Queries

The list of available queries appears under the Queries tab on the Survey page.

The list includes the name, description, and owner of each query, and shows whether or not each query is shared. Anyone who has privileges to create and run queries against this survey can view shared queries.

• To run a query in the list, click on the name of the query.
• The edit the query, click the Edit link. This allows changes to variables, filters, cross-tabs, and other attributes of the query.
• To change properties such as the query name and description, whether it includes test data and whether it is shared, click the Properties link.
• To delete the query, click Delete. A User can delete only those queries that they own.
• To create a new query, click the New button that is located at the top or bottom of the Queries list.

 

To create a query, follow these steps:

1. Click on the Survey name listed in the Projects tab for the survey you wish to query

2. Click the Queries tab on the selected Survey page.

3. Click the New button.
4. Type a name for the new query and include an optional description.

5. (Optional) Check the Share option to give others the ability to see and run this query.
6. (Optional) Check the Use Test Data option to see only test data in the Query. Test data include any data submitted by participants who have been marked as testers, and any data created through the Add Random Test Data page.
7. Click Continue to create the new query.
8. Once the query is created, follow the instruction for Editing a Query to choose variables, filters, etc.

To edit a query, click the Queries tab of the Survey page, then click the Edit link next to the name of the query you want to edit.

After clicking the edit link, the Query Edit page will open.

Editing a query involves selecting a list of variables, and optionally applying criteria for filtering, sorting, cross-tabulation, time periods and submit status. The Query Edit page provides access to each of these items.

Choosing Variables

To include a variable in query results, simply click on the variable in the All Variables list. Each variable selected appears in the Selected Variables list to the right.

Variables will appear in the query result in the same order they appear in the Selected Variables list. To re-order variables in the Selected Variables list, click on the name of the variable, then click one of the arrows to the right of the list.

NOTE: When using the tools on the right of the list, only one variable can be moved at a time.  It is a best practice to select the variables in the order they should appear in the Selected Variables list.

To remove a variable from the list, either uncheck the box next to the variable name in the All Variables list, or select the variable in the Selected Variables list and click the delete button to the right of the list.

Shortcuts to Add or Remove Variables

The All Variables list is divided to into sections, each of which represents a collection. It is possible to select all of the variables in a collection by clicking All in the blue bar to the right of the collection name. De-selecting all of the variables in a collection can be achieved by clicking the Clear link in the blue bar.

• Clicking All at the top of the list selects all of the variables from the entire survey.
• Clicking Clear at the top of the list removes all variables from the query.

Seeing Test Data

It is possible to query test data instead of real participant-submitted data. Test data includes any data submitted by test participants (who usually work with the survey designer during the design process), or any data generated by the Data Manager’s Add Random Test Data feature.

The properties section at the top of the Query Edit page tells you whether Test Data will be included in your query results.

To show test data in the query results:

1. Click the Properties button at the top of the query page.
2. Check the box Use Test Data option.
3. Click Save.

Sorting

Raw query results can be sorted by any variable that appears in the results. Sort Variables can be defined before running the query by checking the Sort box and selecting a variable from the Sorting Variable list.

It is possible to choose more than one sort variable.

To add a secondary sort, click on the Add link and select another variable by which to sort. The top sort will be the primary sort, with subsequent sorts applied afterward.

 

To remove a sort, click on the Remove link.

 

The sort direction can be Ascending or Descending. “Ascending” is the default, and it will sort text alphabetically, a through z. Numbers will sort from lowest to highest. Dates and times will sort from early to late. If “Descending” is selected for Direction, the sort will be reversed.

Cross-Tabs

When defining a cross tab variable, Illume breaks down summary results according to scale values of the cross tab.

For example, if the survey includes a question called GENDER, and it is selected as the cross tab variable, then the query results will show response data for males and females side by side.

To apply a cross tab variable, check the Cross Tab checkbox below the All Variables list, then choose the cross tab variable from the Cross Tab Variable list.

Cross-Tabs and Preloaded Data

Pre-loaded data can be used as a crosstab variable only if the preloaded item has a scale (a defined set of possible values). The person designing the survey decides whether the preloaded data will have a scale.

