Question Metadata - VoxDash Help Center

The Question Metadata panel allows you to configure the technical and statistical properties of an individual question. These settings determine how the question and its responses are represented in the exported dataset, particularly when the questionnaire is exported to statistical software such as SPSS. Question metadata does not change how a question is displayed to respondents or how they interact with it. Instead, it defines how collected responses are stored, formatted, labeled, and interpreted during data analysis. Metadata is especially useful for researchers, data analysts, and organizations that require standardized datasets. Properly configured metadata helps ensure that exported data has consistent:

Variable names and labels
Value labels
Data types
Measurement levels
Numeric formats
String widths
Missing and special values
Date and time formats

Tip: In most cases, you can keep the default metadata generated by VoxDash. Modify these settings only when your research methodology, reporting standards, statistical requirements, or organizational data dictionary requires specific formatting.

Accessing Question Metadata

To open the Question Metadata panel:

Open the questionnaire in the Editor Panel.
Select the question you want to configure.
Open the Question Metadata section in the right-side panel.
Review or modify the available metadata settings.
Save the questionnaire to apply your changes.

The metadata options available in the panel may vary depending on the selected question type and its configuration.

Question Metadata Sections

The Question Metadata panel contains the following sections:

Label Preparation
Advanced SPSS Settings
SPSS Format
Missing & Special Values
Data Type Information
Date and Time Format

Note: The Date and Time Format section is available only for questions that use date, time, or date-and-time inputs.

1. Label Preparation

The Label Preparation section controls how the selected question and its response options are identified in the exported dataset. Clear labels are especially important when working with large SPSS datasets containing hundreds or thousands of variables. Descriptive labels allow analysts to understand what a variable represents without having to refer back to the original questionnaire. The available settings include:

Question Full Text
Variable Label
Bytes Counter
Value Label — available for applicable choice-based questions

1.1 Question Full Text

The Question Full Text field displays the complete text of the selected survey question. This information provides a reference when preparing variable labels, reviewing the questionnaire structure, or generating documentation. Example: Question shown to respondents:

How satisfied are you with your recent online shopping experience?

The Question Full Text field contains the complete question text. The full text can be particularly useful when a questionnaire contains several questions with similar wording.

1.2 Variable Label

The Variable Label is the descriptive label assigned to the question in the exported dataset. A variable name is generally short and system-oriented, while a variable label should provide enough information for an analyst to understand what the variable represents. Example:

Question	Variable Label
How satisfied are you with your recent online shopping experience?	Customer Satisfaction

In SPSS, the variable may appear as:

Variable Name : Q5
Variable Label: Customer Satisfaction

A descriptive label such as Customer Satisfaction is easier to understand than a generic label such as Question 5.

Best Practices for Variable Labels

Use labels that are:

Clear and descriptive
Consistent across questionnaires
Easy for analysts to understand
Consistent with your organization’s data dictionary
Short enough to comply with the limitations of the export format

Tip: Use a consistent naming and labeling convention across questionnaires. This makes it easier to combine datasets and perform analysis across multiple projects.

1.2.1 Bytes Counter

The Bytes Counter displays the number of bytes currently used by the Variable Label. This counter helps you monitor the label length and avoid exceeding the supported limit of the export format. Example: Variable Label:

Customer Satisfaction

Bytes Counter:

21 / 255 bytes

Note: The byte count may differ from the number of visible characters when the label contains characters that require more than one byte to encode.

1.3 Value Label

The Value Label section is available for applicable choice-based questions. It allows you to define how individual response options are represented in the exported SPSS dataset. The section may include the following fields:

Field	Description
Value	The response value assigned to the option
Option Text	The text displayed to respondents
SPSS Value	The value used for the option in SPSS
Bytes Max	The maximum supported size for the value label

By default, the SPSS Value label is based on the Option Text. You can edit it when you need a different label for statistical analysis or reporting. Example:

Value	Option Text	SPSS Value
1	Very dissatisfied	Very dissatisfied
2	Dissatisfied	Dissatisfied
3	Neutral	Neutral
4	Satisfied	Satisfied
5	Very satisfied	Very satisfied

You can modify the SPSS value label if your organization’s data dictionary requires a different naming convention.

Tip: Keep value labels descriptive and consistent. Avoid unnecessary abbreviations unless your organization has an established standard for them.

2. Advanced SPSS Settings

The Advanced SPSS Settings section controls how the selected question is represented and formatted in SPSS. These settings are primarily intended for users who need more control over statistical data formatting. Most users can keep the default values unless their organization has specific SPSS or data-processing requirements. Available settings may include:

Numeric Width
Numeric Decimals
Numeric Format
String Width

2.1 Numeric Width

Numeric Width specifies the maximum number of characters allocated to a numeric value in the exported SPSS format. Default: 10 Example: Question:

How many products did you purchase?

Possible response:

With a numeric width of 10, the exported variable is configured using a width of 10 characters.

Note: Numeric width controls the SPSS storage/display format. It does not define the acceptable response range for respondents. Use the question’s validation settings to control which values respondents are allowed to enter.

