool for interpretation.

By comparing importance and satisfaction scores in your chart you can use gap analysis to identify priorities for improvement. Gap analysis results indicate that if the satisfaction measure is less than the importance measure the business may not be meeting expectations.

Interpreting the gap analysis chart

In the example chart below a 5-point scale question was used in the Course Evaluation sample survey for both importance and satisfaction. The ratings are from 1 to 5, where 1= totally dissatisfied to 5= totally satisfied and 1= not at all important to 5 = extremely important.

The gap analysis chart

The chart plots how important various aspects of service are to customers, compared with how satisfied customers actually are with that service. The bars indicate the gap which is the mean score for the satisfaction rating subtracted from the mean score for the importance rating.

Interpretation

The interpretation of the results is as follows:

If the mean score of a service is positive (above zero) then respondents rated the service very important, but they are not satisfied with the service. In this instance, action is required.

If the mean score of a service is negative (below zero) then respondents rated this service relatively unimportant, but are very satisfied with the service. In this instance no action is required.

The closer the gap is to zero the better balance there is between importance and satisfaction.

Below is an interpretation of the findings in the example chart above.

HelpDesk Service – has a small positive gap therefore, respondents rated Helpdesk as a relatively important feature compared to their low satisfaction rating. This could be seen as an area for improvement.

User Guides – has a small negative gap therefore customers have rated this with higher satisfaction than importance. More time could therefore be spent improving other products.

E-Newsletter – has a large negative gap therefore respondents have given this service high satisfaction scores when answering this question, but they do not think this is an important feature. The company needs to concentrate on improving other services and products and leave the E-newsletter as low priority.

On time delivery – has a large positive gap therefore respondents think this service is very important, but their satisfaction of this service is low therefore it is a high priority for improvement.

Setting up the gap analysis

The following instructions show how to create a gap analysis chart. This gap analysis can be found in the Course Evaluation survey provided with Snap XMP Desktop. The Gap Analysis chart style is also provided with Snap XMP Desktop

To set up the gap analysis chart you will need the following:

• create a number of derived variables
• tailor the chart
• create a chart style

Open the Course Evaluation survey and familiarise yourself with the importance (Q5a to Q5d) and satisfaction (Q6a to Q6d) questions.

If you open the Variables screen, you will notice that the derived variables to calculate the scores have already been created for you in V1.1, V1.2, V1.3, and V1.4. You can open these, by double clicking on them, to see how they have been set up. However, if you wish to create the derived variables to follow this worksheet we will name them V2.1, V2.2, V2.3 and V2.4 in the following steps.

Step 1: Create the derived variables

Create the derived variables. The derived variables will subtract satisfaction scores from importance scores for each attribute or service rated, for example Course content satisfaction scores will be subtracted from Course content importance scores. Each rated attribute will have to be set up as an individual derived variable.

1. Open the survey and click to display the Variables window.
2. Click to add a new variable.
3. Setup the new Variable with the following details:
   • Name: V2.1
   • Label: Course content (The label is a full description of the variable that will appear in the Data window and on all analysis reports.)
   • Type: Derived (The variable will derive its data from other existing variables.)
   • Response: Quantity
4. Click inside the Values area, in the same row as Valid. Type: Q6a – Q5a (Q6a is the satisfaction question and Q5a is the importance question.)
5. Click to save the derived variable. You will see the saved derived variable (V2.1) in the variable window.
6. Repeat the steps 2 to 5 above to create 3 more derived variables with the following details:
   • Name: V2.2
   • Label: Instructor
   • Type: Derived
   • Response: Quantity
   • Value: Q6b – Q5b
   • Name: V2.3
   • Label: User guides
   • Type: Derived
   • Response: Quantity
   • Value: Q6c – Q5c
   • Name: V2.4
   • Label: Value for money
   • Type: Derived
   • Response: Quantity
   • Value: Q6d – Q5d

Step 2: Create the gap analysis chart

The next stage is to create a chart to display the results of the derived variables.

1. Click Analysis Chart  to display the Analysis Definition dialog for a chart.
2. Select the chart style Gap Analysis from the drop down list.
3. Type the names of the derived variables into the Analysis field, for example V2.1, V2.2, V2.3, V2.4.
4. Type stats in the Break field (or select Statistics table from the Break drop-down list).
5. Check the Transpose box.

6. Click the Descriptive Statistics tab to select which statistics will be used.
7. Select all of the items in the Used column on the right-hand side.
8. Click on the left arrow button.
9. Select Mean from the list in the Available column and click on the right arrow button.

10. Click Apply to see the chart.
11. To change the title shown on the chart, click the Notes/Title tab of the Analysis Definition dialog and type in a title in the Title box.
12. Click Apply to see the chart.
13. To add the text at the base of the chart (Needs Attention and Needs No Attention is this example), click into the Notes area of the Notes/Titles tab and type the text you require and click Apply.

Note: You may need to format the text in the Notes area so that it lines up appropriately under each side of the chart. Adding extra spaces will add a gap between the text.

14. Click OK to close the Analysis Definition dialog.
15. Click to save the chart.
16. To save this chart style right click anywhere on the chart and select Save Style.
17. Browse to the Styles folder within the Snap folder and give the new style a name (e.g. Gap Analysis New.csf).

The new style is available to select in the Style dropdown list in the Analysis Definition dialog.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Gap Analysis: Compare results from satisfaction and importance questions appeared first on SnapSurveys.

Scores allow you to assign a value to each question code, and then calculate analyses using the score instead of the code. This is generally used for scoring satisfaction surveys, so that positive ratings are given positive values and negative ratings are given negative values. You can then summarise the whole satisfaction by calculating the mean of all the cases, showing whether participants are generally satisfied or not, the higher the mean is, the higher the satisfaction score.

When the satisfaction question includes a “Don’t know”, “OK”, “Prefer not to say” or similar neutral answer then these can be treated in different ways in the analysis. A neutral response can be included in the analysis and assigned a value of zero or it can be discarded and not included in the analysis. This produces different calculations for the mean values.

For example, if you have five participants to a survey, who have given the following satisfaction values (from a range of -2 to +2).

Participant 1: -1

Participant 2: +1

Participant 3: +2

Participant 4: +2

Participant 5: Don’t know

To calculate the mean, you sum the values and divide by the number of cases.

• Calculating the mean scoring “Don’t know” as 0 gives the mean as (-1 + 1 + 2 + 2 + 0) / 5 = 4/5 or 0.8
• Calculating the mean discarding the “Don’t Know” response gives the mean as (-1 + 1 + 2 + 2)/4 = 4/4 or 1.

It is important to decide how to treat neutral values when judging the satisfaction of your participants. By using a score you can choose to score them as a neutral value, or discard them from the calculation.

This tutorial shows how to create a score to analyse the satisfaction values in the Crocodile Rock Cafe survey that is supplied with Snap XMP Desktop. It shows both the effect of scoring neutral responses as zero, and the effect of discarding the neutral responses.

Step 1: Creating the score weight

The Crocodile Rock Cafe survey that is supplied with Snap XMP Desktop contains a rating question that asks participants to rate how satisfied they are with aspects of the service.

The questions to be scored are Q6a to Q6e.

1. Click Survey Overview on the Snap XMP Desktop toolbar to open the Survey Overview window then click Offline Surveys.
2. Open the Crocodile Rock Cafe survey and move to Q6 How do you rate the following?

3. Click Variables  on the Snap XMP Desktop toolbar. This opens the Variables window.
4. Select Q6a then click Properties . This opens the Variable Details window where you can view the Code list showing the label assigned to each code.

5. Click Weights on the Snap XMP Desktop toolbar. This opens the Weights window.
6. Click New Weight  to create a new weight. A weight is a way of matching values to codes for analysis.
7. Enter the following values for the new weight:
   • Name: scoreSat
   • Label: Balanced score -2 to +2
8. In Code List, enter the values for each code. NR is used when the response is OK or code 3 in the example. This discards any responses that have a value of NR and these are not included in the analysis..

9. Click Save  to save the weight.

Step 2: Using the score in an analysis

1. Click Analysis Table  on the Snap XMP Desktop toolbar. This opens the Analysis Definition window to create a new table.
2. In Analysis, enter the value Q6a ~Q6e (this includes all questions from Q6a to Q6e).

3. In Calculate, check that Counts and Percents is selected.
4. In Name, enter Satisfaction in order to identify it easily.
5. Select the Summary Statistics tab. In Score, enter the name of your score, scoreSat. This score is used to create a summary of the analysis, scoring the codes with the specified values.

6. Click on Mean on the left side in the Available column and move it to the Used column to display the mean.
7. Click OK to display the table.
8. There is a column displaying the mean scored values.