Submit Status

By default, the query will return results from both completed, partial and terminated surveys. A completed survey is one in which the participant clicked the final Submit button. A partial survey is one in which a participant may have answered some questions, but left before clicking the Submit button. A Terminated Survey is one that has “Terminate” set in a Jump Object, and has been exited out of the survey based on Jump-If logic. Data can be limited in the query to include either Partial Submissions, Completed Surveys or Terminated Surveys by following these steps:

1. Check the Submit Status checkbox.

2. Under Submit Status, check Partial Submissions, Completed Surveys or Terminated Surveys. Any combination can also be selected.

NOTE: If no box is checked, partial, completed and terminated submissions will appear in the results.  The only way to restrict results to a single submission type is to check only one of the boxes.

Filtering

A filter or condition can be added to a query to limit the results that are returned by the query. The user defines filters by selecting the variable to filter, the operator to apply to the filter, and the value to be used by the filter. The result is in the form of “Variable Operator Value”.

Filtering and Yes/No Questions

When applying filters to Yes/No questions, there are some special considerations.

Yes/No questions include both individual checkboxes and check all that apply questions.

 

These questions can have three possible values in the dataset: Yes, No, and Unanswered.

• A value of Yes indicates the participant saw the question and checked the box.
• A value of No indicates the participant saw the question and chose not to check the box.
• A value of Unanswered indicates the participant never saw the question. This is often due to show-if logic.

If a survey with 1000 respondents includes a conditionally displayed checkbox question, the responses may break down as follows:

Yes	No	Unanswered
600	300	100

In a query that includes a filter on this question, the number of responses that pass through the filter will vary depending on the Operators selected.

Filter	Results	Comment
QUESTION Is 1:Yes	600	Includes only those who saw the question and checked the box.
QUESTION Is 0:No	300	Includes only those who saw the question and did not check the box.
QUESTION Is Not 1:Yes	300	Includes only those who saw the question and did not check the box.
QUESTION Is answered	900	Includes all who saw the question, whether they checked the box or not.
Question Is unanswered	100	Includes only those who never saw the question.

 

In some cases, it may be interesting to see responses from everyone who did not answer Yes to the question. In this example, 400 participants did not say yes: there were 300 explicit Nos and 100 who never saw the question. To get results for these 400 participants, you need two filters:

• QUESTION Is 0:No
• QUESTION Is unanswered

These filters must be joined with an OR in the Logic section. The final filter to retrieve these 400 results looks like this:

Filtering Unanswered Questions

When applying a filter like this to a query:

 

STATE Is Not : FLORIDA

It would be expected to see results from all participants who did not indicate Florida as their state of residence.

In fact, the results would display participants who said they live in some state other than Florida, but will not display participants who never answered the question about what state they live in. For participants who did not respond to the STATE question, DatStat Illume cannot definitively determine whether or not they live in Florida, so it excludes them from the results.

If the query results should include everyone except those who explicitly stated that they live in Florida, the following two filters must be applied:

STATE Is Not Florida

OR

STATE Is unanswered

 

NOTE: The two statements use “OR,” not “AND.” This ensures that anyone meeting either criterion appears in the result set: either they explicitly stated that they do not live in Florida, or they did not answer the question.

Advanced Filtering

The real power of query filtering comes from adding multiple filters and logically organizing the filters to retrieve narrowly targeted results. For example, two filters can be applied to the same variable to ensure values fall within an expected range. When two or more filters are used, it is possible to AND the results (where all filters are true) OR the results (where either filter is true). This causes the query to return the intersection or union of the individual filters, respectively.

If more than two filters are used, further power can be gained by logically arranging the filters in an expression. For example, it is possible to retrieve results where the first filter is true and either the second or third filter is true.

In the example above, this query will find all participants in grade 10 or above who report being sick or injured at least 3 times.

Complex Expressions

To write complex expressions:

• Notice that each filter has a letter ID, beginning with A and proceeding through the alphabet. Use the letter IDs to represent filters in your expression.
• Operators in an expression include AND, OR, and NOT.
• AND generally limits the number of results, since conditions on both sides of the and operator must be true for data to pass the filter.
• OR generally expands the number of results, since only one of the conditions in the OR statement must be true to pass the filter.
• NOT negates a condition.
• AND is evaluated before OR if they are on the same level of the expression.
• Use parentheses to set the logical structure of the expressions. Nested expressions are evaluated first.
• A letter can be used in an expression multiple times (A and B) or (A and C) for example.
• It is not necessary to refer to ALL filters in the complex expression. In the above example, it is OK to write (B or C), essentially ignoring the A condition.
• Expressions must be well formed.

