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.

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

The post Calculating the difference between times on different dates appeared first on SnapSurveys.

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

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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

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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.

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