The top line labelled Base shows the total number of all participants for all the questions. The mean satisfaction of everyone with all the services is 0.10.The other rows show the average satisfaction for each service. You can see that people are generally positive about the Cleanliness and the Choice of food and unhappy about the Parking and Speed of service.

Step 3: Showing the effect of including and excluding neutral responses

You can examine the effects of including the neutral responses as zeroes by changing the score.

1. Click Weights to open the Weights window.
2. Double-click the weight scoreSat to open the Weight Details window.
3. Edit the neutral score in the list, changing it from NR to 0.

4. Select the Analysis display window for the table. The Rebuild button  is now enabled, as the score has changed since the analysis was performed.
5. Click Rebuild  to recalculate the analysis and show the updated mean values.

6. Click to save the changes.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Using scores to analyse satisfaction questions appeared first on SnapSurveys.

Snap XMP provides a large number of pre-defined chart styles that can be used as supplied or as the basis for a new style. The pre-defined styles can be edited using the Chart Designer or new styles can be created using the Chart Wizard.

The chart style is made up of three components:

• Layout which defines the type of chart and how it is displayed
• Background which defines the color or images used behind the chart
• Series colors which defines the colors used on the chart

This tutorial shows how to load a pre-defined chart style, use the Chart Designer to edit the chart style and then save the new style. When you save the style you can choose which components are saved in the chart style. Often the chart style is saved with the background and series colors only, so that the style applies these to a chart without changing the layout.

Step 1: Loading an existing chart style

A chart style must be selected when creating a new analysis chart. This can be edited later by loading another existing style or using the Chart Designer. The examples in this worksheet are based on the Crocodile Rock Cafe survey provided with Snap XMP Desktop.

1. In Snap XMP Desktop, click Survey Overview to view the list of surveys.
2. Click Online Surveys or Offline Surveys and open the survey in the Questionnaire window.
3. Click Analysis Chart  on the Snap XMP Desktop toolbar. This opens the Analysis Definition window where you can create a new chart.
4. Click the Select button. This opens the Select Analysis Style dialog. A thumbnail image is shown for each pre-defined chart style, helping you choose the style you want. Click on the image to select the chart style. The style selected in the tutorial is Pie Percent Labelled Outside. Click OK to select the style.

5. This example creates a simple chart based on a Single Choice question. The Crocodile Rock Cafe contains a question Q11 that records the participant’s age group. Enter Q11 in the Analysis field. When creating a color style file for your charts, you should work from the most complex chart that you use, in order to ensure that you have enough colors. This question has six choices which each require a different color or color/pattern combination.

6. Click OK. This creates the analysis and displays the chart.

7. To load another style, right-click the chart and select Load Style from the context menu. This opens the Select Analysis Style dialog again.
8. The selection shows the chart style in use. Click on another image to select the new chart style then click OK.

Step 2: Setting a new chart background

The Chart Designer lets you customize each aspect of the chart. This step shows how to replace the background with a picture. The background can also be set to another color.

1. Double-click the background of the chart to open the Chart Designer.
2. Select Chart in the left hand column, if it is not already selected.
3. Select the Picture tab and click the Browse button to choose your picture. Navigate to the folder containing the background image then select the image and click Open.
4. In Picture Size, select the best fit for your image. Stretch to Fit will stretch the image to the background of the chart.

5. Click Apply. The Chart Designer dialog remains open and the new background appears on your chart.

Step 3: Setting the series colors

1. If the Chart designer dialog is not open, double-click the series item that you wish to change, such as a bar or pie slice. This example starts with the Under 18 series. Select Series | Under 18 | Datapoint defaults | Datapoint1 in the left hand pane.
2. Select the Fill tab and change the Fill Color to the required color and click Apply to update the chart.

3. Select Series | 18 – 24 | Datapoint | Datapoint1 in the left hand pane.
4. Select two colors to mix in the Fill color and Pattern color. In this example, we used blue and black respectively. In Pattern, select a textured pattern. The pattern selection displays the colors you have already chosen.

5. Repeat the process for each data point in the series. Click Apply if you wish to make further changes, otherwise click OK to close the dialog.

Step 4: Saving the new chart style

1. Right click your modified chart and select Save Style from the context menu. This opens the Save Style dialog.
2. Enter a name for the new chart style.
3. Clear the Layout checkbox but leave the Background and Series checkboxes selected. This saves the chart colors and background only.
4. Browse to the location that you wish to save the chart style.
5. Click Save to save the chart style.

If you leave the Layout checkbox selected the style is saved with all the chart information and you will overwrite the chart type when you load the style.

Step 5: Testing the new chart style

1. Click Analysis Chart from the Snap XMP Desktop toolbar.
2. In the Analysis Definition dialog, change the style to bar percent, type Q2 in the Analysis box and click OK. This displays a new chart showing a four series element bar chart.

3. Right click the chart and select Load Style from the context menu.
4. Select the style created in Step 2.
5. Clear the Layout box and click OK. The chart will be updated with the new colors and background.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Use Chart styles to add branding to your charts appeared first on SnapSurveys.

You may need to merge surveys in these situations:

• Surveys run on an annual basis, to analyse trends through two or more years. (The questionnaire might change slightly from year to year).
• Surveys on one subject but with different focal points (e.g. surveys of users and non-users of a service).
• Surveys conducted for different locations (e.g. a survey of similar tourist attractions which varies between sites).
• Surveys conducted using two different interviewing methods (e.g. kiosk and web).

Step 1: Create a master survey to contain the data for both surveys

It is preferable to make a separate master survey to combine your surveys so that both the original surveys remain intact. The master survey will need to contain all the variables and codes in both surveys so that the analysis can be performed correctly.

1. In the Survey Overview window, select the survey with the highest number of variables.
2. Select Clone to create a clone of this survey.
3. In the Clone Survey dialog, select whether the new survey is Online or Offline then select Also clone the raw data to include the existing data. Click OK.
4. In the New Survey dialog, enter a name for the new survey that indicates it is the combined survey.
5. Click OK to open the new survey. Add in any variables or additional codes that are in the other survey but are not in this survey.

Step 2: Setup a database link to import the data

1. Select Database links from the Snap toolbar. This opens the Database Links dialog.

2. Click New to open the Database Linkage Wizard which guides you through the process of creating a new database link.
3. In the Linkage Type section, select Import from Snap Survey and then click Next
4. A list of all the surveys in the default directory is shown. Select the survey you want to import from and then click Next. (If the survey you require is not shown use the Browse button to find the appropriate directory.)
5. You are asked how you wish to merge the survey. Select Append all cases. This adds all the imported cases as new cases at the end of the survey. Click Next.

Step 3: Mapping the variables

The next window is where you will match the variables from the survey you are importing the data from to the survey you cloned as the master.

The variables in the survey you are importing from are displayed in the left column and the current survey variables are displayed in the right column.

1. The wizard attempts to map any variables from both surveys if the variables are identically named.

2. Click each of the variables in the right-hand column. If the variable is a coded question the codes will be displayed in a section below the mapped variables. Check that the codes on the left match the codes on the right.

3. If there are any blank codes in the right hand column, see the code label where Ice cream should be in the image above, click into the blank area and select the correct code label.

4. Click Next to see a summary of the mapped variables. You can also give the Database Link an appropriate name in this window.

5. Click Finish to return to the Database Links dialog where you will see the link you have just created.

Step 4: Running the database link

When a Database link has been set up it can be run at any time.

1. Select Database links to open the Database Links dialog, if required.

2. Select your new Survey Import link and click Run.
3. A Report dialog is displayed. You can see the number of cases imported in Processed Cases of the Report dialog. Click OK to close the report.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Analysing two surveys together appeared first on SnapSurveys.

Step 1: Creating the time questions

1. In the Questionnaire window, create two open ended questions. These are used to enter the start time and end time.
2. In the Topics toolbar select Response and set the question response type to Time.

Step 2: Creating a variable to calculate the time difference

A Variable needs to be set up in order to calculate the interval between the Start and End time. This variable needs to be set up as a Derived, Quantity-Response Variable. That is, a new variable deriving (taking information) from other variables.

1. In the Variables window, click New Variable to create a new Variable.
2. Ensure the following settings are applied:
   • Name: V1
   • Label: Time difference
   • Type: Derived
   • Response: Quantity
3. In the Values column in the same row as Not Ask type: Q1 missing or Q2 missing. This will ensure that the time difference is not calculated if Q1 or Q2 are empty.
4. Type the formula below in the values column but this time in the same row as Valid:

((Q2\1)*60 + (Q2-Q2\1)*100) – ((Q1\1)*60 + (Q1-Q1\1)*100) + (1440 * num(Q2<Q1))

The formula is divided into 3 sections. The first section converts the answers to Q2 into minutes, the second section converts Q1 into minutes. The last section is only needed if times could span midnight – it adds 1440 minutes if Q2 has an answer after midnight and Q1 has an answer before midnight.