This notation is the same as that used in creating complex show-if conditions in the Survey Designer.

Time Period

When a survey has multiple time periods, the query can filter the data by time period. The default (no time period selected) will not filter the data and data from all time periods will be returned.

 

A saved query, can be ran at any time with a single click. To save a query, simply give it a name and a description and click the Save button at the top of the Query Edit page.

 

The query name will appear in the Query list, In the Query Tab of the survey to which it belongs.  Clicking on the query name displays the results.

 

Save vs. Save As

An existing query can be saved by clicking the Save button if that user is the creator of that query.

If a User opens a shared query, they can only Edit that query and Save As another name.  Only the Query owner can modify their queries. Save As can also be used by the Query owner to create a different query from the first.

 

View of options when viewing a shared query not owned:

 

 

To run a query from the Survey page, click the Queries tab, then click the name of the query you want to execute.

To run a query from the Query Edit page, click the Run Query button at the bottom of the page.

 

Sharing a query makes it available to others. Anyone who has privileges to view the data in the survey that underlies the shared query will be able to see and execute the query once shared. In addition, they may modify the shared query and save it as their own. Other users’ modifications will not affect the original queries. Users cannot change any attributes of any queries they did not create.

Sharing and Un-sharing Queries

To share a query:

1. Go to the Query Properties dialog. There are two ways to reach this:
   • From the Survey page, click the Queries tab, then click the Properties link next to the query you want to share.
   • From the Query page, click the Query tab, then click the Properties button.
2. Check the box next to Share to share the query. Uncheck the box to stop sharing.
3. Click Continue to save the change.

When a query is shared, it will appear in the Queries list of other users who are allowed to view data from the underlying survey. The creators name will appear as the owner.

In addition, shared queries appear with the small arrow icon in the slide-out navigation tree on the left side of each Data Manager page.

To delete a query, click the Queries tab on the Survey page, then click the Delete link next to the name of the query to delete.

NOTE: A User can only delete queries they have created.

Looping Explained

Conceptually Looping is the same as repeating a Collection of questions a number of times.  Think of each Collection as a survey in itself. Where there would be one row of data for a single submitted survey, each looping survey will create a number of rows.

 

For Example:

We will build a survey in pieces and discuss what is happening visually and in the

data.

1. Starting with a simple survey of 3 questions. The last question being:  “How many people are in your household”?

If this were the only questions  in the survey, a submission would result in one row of data.

 

2. Add a Loop Object of type Open-Ended.  Using a pipe from the question of how many in the household, we can set the minimum, and possibly maximum, to the number given.

Each Iteration of this loop asks for the name of that person.

There will be 2 Iterations because we answered 2 to the number in the household.

The two people are Beau and Sasha.

 

If this survey is taken once it will result in two rows of data

 

3. Next we add a Nested Open-Ended Loop Object to the previous Object.  We need to add a final question to the list of questions in the first Loop Object.

The last question asks how many prescription drugs the person takes.

 

If this survey is taken once and the number given individuals is 2. The first person listed is

Beau and he takes 1 prescription medication.  The second person is Sasha and she takes  2 prescription medications. There will be 2 Loops of the questions within the

object.

 

This would result in 3 rows of data upon submission.

Beau has one row because of the 1 prescription medications

Sasha has two rows because of the 2 prescription medications

 

NOTE: Beau’s results would also be 1 if he had taken 0 prescription medications because there was only one iteration in the Nested Loop Object.

 

4. Now we put a twist into this example.  We add a Follow-up Loop that asks 2 questions about each of the people listed in the first loop. Because there are no new individuals listed this example would not result in any additional rows of data. A single submission would still result in 3 rows of data.

5. Continuing on, let’s add another loop, but this time a Pre-defined Loop.  This loop will have four pre-defined Loop Iteration Response Options.  This makes it behave like 4 different variables. So a single submit with the same information will now have 12 rows of data.  Beau had 1 and we multiply that by 4 to a total of 4.  Sasha had 2 multiplied by 4 for a total of 8.

