|Related Online Training modules|
|Question Types and Statistics|
|Generally it is best to access online training from within Q by selecting Help > Online Training|
|Part 3 of Creating and Modifying Variables (Video)
The way that Q presents data is determined by the underlying Question Type of the data. These are set automatically when importing data and can be modified in the Variables and Questions tab.
Each observation in the data file contains text. For example, data obtained from a question like:
Please enter the name of the last soft drink you bought.
Text questions are transformed into other types of questions by changing Variable Type or Coding.
Text – Multi
Multiple related fields of text for each observation in the data file. For example, from a question like:
Please type in the names of your three favorite soft drinks
Text – Multi questions are sets of two or more Text Variables.
A series of mutually exclusive and exhaustive categories (i.e., nominal or Ordinal scales). A Pick One question can be created from all non-Text Variables. For example:
Pick One – Multi
A set of categorical variables sharing the same scale points.
Please rate your satisfaction with the following restaurants
A numeric variable (i.e., interval or ratio scale). For example:
How many glasses of wine did you drink last night? ____
Number – Multi
A series of numeric variables measured on the same scale. For example:
Next to the brands below, please indicate how many times you have purchased them in the past week.
Number – Grid
Similar to a Number – Multi question except that the variables can be thought of as being ordered in two dimensions. For example:
In the past month, how many economy flights did you take on.
Qantas ___ United ___ SAS ___
In the past month, how many business class flights did you take on.
Qantas ___ United ___ SAS ___
Q infers the structure of the grid by inspecting the variables’ labels at the time of importing the data; where Q cannot discern the structure of the data this can be set using Grid Layout, which is shown when using Set Question to create a grid question.
What is usually referred to in market research as a multiple response or multi-response question. Essentially, it is a type of Pick One – Multi question where there are only two categories in each variable. The most common examples relate to non-mutually exclusive categories.
Which of the following have you bought in the past week? Tick all that apply.
Pick Any – Compact
The same as a Pick Any question stored in a max-multi format; first variable contains first response, second variable contains second response, etc. This format should only be used to represent multiple response data when there are truly huge code frames (e.g., thousands of options). It is generally inferior to a Pick Any format as it is unwieldy for data manipulation (e.g., for use in formulas) and it cannot accommodate the notion of missing data.
Prior to version 4.7.4, all cases were treated as having data. From 4.7.4, cases with no data were treated as missing.
Pick Any – Grid
Similar to a A Pick Any question where the variables can be thought of as being ordered in two dimensions.
Which of these brands are fun?
Which of these brands are sexy?
Which of these brands are masculine?
Q infers the structure of the grid by inspecting the variables’ labels at the time of importing the data; where Q cannot discern the structure of the data this can be set using Grid Layout, which is shown when using Set Question to create a grid question. For a worked example of how to setup labels and variables to be recognized as a grid see our blog here
A question containing a single date variable. Most commonly they are automatically created in data files (e.g., time stamps for when interviews are completed), but they can also be obtained as questions:
What is your date of birth?
____ / ____ / ____
Date questions can be used like any other question except for dragging and dropping to merge categories, i.e., they can be crosstabbed, used to create filters, etc.
Date questions are useful for easily changing between different time periods (e.g., weekly to quarterly data) and for creating moving averages and other smoothers (see time series charts, Time Series Analysis and moving average questions.
A Date Variable can be turned into a Date question in the Variables and Questions tab by selecting the variable and selecting Set Question or by changing the Question Type.
Dates can converted to different time scales (e.g., months, weeks, quarters) by right-clicking on a data (either in a table or the dates shown in a chart) and selecting Values.
Multiple numeric variables that represent a ranking, where the highest number is most preferred and ties are permitted. For example:
Rank the following brands according to how much you like them. Please a 3 next to the brand you like most, a 2 in your next preferred brand and a 1 next to your least preferred brand.
Note that if your question uses lowest numbers as indicating alternatives being more preferred you will need to reverse the Value assigned to each rank in the Value Attributes.
When creating Segments, using Smart Tables and creating Trees it is generally recommended to use the Ranking type as it is, in a statistical sense, the best representation of the data. However, this question type is difficult to understand and even more difficult to explain to end users and should often be avoided when preparing final results so as to avoid confusion.
This question type is used to represent the various different types of experiments, from randomized experiments (Fully randomized experiments through to Conjoint Analysis and Choice Modeling). See Experiments for more information.
Question Types Related Online Training modules Question Types and Statistics Generally it is best to access online training from within Q by selecting Help > Online Training
pick two (pick any two)
Pick two, sometimes expressed as pick any two, is the principle that in many sets of three desirable qualities, those qualities will be somewhat mutually exclusive.
One of the classic “pick two” scenarios is the triple constraint of project management and product development, which consists of these elements: schedule, scope and cost. Each constraint defines a boundary of the project and none can be altered without affecting at least one of the others. For example, if a software product must be developed quickly, it will probably be necessary to cut back on features or increase development resources.
(Image republished with permission of Compass Creative)
The Venn diagram above illustrates the pick two concept. Clients typically want products or projects to be delivered quickly and cheaply but also to be of high-quality. The areas of overlap between in the diagram indicate the combinations that are likely to be possible. The product can be: great and cheap — but not fast; fast and cheap — but not great, great and fast — but not cheap. The client is advised to pick two: Decide which two of the three requirements are most important and be a little more flexible with the third one.
Diversity training is education about demographic differences among people. The training is designed to increase participants’ understanding of various demographics and improve their interactions with people in those groups.
What does "pick two" mean? This definition explains what pick two means in terms of project management and product development.