Note: Time is analysed in minutes. There are 60 minutes in each hour and 1440 minutes in 24 hours: 60 (minutes in an hour) X 24 (hours in a day)

Step 3: Creating a derived variable to display time categories

Another variable needs to be set in order to display the results in time categories.

1. In the Variables window, click New Variable to create a new Variable.
2. Enter the following settings:
   • Name: V2
   • Label: Time categories
   • Text: Duration between start and end times
   • Type: Derived
   • Response: Single
3. Add the following code information in the Code list to create the categories and values:

4. Click on the Count Responses button to calculate the responses. The Counts column in the variable will display the results.
5. Click on the Save button. You can use the variable to create a table or chart of the results by entering the derived variable name in the analysis box of your table or chart to generate the results.

Using other built-in time functions

Other built-in functions that may be used for extracting information from times are shown in the table.

You can build a similar derived variable to categorize replies according to the day of week, regardless of the month or the year, by using the weekday function in a similar way to the way the month function was used in the previous example.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Calculating the time in minutes between two time variables appeared first on SnapSurveys.

The UK postcode standard is made up of four components; Area, District, Sector and Unit. Each component is then broken down to accept a particular literal response as detailed in the table below:

Step 1: Creating a new pattern to extract the postcode area

The first step is to create a new pattern that will extract postcode area from a full UK postcode.

1. Select View | Patterns from the Snap XMP Desktop menu to open the Patterns dialog.
2. Find and select the postcode uk pattern then click Clone to make a copy of it.

3. Name the new pattern UK Postal Area.
4. In the Result box, clear the details then right click and select Component | area. {area} is shown in the Result box.

5. To test the pattern, click Test and type a postcode in the text box. If set up correctly, the result box should display just the area component of the postcode.
6. Click OK to exit the pattern test and then click OK again to save your pattern.

Step 2: Creating a derived variable to use the new pattern

The second step is to create a derived variable that can be used in conjunction with the UK Postal Area pattern that you have created.

1. Select Variables  from the Snap toolbar.
2. Click New variable to create a new variable.
3. Enter the following values:
   1. Name PostalArea (or another meaningful name)
   2. Label Area
   3. Type Derived
   4. Response Single
4. In the code list you can identify the postcode areas that you want to use in the analysis stage. In the example, the analysis looks at the postal areas for Birmingham (B), Bristol (BS) and Reading (RG).
5. Click Count Responses  which displays the results in the Counts column.

6. Click Save  to save the derived variable. You can then use this new variable to create tables and charts.

If there is a topic you would like a tutorial on, email to snapideas@snapsurveys.com

The post Using patterns to categorise postcodes by postal area appeared first on SnapSurveys.

The questionnaire needs to ask for the date when the participants went on vacation, the date they returned and their age in order to calculate the analysis requirements.

Create a variable for the number of days away

1. Enter two open ended, date response questions, one for the vacation departure date and one for the vacation return date. Then create Q3 using a multiple-choice, single response question to capture respondent’s ages. The questions are shown here.

2. From the Variables window click New Variable to create the variable that will contain the formula to work out the number of days between the two dates

3. Enter the following information:
   • Name: DaysAway
   • Label: Days Away on Vacation
   • Type: Derived
   • Response: Quantity
   • Text: Days Away on Vacation
4. In the Values column, in the same row as Valid, type Q2-Q1. This will calculate the number of days between holiday departure date and holiday return date.

Create a table showing the average length of stay by age

1. Click on Analysis Table . This opens the Analysis Definition dialog.

2. In Analysis, enter Q3 What is your age?
3. In the Calculate dropdown select Means and Significances and in the box to the right enter the derived variable, DaysAway.
4. Click OK to show results.

Grouping the number of days on vacation into bands

This will categorise the data so that a table or chart can be produced.

1. From the Variables window  click New Variable  to create the variable to show the amount of days from the departure date to the return date.
2. Enter the following information:
   • Name: Length
   • Label: Length of Vacation
   • Type: Derived
   • Response: Single
3. In the Code list, click the Label field for Code 1 and enter 1 to 3 days. Press Tab on the keyboard, to move to the Values column. In this column Snap needs to know what numbers are to be displayed in this answer, enter 1 to 3. Follow the same procedure to create categories for 4 to 6 days, 7 to 10 days and over 10 days.

4. Click on Toggle Definitions   and change Calculation to DaysAway.
5. Click Save  to save.
6. Click on Analysis Table to show the Analysis Definition window.
7. In Analysis enter Length. Click OK to show results.

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com

The post Calculating the number of days between two dates appeared first on SnapSurveys.

In this example, four multiple choice questions are used, each with four possible answers. Using weighted variables we identify the response for a correct answer as those weighted as one and for an incorrect answer as those weighted as zero. Using these numbers the total number of correct responses can be calculated.

Insert the questions

1. Create a multiple choice question with a single response.

2. Select Q1 and click Clone to set up three more multiple choice questions called Q2, Q3 and Q4, each with four possible answers (codes) and with a single response.

Create the weights

1. Click on the weights  button to open the Weights window.
2. Select New Weight to create a new weight. In Name enter ScoreA. This will be used for each question where A is the correct answer.
   In Code List, enter a Value of 1 for Code 1 and 0 for Code 2, 3 and 4.

3. Click Save  to save the new weight and close the Weight Details window.
4. Repeat steps 2 to 3 to create three more similar weights for ScoreB, ScoreC and ScoreD where the value of 1 is in codes 2, 3 and 4 respectively.

Create the derived variable

1. Open the Variables window  and click   to add a new variable which will be used to total the number of correct responses.
2. In Name enter Total.
3. Set the Type to Derived and the Response to Quantity.
4. In the Code list set the value for OK as ScoreA(Q1)+ScoreB(Q2)+ScoreC(Q3)+ScoreD(Q4)

The OK value is used as the total score. This depends on the correct answers for each question. In the example, Q1 has the answer A (the first answer), Q2 has the answer B (the second answer), Q3 has the answer C (the third answer) and Q4 has the answer D (the fourth answer).

Display the result

You can display the total score to the participant.

1. Place the cursor in the text area of a question, title or instruction where you want the total to appear.
2. Right click on the cursor and click Insert and then click Variable Field.
3. In the New variable field window select Total in the Variable list and click OK.

The variable field appears like this in your questionnaire.

When your survey is completed it will appear like this in the browser.

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com

The post Calculating marks in a quiz, test or assessment appeared first on SnapSurveys.

When a participant responds to a date response question they can enter a free format date. Examples of the date can include July 1st, 2020 or 1 Jul 20. The inbuilt operations enable date responses to be categorized by month, by day of the week or by custom specified ranges.

The following steps show how to create a derived variable which categorizes the response, to a date question, into quarters of the year.

Step 1: Create the derived variable

1. Open the Variables window  and click   to add a new variable.
2. In Name enter Quarter as the derived variable name.
3. Set the Type to Derived and Response to Single.
   

Step 2: Enter the date ranges

1. In the Code list grid, enter Quarter 1 as the Label for the first quarter then in Values enter the date range Q1 >= 1 Jan 20 and Q1 < 1 Apr 20. Any appropriate date format can be used to specify the fixed dates.
2. Repeat this for the other quarter date ranges.

3. Alternatively, for this example we can use the MONTH function. This gives a number representing the month of the year (1=January, 12=December). Using that, we can set up the codes of the categorizing variable as follows:

Using the MONTH function here produces a derived variable that works for every year. This method also takes advantage of the fact that for a derived single variable, only the upper bound for each code value needs to be specified so that any respondent in the previous code will already have been captured by that code.

Step 3: Using other built-in date functions

Other built-in functions that may be used for extracting information from dates are shown in the table.

You can build a similar derived variable to categorize replies according to the day of week, regardless of the month or the year, by using the weekday function in a similar way to the way the month function was used in the previous example.

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com

The post Analysing dates appeared first on SnapSurveys.

When analysing postcodes or zip codes in Snap XMP Desktop consider:

• The data is treated as case sensitive, which lets you enter the postcode or zip code question in a variety of ways. As a result, you need to account for every possible variation in order to capture all the data.
• If the postcodes or zip codes include letter combinations that are also in another postcode or zip code, for example, S7 and BS7, put the more complex codes before the simpler ones, that is, insert BS7 before S7.
• If the postcodes or zip codes include number combinations that are in another postcode or zip code, for example, SW1 and SW11, put a space after the shorter code.
• Additionally, it is always best to enter each postcode or zip code in inverted commas, for example, “BS8”.