 

Depending on how the loops are created and the number of responses, one submission could spawn many more rows of data than our example. When using Loops within a survey, careful and thoughtful querying is important to get at the data desired.

Queries with Loops

Querying the dataset of a survey that does not contain Loop Objects is straight forward because each respondent submission equates to one row. As we saw in the example above, adding Loop Objects can create many more rows of data for that one submission.

There are some Loop Object details that will make getting at the desired data easier:

All Iteration Types

• Create queries that contain either all Non-Looped Variables or only Looped Variables
• With looping, the selected variables will affect filtering

Open-Ended Loop

• LOOP_OBJECT will give the total number of iterations for that loop
• DATSTAT.LOOP_OBJECT.LOOP will contain the iteration for that Loop

Follow-Up Loop

• LOOP_OBJECT will give the total number of iterations for that loop
• LOOP_OBJECT.ANSWERED will either have Answered or Unanswered for that loop iteration in a Follow-up Loop

Pre-Defined Loop

• LOOP_OBJECT will present a comma separated list of the Loop Response Options
• LOOP_OBJECT.RESPONSE_OPTION lists Yes or No for was that iteration answered or not
• DATSTAT.LOOP_OBJECT.LOOP will display the Response Option for that Iteration

 

Query results are available in five formats:

• Summary Results – The Summary Results tab includes counts and percentages showing how many times participants chose each available response option. Summary statistics are presented as tables of figures. Clicking on any of the counts in the summary filters the results. For example, if 50 participants answered YES to a question, clicking on the number 50 next to the YES answer limits the results to those 50 participants who answered YES.

For questions with numeric scales, the tables include:

• Count – The total number of responses to a question.
• Min – The response with the lowest value for the given question.
• Max – The response with the highest value for the given question.
• Sum – The sum of all responses to the question.
• Mean – The average value of all responses.
• Median – 50% of responses have a value greater than or equal to this value; 50% have a value less than or equal to this.
• Std Dev – Standard Deviation. A measure of dispersion from the mean.
• Variance – Another measure of dispersion from the mean, this is the square of the standard deviation.
• Bar Graphs – The Bar Graphs tab represents summary data in graphical format. Bar graphs include response counts and percentages. Clicking on a bar, or on the count to the right of the bar narrows the results, just as clicking a number in the Summary Results does.

• Raw Data – The Raw Data tab shows a table of raw participant results. Each row represents a single participant. Each column represents a question. Each cell in the table contains a participant’s response to a single question. Click any row number in the Participants view to see the survey results for that participant.
• Participant – This view displays survey results for an individual participant. The only way to view this is to click on a row number in the Raw Data tab.
• Download – The download tab provides several options for downloading query results into various formats.

Each view of the query results includes a header describing how the results were filtered.

• Drill Down – Drill downs are filters added by clicking on one of the counts on the Summary Results page, or by clicking on a count or a bar in the Bar Graphs page. Those clicks drill down into narrower subsets of the data returned by the original query. Remove a drill down filter by clicking the Remove link to the right of the filter description.
• Time Periods – If Time Periods appear in the results header, the results are limited to the associated time periods. If the results header shows no time period restrictions, then the results include all time periods. Remove Time Period limits by editing the query on the Query Edit page.
• Filters – These filters were defined on the Query Edit page as part of the query. To remove them, click the Query tab and click the Remove link next to the filter description on the Query Edit page.
• Sort Columns  – These describe the sort criteria defined in the query. Sort criteria apply only to raw results, not summary results.

 

In addition, the “Results Include” note on the right side of the Results Header shows which data are included in the results. Data may include any or all of the following:

• Completed Surveys – These are surveys that have been completed and submitted with the submit button.
• Terminated Surveys – These are surveys that contained a Jump Object that ended the survey based on JumpIf logic.  These are submitted surveys that usually result from an ineligible participant.
• Partial Submissions – These are surveys that have not been completed. The participant has supplied some answers, but has not clicked the submit button to submit the completed survey.
• Test Data – (not shown in this example) Test data include data submitted by users who have been marked as testers and data that have been automatically generated through the Data Manager’s Random Test Data feature. Test data were not submitted by normal (non-tester) participants. If the “Results Include” note says that Test Data are included, then all of the data in the results you are looking at are test data.

 

To view Summary Statistics:

1. From the Survey page, click the Queries tab, then click on the name of the query to run.
2. From the Query Edit page, click the Run Query button at the bottom of the page.
3. From the Results page, click the Summary tab.

Summary Results include counts and percentages showing how many times participants chose each available response option. Summary statistics are presented as tables of figures.

For questions with numeric scales, the tables include:

• Count – The total number of responses to a question.
• Min – The response with the lowest value for the given question.
• Max – The response with the highest value for the given question.
• Sum – The sum of all responses to the question.
• Mean – The average value of all responses.
• Median – 50% of responses have a value greater than or equal to this value; 50% have a value less than or equal to this.
• Std Dev – Standard Deviation. A measure of dispersion from the mean.
• Variance – Another measure of dispersion from the mean, this is the square of the standard deviation.

 

In addition, this display includes the question name and type, the display type and data type, and for items with scales, a list of all the scale values and labels.

Questions with scales also include a chart icon.  Click this to see a chart displaying response data. These charts are customizable and are available in a variety of formats. See Charting Results for more information.

Drilling Down

Click on any Count value to create a Drill Down filter. The page will refresh to show only those submissions that match the filter.

 

For example, if the results show 800 participants whose age is 18-21, by clicking on the number 800, the page will refresh to show statistics from only those 800 participants aged 18-21.

 

Drill down filters can be added by clicking on other counts.

 

The drill down filters currently in effect appear at the top of the results page. Click Remove next to any filter to remove it. Removing a filter expands the number of results.

 

After adding or removing filters, they can be saved as part of the query by clicking the Save button at the top of the page. The query can also be saved, with filters, as a new query by clicking Save As.

 

When filters are saved as part of the query, Illume will apply the filters automatically the next time the query is run.

 

Bar Graph results represent response counts in a visual format. To view Bar Graph results, click the Bar Graphs tab on the Results page after the query is run.

Drill Down Filters

Click on any bar or underlined response count (to the right of each bar) to create a Drill Down filter. The page will refresh to show only those submissions that match the filter.

 

For example, if the results show 800 participants whose age is 18-21, and you click on the number 800, the page will refresh to show statistics from only those 800 participants aged 18-21.

 

More drill down filters can be added by clicking on other bars or counts.

 

The drill down filters currently in effect appear at the top of the results page. Click Remove next to any filter to remove it. Removing a filter expands the number of results.

 

After adding or removing filters, the filters can be saved as part of the query by clicking the Save button at the top of the page. The query can also be saved, with filters, as a new query by clicking Save As.

 

When the filters are saved as part of the query, Illume will apply the filters automatically the next time the query is run.

Illume can produce charts to display query results for any question that has a scale. These charts can be dragged and dropped into other common applications, such as Microsoft Word and PowerPoint.

To view charts:

1. Create and execute a query; or click on any existing query in the left navigation bar.
2. Click the chart icon  under any question name on the summary results page to view a chart displaying the query results for that question. (The chart icon appears only for questions that have a scale. Illume does not produce charts for questions with open-ended responses because the number of potential responses is too large to display in a single chart.)

 

The chart appears in a new window that includes customizable chart properties and a summary of information in addition to the chart itself.

Customizing the Appearance of the Chart

To alter the appearance of the chart, set any of the properties at the top of the chart page and click Redraw.

The chart page provides the following configurable properties:

• Height The height of the chart, in pixels.
• Width The width of the chart, in pixels.
• Type The type of chart to display.
• Data This determines whether the chart displays raw counts of answers or percentages.
• Theme This controls the chart’s color scheme.
• Include title If this is checked, the chart will include the text in the box to the right as the title of the chart.This is useful if you will be dragging the chart into another application.
• Include subtitle If this is checked, the chart will include the text in the box to the right as the subtitle of the chart.
• Show Scale Values – Check this option to display scale values along the X-axis of bar and line charts, or in the legend of pie charts. This setting does not apply to box and whisker charts.
• Show Scale Labels – Check this option to display scale value labels along the X-axis of bar and line charts, or in the legend of pie charts. This setting does not apply to box and whisker charts.
• Max Label Length – Enter a whole number to specify the maximum number of characters that should appear for scale value labels in the chart. This setting applies only if Show Scale Labels is checked. Illume truncates the scale value label at the number of characters you specify. You may want to limit scale value label length when the labels take up too much space, or are so long that they can only be rendered in a tiny font.
• Use fill patterns When this is checked, the chart uses patterns rather than colors as the primary means of distinguishing segments of the data. This option applies mainly to pie charts.
• Include Filters – Check this option to include a description of the query filters in the chart. You may want to do this if you are dragging the chart into another application such as PowerPoint.

Exporting Charts into Other Applications

To export a chart into another application, simply drag the chart from your browser window into an open document belonging to the other application. Most Windows applications that support Drag and Drop and PNG image files will receive the chart.

 

When exporting, it’s often useful to include a title and subtitle within the chart itself. You can customize these properties using the Include title/subtitle fields in the Chart Properties section at top of the page.

 

You can also drag the tables of data below the chart into applications such as Word and PowerPoint:

1. Move the mouse pointer to a point just below the chart image.
2. Click and drag the mouse to the bottom of the page. The data tables should now have a dark background, indicating they are selected.
3. Drag the data tables into Microsoft Word or PowerPoint.

 

The tables will appear in the Word/PowerPoint document, retaining most of their formatting.

Known Issues with Dragging and Dropping

Microsoft Word may make slight alterations to the appearance of data tables.

 

Microsoft PowerPoint may split nested tables into several separate tables. You can position the separate tables independently, or you can select them all at once and move them as a group.

 

To select all of the tables at once within PowerPoint:

1. Move the mouse to a position above and to the left of all of the tables.
2. Click and drag the mouse to a point below and to the right of all of the tables. You’ll see that all of the tables now have borders around them.
3. Click on any of the table borders, hold the mouse button down, and drag the table wherever you want it to go. All of the other tables will move with the table you’re dragging.

 

The Raw Data tab on the Results page displays the raw results.

In a Survey without Looping Objects, each row in the results table contains the responses of a single participant.

In a Survey that contains Looping Objects the number of rows per respondent submission is based on the number of objects, the type of looping and the number of responses in the looped variables.

See Querying and Data in a Looping Survey

Click on column headers to sort the data. The yellow column is sorted, the arrow indicates the direction of the sort. To view the complete response from one of the participants, click on the row number, which is underlined.

To navigate to a specific page in the results. Select from the “Page” drop down menu.

 

To modify the number of results displayed on a page, change “Results per page”.

 

Options for results per page are 1, 2, 5, 10, 20, 50, 100, and 200.

 

Clicking on the arrows at the top of the table will move the current view to the previous or next page. The double arrow buttons are useful to move to the first or last page in the results table.

Additional Information

The number of rows that were returned from the query are displayed in the bottom left corner of the screen.

The number of pages is displayed in the table title bar.

 

To download the results of a query, click the Download tab on the results page. Note that the data downloaded here includes only the data returned by the query. To download all of the survey data, see Downloading Data.

 

 

Downloading Results

To download data:

1. Click the Download tab on the Results page.
2. Choose the type of data to download. Summary data includes only statistics about the results, such as counts and percentages. Raw data includes all of the actual responses that participants have submitted.
3. Choose a file format. These are explained in more detail below.
4. Click Download.

File Formats

A number of different file formats are available. SPSS and SAS data can be viewed in the raw data format only. Summary data are not available for SPSS and SAS. Data can be downloaded raw or summary data in Excel, HTML, Tab Delimited text, or XML.

 

• HTML – This is suitable for formatted display in most current browsers, including Internet Explorer and MozillaFirefox.
• XML – This is suitable for applications that can manipulate XML.
• SPSS (.sav) – This format will import the scale code and label for questions with text datatype/scale codes. There is no limit to the length of the scale code.  The maximum length of text data is 32767.
• SPSS Syntax – This is suitable for import into newer versions of SPSS.
• SPSS (Short Names) – This is suitable for import into newer or older versions of SPSS.
• SAS (zip) –  The ZIP file contains a syntax or program file and a separate data files. Contains all the features outlined in the SAS download section. The separate data file allows text data to be as long as 32760 characters. The data file is encoded using UTF-8 (Unicode) so non-ASCII chars are preserved.