Create the postcode or zip code derived variable

To create the derived single variable, please follow the steps below to categorize the postcodes and zip codes by area.

1. Open the Variables window  and click   to add a new variable.
2. Select Derived from the Type drop-down and Single from the Response drop-down. A single response is used as each respondent only falls into one of the new codes.
3. When created, a derived variable is automatically named, starting from V1. You can change this name to one that describes the variable. Enter the new derived variable name in Name.

4. In the Code list grid, click in the Label column next to code 1 and type in the first postcode or zip code area you want to categorise, in this case BS4.
5. In the Values column for code 1 type in the values to search the data. For example, when searching for any cases where BS4 has been entered the values would be Q11=”BS4″ or Q11=”bs4″ or Q11=”Bs4″ or Q11=”bS4”, with each variation of BS4 being accounted for. Note: Using the word OR, or a comma (,) in the values has the same effect.
6. Repeat steps 4 and 5 for each postcode or zip code you want to search for.
7. Click Count Responses  to see the count for each postcode or zip code area in the derived variable.
8. Click Save to save the derived variable.

You can now use the derived variable, in the same way as any other variable, in your analysis or in a filter.

If there is a topic you would like a worksheet on, email to snapideas@snapsurveys.com.

The post Analysing postcodes and zip codes appeared first on SnapSurveys.

Group variables may be used to create summary analyses of a number of variables which share a common code list such as might be found in a question grid.

For example, you could create a horizontal stacked bar chart to show the results of respondents’ opinion of five aspects of service offered.

You can use Group variables to include a summary of all five variables in the same chart. All responses will be added together when calculating means, so variables with a larger number of responses will have a larger percentage.

1. Click on the main toolbar to open the Analysis variables window.
2. Click and select New Group Variable from the drop-down list. The Group Variable Details dialog opens.

3. Give the group variable:
   • Name: GV2
   • Label: Overall opinion
   • Source: list the variables to group e.g. Q6a~Q6e, or Q1, Q5, Q8

4. Click to save the variable.
5. It appears in the list of analysis variables.

6. Click to display the Variables window.
7. Click to add a new variable. This is a blank variable that you can use to insert a blank line in your chart.
8. Specify the Variable Details:
   • Name: Blank
   • Label: Calculated difference
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Single (there will be one response for each case).

9. Click to save the new variable.
10. Click to build a chart.
11. Specify the Definition details:
    • Style: Horizontal Stacked Bar Percent Transposed
    • Analysis: GV2, Blank, Q6a, Q6b, Q6c, Q6d, Q6e
    • Transpose: Checked
    • Show Options: Check Analysis Percents

12. Select the Cells tab and set the decimal places for the Percentages to 2. This reduces rounding errors when calculating the percentages.

13. Click OK to display the chart.

The post Introduction to group variables appeared first on SnapSurveys.

Auto category variables can help you to

• Limit the number of codes to those with the highest number of responses. The codes may change when the data changes.
• Create an “Other” category to group the codes that are outside the limit.
• You can choose to remove words that add nothing to the analysis, by adding them to the list of stop words.
• Order the codes by the number of counts
• Set options on how to categorise words in the response

Auto category variable and word clouds

Creating an Analysis Cloud automatically adds an Auto Category variable.

1. In the Survey Overview, open the survey required.
2. Click Analysis Cloud   on the Snap XMP Desktop toolbar. This opens the Analysis Definition window to create a word cloud.
3. In Analysis, enter the question name. This question usually contains comments given by the respondent.

4. Click Save to save the Analysis Cloud.
5. Click Analysis Variables on the Snap XMP Desktop toolbar to show the list of analysis variables. The Auto Category variable, named AV.Q7, was automatically created when you saved the Analysis Cloud. This is shown in the list of analysis variables.

Editing the Auto Category variable

You are able to edit most of the settings in the Auto Category variable.

1. Click Analysis Variables on the Snap XMP Desktop toolbar. This opens the Analysis Variables window. All the Auto Category variables have the Method “AutoCategory Variable” in the list.
2. Double-click the Auto Category variable that you wish to edit. This opens the Auto Category Variable Details window.

3. If any analysis depends on the variable then the name is read-only, otherwise you can edit the name.
4. Changing the Source question may change the name, if any analysis uses the original Auto Category variable. This creates a new Auto Category variable when you save.
5. Limit codes sets the maximum number of codes for use, for example, a word cloud analysis will show the top 25 most commonly used words. Categories that are outside the limit codes setting appear with the category number in brackets.
6. With Other groups the codes outside the Limit codes setting. For example, if Limit codes is 25, only the top 25 codes are shown and the remaining codes are grouped in the category ‘Other’.
7. Select Order by counts to order the codes by the number of times they occur in the responses. You can also sort the categories by clicking on the column header.
8. Click to save your changes.

Include or exclude categories

1. Open the Auto Category Variable Details window.
2. Select Change codes to display the Include column.

3. Clear Include to exclude the code or select it to include the code in the analysis, such as a word cloud. The excluded code moves to the bottom of the list.
4. Click Save to save the changes.

Stopping unwanted words

When a respondent enters a comment, the response often includes common words, such as ‘the’ or ‘and’. The default stop words list includes many of these words, which means they will not appear in the Auto Category variable or the word cloud. You can add your own stop words and you can also choose to include any of the default stop words.

1. Click the Stop words button to open the Stop Words dialog where you can add stop words or allow a default stop word.

3. In Stop words, type any words you wish to exclude. Use spaces to separate the words.
4. In Allow stopped words, type any words from the Default stop words that you wish to include. Use spaces to separate the words. This is only relevant when using the Default stops words list.
5. Clear the Use checkbox if you do not want to use the Default stop words list. The default is to use the Default stop words list.
6. Click OK to save your changes.

Options

You can choose different ways of categorizing the multiple response or open-ended questions by selecting different options.

1. Click the Options button. This opens the Auto category Options dialog.

2. In the Multi Choice section, select Separate Codes to treat each code in the response as separate, otherwise the codes are categorized as a group.
3. Select Case Sensitive if you want the same word entered in different cases to be in a different category, otherwise make sure this is clear.
4. Select Separate Words unless you know that there will be common phrases (e.g., zip codes) that you wish to use as codes.
5. Select Customise Delimiters if you want to specify the delimiters.
6. Click OK to save the options.

Creating an Auto category variable for a chart

An Auto Category Variable can also be used as the source variable for a standard chart such as a bar chart.

Create the Auto Category Variable

1. Click Analysis Variables to show the list of Analysis Variables.
2. Click New Analysis Variables Item on the Snap XMP Desktop toolbar then select New Auto Category Variable from the list. This opens the Auto Category Variable Details dialog.
3. In Name, enter a name for the Auto category variable. In this example, the name is AVQ4a as the source question is Q4a.
4. In Source, enter the source question. When you tab off the field the label will update with the question text.

3. In Limit codes enter the maximum number of codes. This determines the number of bars in the chart.
4. Check Order by counts to order from the highest to lowest number of counts.
5. Click Save to save the Auto Category Variable.

Create the Analysis Chart

1. Click Analysis Chart on the Snap XMP Desktop toolbar. This opens the Analysis Definition dialog.
2. In Analysis, enter the Auto Category variable name, for example, “AVQ4a”.
3. Select Transpose.

4. Click Apply to update the chart display. This shows a bar chart of the top 5 other items ordered.

The post Introduction to Auto Category variables appeared first on SnapSurveys.

In this example of use of a Time variable, a Derived Single Response variable is used to group Q1b, “What time did you arrive?” into suitable ranges for analysis on a table or chart.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable details:
   • Name: Time
   • Label: Time of arrival
   • Type: Derived (the variable will derive its data from Q1b, the existing time of arrival question).
   • Response: Single (each respondent will fall into only one of the new codes as there is only one time of arrival per case).
4. Specify the Code Details. Any recognisable time format can be used in the Value specification but if using the 12 hour clock the am and pm suffixes must be applied

.

5. Click to save the variable. This derived variable can now be used in tables and charts in the usual way.

Categorising time responses using a time function

You can also categorise time responses using time functions. This example uses the hour function in a Derived variable to group responses to Q1b, “What time did you arrive?” into suitable ranges for analysis.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable details:
   • Name: Arrivalhour
   • Label: Hour of arrival
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Single (each respondent will fall into only one of the new codes, as there is only one time of arrival per case).
4. Set up the code values to categorise by hour.

5. Click to save the variable. You can now use this variable to analyse by respondents’ hour of arrival.

The post Categorising time responses appeared first on SnapSurveys.

Data will have already been entered into the questionnaires; including answers to the time variables (the variables must have a response type of Time). For this example, Q1b is the time of arrival and Q1c is the time of departure.

1. Click to display the Variables window.
2. Click to add a new variable.
3. To calculate the duration of waiting for each respondent, specify the Variable Details as:
   • Name: Wait
   • Label: Duration of time
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Quantity (the response will be a numeric value for each case).
4. Specify the Code Details to calculate the amount of time in seconds. The Not Asked value is specified to ensure that the calculation is only performed if data exists for the time question.

5. Click to save the variable. The variable can then be used to produce a statistics table of the duration of wait.

The post Calculating time intervals appeared first on SnapSurveys.

• Times can be categorised as falling into particular periods in the day, such as ‘rush hour’; ‘mid-morning peak’ or ‘lunchtime’.
• The difference between two times (for example between a time of arrival and a time of departure) can be calculated and the totals grouped into appropriate ranges, such as ’15 minutes waiting time’; ’30 minutes waiting time’ and ‘1 hour waiting time’.

Time formats

A wide range of ways of entering time data is supported.

• When time data is entered, use a consistent time format. If you use the twelve hour clock remember to add the am or pm suffix otherwise all times will be read as am.
• Any calculations of time between two periods that fall either side of midnight (e.g. 11.58 pm to 3.30 am) produces a positive number in calculations such as ‘Duration of time’ from time of departure minus time of arrival.

Time functions

When defining times, there are a number of functions that can be used to perform calculations and tests on the times.

The post Analysing time responses appeared first on SnapSurveys.

This example shows the use of the date function weekday. You define a Derived Single Response variable to work out the day of the week for a particular date.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable Details:
   • Name: V6
   • Label: Day of the week restaurant visited
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Single (each respondent will fall into only one of the new codes, as there is only one date of visit per case).
4. Specify the Code Details:

5. Click to save the variable. The variable can then be used in tables and charts in the usual way.

Calculating age from a date of birth

These instructions tell you how to create variables to calculate the respondent’s age from their date of birth and then put the respondents into age bands, based on the difference between a date of birth variable and the date function today. The instructions also apply if you have another date question in the questionnaire, which you want to use instead of today.

For this example, the date of birth variable is Q1a. It assumes a four-digit year.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable Details as:
   • Name: Age
   • Label: Calculated age in years
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Quantity (the response will be a numeric value for each case).

4. Specify the Code Values to calculate the number of years. Note the use of \ to extract the whole number of years.

5. Click to save the variable. You can then use this to analyse respondents’ ages.
6. The Not Asked value is specified to ensure that the calculation is only performed if data exists for the date question.
7. To band the respondents into age groups, create a new variable:
   • Name: Agegroup
   • Label: Age groups
   • Type: Derived
   • Response: Single (each respondent will only fall into one of the new codes).
8. Specify the Code Details:

9. The codes are assigned in order, so all responses that have not been sorted into previous bands appear as 65 plus.
10. Click to save the variable.

The post Date function examples appeared first on SnapSurveys.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable details:
   • Name: Date
   • Label: Quarterly visits
   • Type: Derived (the variable will derive its data from the existing date of visit question).
   • Response: Single (each respondent will fall into only one of the new codes as there is only one date of visit per case).
4. Specify the Code Details as shown in the example below. Any recognisable date format can be used in the Value specification.

5. Click to save the variable. The variable can then be used in tables and charts in the usual way.

The post Categorising date responses appeared first on SnapSurveys.

• Dates are categorised as falling in particular years, months or weeks of the year, or on particular days of the week.
• The difference between two dates can be calculated, such as calculating a respondent’s age. These can be categorised or grouped into appropriate ranges or used in the production of descriptive statistics.

Date formats

The software supports a wide range of date formats. All dates are based on the Gregorian calendar.

Day

The day number must appear in the range 1 to 31, with optional leading zeros. For example, 01, 02 are acceptable. Checks are made on the validity of a day number for the given month. For example, 31 September will be invalid. In the case of the month of February, the calculations also check whether the year is a leap year.

The day number can appear before or after the month, e.g. 1 Jan or Jan 1.

You cannot use the day suffixes such st, nd, rd, th, unless you apply a suitable date pattern to the variable.

The system settings determine the local rules for dates, such as the US standard of Month/Day/Year. Consequently, 01/05/97 will be calculated as January 5th 1997 in the US and May 1st 1997 in the UK (the computer date settings control these formats).

The characters, full stop (.), a hyphen (-) or a slash (/) separate the month, day and year. A space is used when the month is a word.

In the special case where the month name is given before the day number, a comma can be used to separate the day and year, e.g. January 5,1997

Month

The month can appear in full, e.g. January, or abbreviated to the first 3 characters, e.g. Jan, or as a number, e.g. 01.

The only exceptions to the 3 character abbreviation rule are July which can be Jul or Jly, and September which can be Sep or Sept.

The characters, full stop (.), a hyphen (-) or a slash (/) separate the month, day and year. The space character separates the month from the rest of the date if it is in word format.

Year

The year number has two formats available, either the last two digits, e.g. 97, or the full year, e.g. 1997. The characters, full stop (.), a hyphen (-) or a slash (/) separate the month, day and year.

When a year has only two digits, the default sets the year between 1930 and 2029 inclusive. In this way, 97 represents the year 1997, 00 represents 2000, and 29 represents 2029. Update the default year range by opening the Data Entry Tailoring dialog, using the Tailoring | Data Entry menu item. The Dates section contains the Date format and 2 Digit Years fields, which sets the default year range.

Date functions

When defining dates, there are a number of functions that can perform calculations and tests on the dates. The function names are not case sensitive and can be written in uppercase or mixed case.

Examples

Date functions are useful in filter expressions to return a group of data responses based on a date, for example all responses completed in a particular month or year. Filters are available in analyses, reports and when looking at responses in the data entry window.

Here are some examples of date filters using the date functions:

• ID.Date year=2023 gives responses for everyone who responded in 2023
• ID.Date year=Today year gives responses completed this year
• VisitDate Month = Today Month gives responses where respondents visited this month.
• VisitDate Month = Today Month AND VisitDate Year = Today Year returns responses where respondents visited this month in the current year, which you can use if your survey runs over a number of years
• VisitDate Month = 1 gives responses where respondents visited in January
• VisitDate Month name = “January” also gives responses where respondents visited in January
• VisitDate day = 1 gives responses where respondents visited on the first day of the month
• VisitDate weekday = 1 gives responses where respondents visited on Monday
• VisitDate weekday name = “Monday” is an alternative filter that also gives responses where respondents visited on Monday
• ID.Date weekday = (1 TO 5) gives responses completed on a weekday from Monday to Friday
• ID.Date weekday = (6 TO 7) gives responses completed on a weekend from Saturday to Sunday
• ID.Date weekday > 6 is an alternative filter which also gives responses completed on a weekend

The post Analysing date responses appeared first on SnapSurveys.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable Details:
   • Name: V5
   • Label: Amount spent
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Single (each respondent will only fall into one of the new codes).

4. Specify the Code Details:

5. Click to save the variable. The variable can then be used in tables and charts in the usual way.

The post Categorising quantity responses with a derived variable appeared first on SnapSurveys.

You can display indexed counts or sums to give a relative measure of individual cell values.

Applying the chi-square test

The Chi-square test can be applied to single-response variables. It compares observed (actual) and expected (theoretical) values in order to establish whether there is a significant relationship between two variables in a table.

To calculate the Chi-square Statistic and related statistics for a cross-tabulation or grid table,

• select the Chi square option in the Analysis Definition dialog
• click on the table analysis toolbar

The Chi-square and associated statistics will be displayed above the table

You can display the expected values by selecting the Expected Counts option in the Analysis Definition dialog .These are the values that would be expected in each cell of the table if the row variable were not influenced by the column variable or vice-versa (the Null Hypothesis). They are calculated as:

(Row Total) x (Column Total) / Table Total (Base).

Since you cannot always compare Chi-squared values directly, Snap generates a statement about the result.