• SAS (raw data only) – This is suitable for import into SAS.
• Double quote values (“) are preserved in text values.
• The pipe character (|) is replaced by an asterisk (*) in text values.
• Have output formats for date (DATE9), time (TIME8), and date/time (DATETIME18) data types.  Without an output format, these values display as a numeric value which is not very human readable.
• The text “informat” length is calculated and based on the maximum length contained in the data set for each specific text property.
• Text data is truncated at 958 characters.
• Tab Delimited – This is suitable for unformatted viewing in Microsoft Excel or in any text editor. In addition,many SQL databases will import data from tab-delimited text files.
• Excel – This is suitable for formatted display in Microsoft Excel.
• MS SQL – This produces a SQL script that will
• 1. 1. Create the necessary tables in SQL server
     2. Insert data into the tables.

The SPSS (Short Names) format limits all variable names to eight characters, to comply with naming restrictions in older versions of SPSS. The other SPSS format leaves your survey variable names intact, and is compatible with newer versions of SPSS.

 

To import data into a SQL database other than Microsoft SQL Server, download using the MS SQL format and run the CREATE TABLE statements in the file. Import the data by removing or replacing the GO statements, or by downloading the tab-delimited data and import that into the newly created database tables.

Summary Data Format

The summary data format displays the aggregate values that can be calculated from the participant data (Count, Percent, Max, Min, Mean, etc). With summary data it is easy to see the breakdown of responses for a particular question. With questions that allow for free-form text responses, such as a comments, aggregate values cannot be calculated. Only the number of the responses can be calculated. If you would like to view text-items in a list instead of viewing only the number of responses to this type of question, choose the “list text items” option. This will provide an overall report that includes summary data for questions with scale values and raw data for text-entry questions.

NOTE: If there are a large number of responses, the list of comments may be long in the report.

Raw Data Format

The raw data format displays questions in a large table. This is your standard spreadsheet layout where each column is a question, and each row is a participant’s response. Each cell of the table is a participant’s response to a particular question. To run reports outside of the Data Manager, download the raw data and import them into other statistical packages.

SAS and SPSS

SAS and SPSS download formats may include only raw data. The options to List Text Items and Include Value Labels do not apply to these formats. Because these formats can only include raw data, the text items are always part of the download. In addition, the downloads include not only the data, but a data dictionary SAS and SPSS automatically import. This means that the value labels always come with the SAS and SPSS downloads and will be present when you import the data into SAS or SPSS.

List Text Items Option

Summary results include no useful information about text items. Because text questions are open-ended, permitting an almost unlimited range of responses, Illume does not calculate counts, percentages, or other statistics for these questions.

If the List Text Items option is checked, the download will include raw data for each of the text questions in the survey. For example, if 100 participants typed in their first names, the download will include all 100 first names.

Include Value Labels Option

The Include Value Labels option adds value labels next to the value codes in the data you download.

For example, a question called GENDER may have two response options: 1 = Male and 2 = Female. When you download the raw data, the GENDER column will be a list of 1’s and 2’s.

 

If Include Value Labels is checked, each entry in the GENDER column will be either 1:Male or 2:Female.

This option makes the data more readable to humans, but it hinders applications such as Excel from processing the data in a purely numeric way.

The Include Value Labels option applies only to Raw data in formats other than SAS and SPSS.

 

To view results from an individual participant:

1. Click on the Raw Data tab on the Results page.
2. Click the underlined row number of the participant whose survey is desired. Note that the row number appears in the first column of the table.

Participant Response Data

The individual participant response view shows all of the participant’s survey responses, even if the query included only a few variables.

For each question in this participant’s response, this view displays the Variable Name, the Variable Description, the Scale Value Code (if any), and the Actual Value (the Scale Value Label seen by the participant).

The layout of this screen is somewhat similar to the Data Dictionary, with questions broken down by collection and displayed in order.

Click on any collection name to limit the variables displayed to that collection.

Returning to the Participants View

To return to the participants view, click the Participants tab.