The interpretation of the Chi-square test result follows a seven point scale and categorises the certainty of a relationship (or non-relationship) between the variables in the cross-tabulation as follows:

The information given in the Chi-square report is as follows:

Detail	Meaning
Chi-square value	where O is the observed (actual) value, and E the expected value for each cell.
Degrees of freedom	Relates to the number of choices that can be made in fixing the values of the expected frequencies. It is calculated as where r is the number of rows in the table and c is the number of columns. The above is a 5 by 5 table, and therefore has 16 (= 4 4) degrees of freedom.
Cramérs V	Used to compare tables of different dimensions and different sample sizes. It is calculated as where n is the number of cases and k the smaller of the number of rows and columns. Its value always lies between 0 and 1.
Phi Coefficient	Calculated as where n is the number of cases. For 2 by 2 tables Phi always lies between 0 and 1. If one dimension is greater than 2, Phi can be greater than 1. Phi can be used to compare tables of the same dimension but different sample sizes.
Contingency Coefficient	Calculated as It will always have a value between 0 and 1. Tables of different dimensions cannot be compared.
Evidence of Relationship	A statement indicating the strength of the relationship (or non-relationship) between the row and column variables. This is used because comparison of chi-square values from tables of different dimensions or different sample sizes is meaningless.
Warning	This appears when a large number of cells in the table have small Expected Values. The reliability of the Chi-square test reduces as the proportion of cells with small expected values increases. A warning appears if more than 20% of the cells of a table have an expected value of less than 5.

Indexed Values

Indexed Values are widely used in product research and other projects to give a relative measure of individual cell values. Indexed Values can be shown on the table by checking the appropriate box (Indexed Counts or Indexed Sums) option in Definition tab of the Analysis Definition dialog.

In general, for any individual cell in the table, the corresponding index value can be calculated from the formula:

The figure of 100 represents a norm or expected value. Thus index values less than 100 would be considered lower than expected and index values of greater than 100 would be considered higher than expected.

The post Statistical calculations on tables appeared first on SnapSurveys.

• A list of Quantity Responses can be produced either for all respondents or for respondents selected on the basis of a filter specification. This is done in the same way as a list of Literal Responses
• Statistics of the quantity values can be calculated either for all respondents or for respondents selected on the basis of a filter specification.

A Derived Variable can be set up to allocate the quantity data into coded ranges. These are used as an analysis or break variable on a table.

Overview of descriptive statistics

You can produce a table of descriptive statistics which shows, for each variable in the specification, the mean, mode, median, quartiles, sum, min, max, range, standard deviation, and variance, standard error of the mean, skewness and kurtosis. Any of the statistics can be excluded using the Descriptive Statistics tab on the Analysis Definition dialog.

Descriptive statistics can be calculated for all cases or for those selected on the basis of a Filter Specification. Missing values such as No Reply will be excluded from statistical calculations. The statistics can also be weighted, perhaps to correct some imbalance in the sampling, by providing a suitable weight specification.

To describe each of the statistics that may be calculated assume that a survey of households was conducted. As part of that survey the number of persons living in the household was requested. The following replies were received from the eleven households interviewed:

1, 2, 3, 4, No Reply, 3, 4, 5, 4, 6, 2

Statistic	Description
Mean	This is often called the average, and is defined as the sum of the items divided by the number of items. Mean = (1 + 2 + 3 + 4 + 3 + 4 + 5 + 4 + 6 + 2) = 34 10 = 3.4 Since one respondent gave no reply, the mean and all other statistics are calculated using a sample of 10.
Mode	The mode of a distribution is the most frequent or most popular item. If two values tie for the mode, Snap will choose the lower. Mode = 4, since 4 is the most frequently occurring value (three occurrences).
Quartile 1	25% through a range of values
Median	The midpoint or 50% through a range of values. To calculate the median, the items of the distribution are arranged in order of magnitude starting with either the smallest or the largest, then: if the number of items is odd, the median is the value of the middle item. if the number of items is even, the median is the mean of the two middle items. 1, 2, 2, 3, 3, 4, 4, 4, 5, 6 Median = (3 + 4) ÷ 2 = 3.5
Quartile 3	75% through a range of values.
Sum	The sum is calculated by adding all the values of a distribution. Sum = 1 + 2 + 3 + 4 + 3 + 4 + 5 + 4 + 6 + 2 = 34
Minimum	The minimum is the smallest value of the distribution. Minimum = 1
Maximum	The maximum is the largest value of the distribution. Maximum = 6
Range	The range shows the spread of the distribution and is calculated by subtracting the smallest value (minimum) from the largest value (maximum). Range = 6 – 1 = 5
Standard Deviation	The standard deviation is a measure of dispersion of values in a distribution. It gives an indication of how much the values deviate from the mean. Thus, a distribution with a large range would have a larger standard deviation than one with a small range. The standard deviation is calculated as: where xi is each value in the distribution, is the mean of the values and n is the number of cases. For the sample in question: Standard Deviation = 1.428286
Variance	The variance is another measure of dispersion of values in a distribution and is calculated as the square of the standard deviation: Variance = 2.04 Snap calculates the standard deviation and variance by assuming the data represents a sample rather than an entire population.
Standard Error of the Mean	The standard error of the mean is calculated by dividing the standard deviation by the square root of the number of items in the sample. It is defined as the standard deviation of the distribution of the sample mean and gives an indication of how far individual scores deviate from the mean score shown. The larger the sample, and/or the closer the individual scores are to the mean score, the smaller the standard error. Standard Error of the Mean = 1.428286 10 = 0.451664
Skewness	A distribution that is not symmetrical but has more cases toward one end of the distribution than the other is called skewed. The measures of central tendency (mean, mode and median) can vary considerably. If the mean is larger than the mid point of the range (the median) and the most frequently occurring value (the mode), the sample is said to be positively skewed. If the mean is smaller than the mid point of the range (the median) and the most frequently occurring value (the mode), the sample is said to be negatively skewed. A small skewness value (close to 0) indicates that the data is evenly distributed about the mean. With this type of distribution it would be expected that the values for mean, mode and median be similar. The skewness of the example is 0.098843 indicating a small positive skewness.
Kurtosis	Kurtosis also gives an indication of the shape of a distribution in the form of the extent to which, for a given standard deviation, the data clusters around a central point. A positive value for kurtosis indicates a distribution that is more peaked than usual. A distribution of this type would typically have most of the values clustered around a central point. A negative value for kurtosis indicates a flatter or more widely dispersed distribution. The kurtosis for the example is -0.75202

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The example below shows coding for the free text for Other food item.

1. Click to display the Variables window.
2. Click to add a new variable.
3. Specify the Variable details:
   • Name: Q4b
   • Label: Other items ordered
   • Type: Question
   • Response: Multiple (each respondent could fall into more than one of the new codes if they ordered more than one item).
4. Specify the Code Details:

5. Click to save the variable. This variable can then be used instead of the original Literal Response variable (Q4a) for data entry.

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Click to display the Variables window.

Click to add a new variable.

5. Ensure that the variable from which the derived one will gather its data has a relevant pattern applied, such as lower case.
6. Click to save the variable. The variable can then be used in tables and charts in the usual way.

Unless a pattern has been applied to the source variable, the specification of the text string must be precise. The pattern match process searches for an exact match (i.e. it is case sensitive) and looks for a capital M at the beginning of the word Milkshake and ignore lower case m. Q4a=”ilkshake” would find both.

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Click to display the Variables window.

Select the variable which will act as a source to the derived variable. Double click the variable or click to open the Variable Details dialog.

Click to display the Pattern drop-down. If it is not already selected choose lower case as the pattern.

Click to add a new variable.

8. Click to save the variable. The variable can then be used in tables and charts in the usual way.

Snap is case sensitive so an appropriate pattern must be applied to the source variable to ensure that all cases with relevant comments are captured.

Categorising postcodes

The best way of analysing postcodes is to create a Derived Single Response variable and to apply a pattern to the variable.

The following instructions show how to categorise the postcodes by area.

1. Select Tailor | Patterns to open the Patterns dialog.
2. Select postcode uk from the list and click Clone.
3. Give the pattern a new name, such as UK Postcode area.
4. Clear the Result box if required. Hover over the Result box and right click.
5. Highlight Component and a list will appear containing the components of the pattern.
6. Choose area. This will now appear in the Result box.
7. Click OK to return to the Patterns dialog
8. This pattern will be used as part of an ‘as’ expression in the derived variable.

9. From the Variables Window click to add a new variable.
10. Specify the Variable Details:
    • Name: V3
    • Label: Area
    • Type: Derived
    • Response: Single (each respondent will only fall into one of the new codes)
11. Specify the Code Details:

12. Click to save the variable. The variable can then be used in tables and charts in the usual way.

If you wish to check your results you may do so by using the filter button in the Data Entry window. Using the example above, you could enter a filter value of V3=1 to ensure you have specified the value correctly for the ‘Bristol’ area.

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1. Click to display the Data Entry window for the current survey.
2. Select the menu option File | Export to display the Data Export dialog box.
3. Set the Format to the required format.
4. Set the Destination for the data report as either File or Clipboard. The clipboard provides the most straightforward method of output and will be suitable for most surveys. However if there is a very large number of cases the computer may not have enough memory to hold all the required data on the clipboard in which case the file option should be used.
5. In the Filter field type the specification to select the cases to be reported.
6. For example to report only those cases where a response has been given to Q4a specify Q4a OK. Leaving the Content field blank will include all data in for each case that meets the filter specification in raw data format.
7. Specify the Content as the name of the variable or variables to be printed:
   • Individual questions (e.g. Q4a)
   • List of questions (e.g. Q1,Q4)
   • Range of questions (e.g. Q1 TO Q4)
   • Include the CASE variable to associate the data with a case number

8. Click OK and the data report will be output to the clipboard or the file specified.
9. Switch to the word processing package and paste the report from the clipboard into a document. Alternatively load the file if the File destination has been used.

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Click to display the Data Entry window for the current survey.

Click to display the Print Data dialog box.

3. Specify a Title for the report if required.
4. In the Filter field type the specification to select the cases to be printed:
5. For example, to print only those cases where a response has been given to Q4a specify Q4a OK.
6. Specify the Content as the name of the variable or variables to be printed:
   • Individual questions (e.g. Q4a)
   • List of questions (e.g. Q1,Q4)
   • Range of questions (e.g. Q1 TO Q4)
7. Click Print to print the report.

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1. Click Analysis List on the Snap XMP Desktop toolbar to create a list definition.
2. Enter the literal variable definition, such as Q9 in the Analysis field.
3. Press Apply to display the list.

Using a derived literal response

You can use derived literals to enter other text in your list. You can also convert dates, times and quantity variables to text in derived literals.

For example, in the Crocodile Rock Cafe survey supplied with Snap XMP Desktop, there is a date variable Q1.a and a literal variable Q8 for “Any other comments”. To create a report with the information in a readable format, you can create a derived literal that writes the date a comment was received together with the comment, and then create a list analysis of the data.

1. Click to open the Variables window.
2. Click to add a new variable.
3. Set the type to Derived and the Response to Literal.
4. Enter the text “Comment'{Q9}’ was received on:{Q1.a}.”

Note that the variable names are enclosed in {} within the text. The quotation marks included in the text (‘) are different to those that mark the beginning and end of the text in the derived literal (“). (If you need to have double quotes (“) in your text, you must use single quotes (‘) to mark the beginning and end of the text.

5. You only want to list if there are comments, so in the Not Asked field, type unless Q9 ok. This means the your derived literal will be left empty if there is no comment

6. Click to save the variable.
7. Create a list as before, but use your new derived variable, V1 in the Analysis field.

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1. Click to open the Analysis Definition window to build a table.
2. Specify the Analysis variables for the table or chart as Q6a TO Q6e (“How do you rate the following?”).
3. Specify the Break as Q12 (gender).
4. Select Means & Differences. Specify the Calculate variable/weight as Score5 (or another appropriate weight for the rating scale).

5. Click OK to build the table.

Each cell in the table shows the mean ratings of speed of service, cleanliness, etc., as calculated according to the values specified in Score5, both for the survey sample as a whole and for males and females separately. The various Analysis Difference, Break Difference and Base Difference options can all still be ticked.

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The only response types that can be used in means and differences tables/charts are Single Response and Quantity Response.

1. Click to open the Analysis Definition window to build a table.
2. Specify the Analysis variable for the table or chart as Q11 (age categories).
3. Specify the Break variable for the table or chart as Q12 (gender).
4. Select Means & Differences as the Calculate option.
5. Specify the Calculate variable as Q5 (amount spent).
6. The Show options on the Analysis Definition change to difference options.
   • Choose Analysis Difference if you want to see a comparison of the means and difference of men and women of each age group.
   • Choose Break Difference if you want to see a comparison of the means and difference of men in each age category and a separate comparison of the means and difference of women in each of the age groups.
   • Choose Base Difference if you want to see a comparison of the means of each cell with the base mean.

7. Click OK to build the table.

This example displays Break Difference so you can see the mean amount spent by men of each age category compared with the overall mean of amount spent for men. We can also see the mean amount spent by women of each age category compared with the overall mean of amount spent for women.

Converting to a chart

The difference values can usefully be displayed in the form of a 2D bar chart:

1. Click to re-define the table and change the form to have the Bar Counts style.
2. Uncheck the Means box and click OK to build the chart.

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The only response types that can be used in means and significances tables/charts are Single Response and Quantity Response.

For example, in the Crocodile Rock Cafe survey it might be required to see whether there is a significant difference in spending in a comparison of men and women who are in the same age bracket or, alternatively, it might be necessary to look at whether there is a significant difference between the amount spent by men in different age categories and the amount spent by women in different age brackets. In other words, the first example will compare men and women of the same age, whereas the second example will compare men in age categories and there will be a separate comparison between women in age categories.

1. Click to open the Analysis Definition window to build a table.
2. Specify the Analysis variable for the table or chart as Q11 (Age categories).
3. Set the Break variable for the table or chart as Q12 (Gender).
4. Set Calculate to Means & Significances.
5. Specify the Calculate variable as Q5 (Amount spent).
6. The Show options in the Analysis Definition now show significance options.
   • Choose Analysis Significance if you want to see a comparison of means between men and women.
   • Choose Break Significance if you want to see a comparison between men of different age groups and a separate comparison of women of different age groups.

7. Click OK to build the table

Each cell in the table shows the average amount spent and the significance of that value compared with every other mean in either the same column or the same row, depending on whether Analysis Significance was selected, or Break Significance.

The table displays Break Significances. You can see that there are highly significant differences between the means of females aged under 18 and those who are 55+. Amongst men, there are no significant differences. The table also displays any significant differences between the base means (all respondents) for each age category.

Converting to a chart

On a chart you can show either the mean or the significance but not both.

1. To convert this table to a chart format, click .
2. Uncheck the Break Significance option.
3. Change the form from Table to Chart and from the drop-down menu select the style Bar Counts.
4. Click OK to build the chart.

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The example describes how to display a table using the data in the Crocodile Rock Cafe survey showing the average amount spent (Q5) broken down by frequency of visit (Q2) and gender of the respondent (Q12).

1. Click to open the Analysis Definition dialog to build a table.
2. Specify the Analysis variable for the table or chart as Q2 (frequency of visit).
3. Specify the Break variable for the table or chart as Q12 (gender).
4. Set Calculate to either Means & Differences or Means & Significances. Only the means are required for this table; differences or significances will not be shown, as only the Means option is ticked.
5. Specify the Calculate variable as Q5 (amount spent).

6. Click OK to build the table.

Each cell in the table shows the average amount spent. For example, the average amount spent by males who visit the restaurant daily is 15.17.

To convert this table to a chart format

1. Click on in the table window.
2. Change the form from Table to Chart.
3. Click Browse by the Style field, and select the style Bar Counts from the drop-down list.
4. Click OK to build the chart.

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If you want these figures to be the default values for the survey, you must set them in the Analysis tailoring dialog. This also has a Summary Statistics tab. You open the Analysis Tailoring dialog by selecting Analysis from the Tailor menu.

1. Open the appropriate dialog (Analysis Definition or Analysis tailoring)
2. Click the Summary Statistics tab
3. Select the Significance (t-test) in either of the columns. The possible options appear below.

• Comparison enables you to select the base used when comparing the mean of base to the mean of each category on your table. Either use:
  • Base: the mean for all respondents
  • Base less current: the mean for respondents that are not included in the category being compared.
• Score is the score applied for all calculations
• Decimal places give the number of decimal places the significance (t-test) value is calculated to. (It is usual to keep the significance (t-test) decimal places to 0 and the mean decimal places to 2.)

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For example, in the Crocodile Rock Cafe survey it might be required to see whether there is a significant difference in perception of speed of service between different age groups.

1. Click to open the analysis Definition dialog to build a table.
2. Specify the Analysis variable for the table as Q6c (Rating of Parking).
3. Specify the Break variable for the table as Q11 (Age range).
4. Click the Summary Statistics tab.
5. Select Significance (t-test) in the Available column and click on the > button to move the option into the Used column.
6. Select Mean and click on > to move it into the Used column.
7. Specify Score5 as the score. (Score5 is the name given to a Weight created in the Crocodile Rock Cafe survey.)
8. Click OK to build the table.

The table shows the counts and percentages of each age group against the rating of speed of service. It also shows the mean rating of speed of service as specified by each age group, and the t-test (significance of the mean) in each category compared with the t-test (significance of the mean) of the base. The significance is shown as a percentage, so if we use the 95% and 99% significance levels as our markers we can see that the under 18s and 35-44 age groups have highly significant results, which means these results are likely to be repeatable in other surveys. The other age categories are not showing significant results and would not necessarily be repeated if the survey was conducted again with a similar group.

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If you want these figures to be the default values for the survey, you must set them in the Analysis tailoring dialog. This also has a Summary Statistics tab. You open the Analysis Tailoring dialog by selecting Analysis from the Tailor menu.

1. Open the appropriate dialog (Analysis Definition or Analysis tailoring)
2. Click the Summary Statistics tab
3. Select the t-test or U-test in either of the columns. The possible options appear below.

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The U test can be used to identify differences between the ratings for the different age groups.

1. Click to open the Analysis Definition dialog to build a table.
2. Specify the Analysis variable for the table as Q6a.
3. Specify the Break variable for the table as Q11 (Age).
4. Select the Summary Statistics tab.
5. Select U-Test in the Available column and click on the > button to move the U-Test option into the Used column.

6. Click OK to build the table.

Each cell in the table shows the number of respondents who ticked each particular age group and speed of service rating. The U-Test result displays 6 characters in each age group column representing the results of comparing a column with those of each other column. It indicates whether there is a significant difference or whether it is just a chance result.

This implies that there is a significant difference between those under 18, and those between 45 and 54. There may be other significant differences which are concealed because your sample size is too small or somehow unrepresentative.

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A table is set up with mean scores applied. The level of confidence between the mean scores for two different groupings can then be displayed by using a t-test.

For example, in the Crocodile Rock Cafe survey it might be required to see ratings for the overall performance of the restaurant question by age groups and to show the mean score for each of these groupings. The t-test can then be used to establish which mean scores are significantly different. For example, under 18s’ rating of overall performance is high; therefore, it would be useful to find out if this is a significant result and likely to be repeated in further surveys or just a chance thing.

1. Click to open the Analysis Definition dialog to build a table.
2. Specify the Analysis variable for the table as Attitude (the derived variable we set up to assess Overall rating of performance).
3. Specify the Break variable for the table as Q11 (Age).
4. Click the Summary Statistics tab
5. Double-click Mean in the Available column to move it to the Used column.
6. Enter score10 in the Score field. Instructions on creating the weight score10 are given in Calculating mean scores after banding a quantity variable

7. Double-click t-test in the Available column to move it into the Used column. Leave the t-test settings as they are.
8. Click on the Base/Labels tab. Because the analysis variable has been scored, change the analysis labels to make this obvious on the table.
   • Click Insert by the Analysis Label and select {label}. This inserts the appropriate code label of the analysis variable into the table.
   • Type “scored as” into the Analysis Label field
   • Click Insert by the Analysis Label and select {score}. This inserts the score for that code into the table.
9. Click OK to build the table.

Each cell in the table shows the number of respondents who ticked each particular age group and overall performance rating. The mean displays seven scores at the bottom of the table, which indicate the mean response for the base and for each age group. The mean score was set up so that good scores are positive and bad scores are negatives. (Excellent scores 5 through to Very Poor scoring -5). The Under 18 age group, showing a mean score of 1.98, indicates that they scored overall performance of the restaurant as good. The 55+ age group has the lowest mean score of -2.5 which represents an average score of poor.

The t-test result displays 6 characters in each age group column representing the results of comparing the mean score value of one column with those of each other column. It indicates whether there is a significant result between the two means or whether it is just a chance result. For example, is it just chance that the under 18s scored highly compared to the 55+ or is this highly significant and will be a repeatable trend?

These levels of confidence can be changed on the Summary Statistics tab of the Analysis Definition dialog. Highlight t-test and alter the numbers in the Upper Level and Lower Level dialog boxes. A 99% level of confidence means that 99 times out of 100 this group of people will always score more highly. The 100 represents 100 surveys. With this level of confidence it is highly likely that the result is repeatable and not a random result.

In the example table the “under 18” t-test result displays “-bCDEF”.

• The first character place refers to itself (under 18s) and is represented with a hyphen; no significant difference is always recorded when comparing with itself.
• The second character place displays a lower case ‘b’ indicating that the result is significant at 95% confidence between the under 18s and the 18-24s.
• Character three place has a ‘C’ recorded which represents 25-34s and indicates a significant result. This result shows 99% confidence that the Under 18 age group will be more satisfied with the overall performance than the 25 to 34 age group. This result can also be seen with the next two age groups as capitals are displayed for D, E and F.

Although the t-test is a reliable statistical method that allows you to analyse any major categories of respondents and see if the way they are responding is significantly different, when comparing more than two categories of data it can produce some unexpected results. Therefore when doing analysis across more than two categories of data it is advisable to apply Tukey’s Correction, which is available in the Summary statistics tab of the Analysis Definition dialog.

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1. Click to display the Analysis Definition dialog box.
2. Specify the Analysis as Q6e, Choice of food.
3. Specify the Break as Q11, Age range.
4. Click on the Summary Statistics tab.
5. Highlight Median from the ‘Available’ list and click > to add it to the Used column. You can position the Median scores above the body of the table by using the Move Up button.
6. Click OK to display the table.

Remember that calculating the median means finding the mid-point. In this example 65 respondents are 25-34; therefore the mid-point is 32. The 32^nd respondent in that age group responded ‘Good’ so the median is ‘Good’. 72 respondents are 35-44 and the mid-point is 36. The 36^th respondent comes within the responses of ‘OK’ and the median is therefore ‘OK’.

You can add a score to the table by checking the Scored box. The median will be calculated using the score displayed.

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You can calculate the mean scores of the banded variable by scoring it, so that the different codes in the derived variables are matched back to numeric values. Open the Crocodile Rock Cafe survey.

1. Click to display the Weights Window.
2. Click to add a new weight (score10) and specify the weight matrix as:

3. You can use other scoring values if you wish. For example 100, 90, 80, etc.
4. Click to display the Variables window.
5. Click to add a new variable.
6. Specify the Variable Details for derived variable which bands Q8 into ten codes. The example shown labels the bands as 1 to 10, but you could label them Poor to Excellent.

7. Click to display the Analysis Definition dialog box.
8. Enter your new derived variable in the Analysis.
9. Specify the Break as Q11, age range.
10. Click the Summary Statistics tab.
11. Double-click Mean in the left-hand column to add it to the list of statistics.
12. A box appears for you to score the mean. Specify the Score as your new score (score10).

13. Click on the Base/Labels tab. Because the analysis variable has been scored, change the Analysis labels to make this obvious on the table.
14. Click Insert by the Analysis label and select {label}. This inserts the appropriate code label of the analysis variable into the table.
15. Type “scored as” into the Analysis label field.
16. Click Insert by the Analysis label and select {score}. This inserts the score for that code into the table.

17. Click OK to create the table.

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A default report title is entered which you can update with a more suitable title.

This dialog has three different report formats:

• Detailed, single column
• Produces a report showing all the elements of each weight, as specified in the Weight Details window, in a single column format

• Detailed, double column
• Produces a report showing all the elements of each weight, as specified in the Weight Details window, in a double column format

• Summary
• Produces an overview-type report that matches the data in the Weights Window

The report settings can be changed by selecting Setup. Click Print and the report will be printed.

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You can use weights to subtract the negative from the positive values of a rating scale variable. By using this data in a statistics table, you can see whether the ratings were generally positive or negative.

This example assumes a five-code scale, in which the middle value is neutral, such as Q6a (speed of service). Because the rating scale only contains counts of responses, you need to set up a derived variable in order to calculate the statistics of the scored response.

1. Click to display the Weights window.
2. Click to add a new weight and specify the Weight Details as follows:
   • Name: WT6
   • Label: Weight pos/neg
   • Decimal places: 0
   • Number of codes: 5
3. Specify the Code Details:

4. Click to save the Weight.
5. Click to display the Variables window.
6. Click to add a new variable.
7. Specify the Variable Details:
   • Name: V10
   • Label: Calculated difference
   • Type: Derived (the variable will derive its data from other existing variables).
   • Response: Quantity (the response will be a numeric value for each case).
8. Specify the Code Details to combine the Weight with the Speed of service rating variable:

9. Click on to save the variable.
10. Click to display the Analysis Definition dialog to create a table.
11. Specify the Analysis as V10, which is the variable to be weighted.
12. Select Statistics table from the list for the Break (or enter STATS).
13. Click OK and the table will be built.

The Sum will be the difference between the positive and negative values.

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The questions to be scored are 6.a to 6.e in the Crocodile Rock Cafe survey.

1. To check which question this is, open the Crocodile Rock Cafe survey and scroll down to question 6.

2. Click or press Ctrl+R to open the variables window.
3. Find the grid questions and look at the Code list so you know which labels seen by the respondent are assigned to which codes.