2.2 Numeric Decimals

Numeric Decimals specifies the number of decimal places used when formatting numeric values. Default: 0 For example:

Stored Value	Decimal Places
5	0
5.5	1
5.75	2

If Numeric Decimals is set to 2, the SPSS format is configured to display numeric values with two decimal places. This setting is particularly useful for questions involving:

Measurements
Currency
Percentages
Average scores
Calculated values
Other values that require decimal precision

2.3 Numeric Format

Numeric Format determines how numeric values are formatted in SPSS. Default: F (Fixed Numeric) The format works together with Numeric Width and Numeric Decimals. Examples:

Value	SPSS Format
25	F10.0
82.35	F10.2

For example:

F10.2

means:

F = Fixed numeric format
10 = Field width
2 = Number of decimal places

Tip: In most cases, the default numeric format is sufficient. Change it only when your statistical analysis or organization’s export standards require a specific format.

2.4 String Width

String Width defines the maximum number of characters allocated to a string/text variable in the exported dataset. Default: 255 Example: Question:

Please describe your shopping experience.

If String Width is set to 255, the exported field is configured to accommodate up to 255 characters. String width is particularly relevant for questions such as:

Long Text
Text
Email
URL
Barcode
Other text-based question types

Tip: Choose a width that accommodates the expected response while avoiding unnecessarily large fields when your organization’s data standards require more restrictive definitions.

3. SPSS Format

The SPSS Format section summarizes the formatting configuration applied to the selected question. Depending on the question’s data type, the available information may include:

Format
Width
Decimals
Configuration Level
Admin Defaults

Example:

Setting	Value
Format	F
Width	10
Decimals	0
Configuration Level	Admin Defaults

This section provides a quick overview of how the question will be represented when exported to SPSS.

3.1 Configuration Level

The Configuration Level indicates where the current metadata configuration originates. VoxDash can use administrator-defined defaults to maintain consistent SPSS metadata across questionnaires. These defaults can be automatically applied to questions unless they are overridden by a question-specific configuration. This approach allows organizations to:

Standardize SPSS exports
Reduce repetitive configuration
Maintain consistent metadata across projects
Apply exceptions when a specific question requires different settings

You can change the configuration level when a question requires a different configuration from the administrator default.

Tip: If your organization has centralized metadata standards, configure the administrator defaults first and use question-level overrides only when necessary.

4. Missing & Special Values

Respondents cannot always provide a valid answer to every question. For example, a respondent may:

Not know the answer
Refuse to answer
Not be eligible to answer the question
Consider the question not applicable
Be unable to provide the requested information

Instead of treating all unavailable responses as the same type of missing data, you can assign predefined Missing & Special Values. These values allow the exported dataset to distinguish between different reasons why a valid response is unavailable. Common examples include:

Don’t Know
Refused to Answer
Not Applicable

When configured appropriately, these values can be recognized by SPSS as missing values rather than valid observations.

Important: Missing values are metadata definitions. You do not need to add them as response options in the questionnaire. Use the Missing & Special Values section to define them for the selected question.

4.1 Add a Missing Value

To add a missing value:

Click + Add Missing Value.
Enter the Code.
Enter the Label.
Select the appropriate Configuration Level.
Save the configuration.

Example: Question:

What is your annual household income?

You could configure the following missing values:

Code	Label
97	Not Applicable
98	Don’t Know
99	Refused to Answer

The resulting dataset could contain:

Respondent	Stored Value
John	65000
Sarah	98
David	99

In this example:

65000 is a valid income value.
98 represents Don’t Know.
99 represents Refused to Answer.

Important: Choose missing-value codes that cannot be confused with legitimate responses. For example, if 99 could be a valid answer to a particular question, use a different code.

4.2 Reset Missing Values

Click Reset Missing Values to restore the administrator’s default missing-value definitions. Use this option when you want to:

Remove question-specific missing-value changes
Discard customized definitions
Return to your organization’s standard missing-value configuration

For example, if your organization uses 97, 98, and 99 consistently for special responses, resetting the configuration restores those administrator-defined values.

5. Data Type Information

The Data Type Information section displays how the selected question is represented in the exported dataset. The two key properties are:

Measurement Level
Data Type

These properties are important because statistical software uses them to determine how variables should be interpreted during analysis.

5.1 Measurement Level

Measurement Level defines the statistical level of the variable. The commonly used measurement levels are:

Nominal
Ordinal
Scale

5.1.1 Nominal Nominal variables represent categories that do not have a meaningful order. Examples include:

Country
Customer type
Product category
Gender
Payment method

For example, if a question asks:

Which payment method did you use?

The responses Credit Card, PayPal, and Bank Transfer are categories without an inherent statistical order. Therefore, the variable is nominal. 5.1.2 Ordinal Ordinal variables represent categories with a meaningful order, but the difference between categories is not necessarily equal. Examples include:

Satisfaction scales
Agreement scales
Ranking questions
Star ratings

For example:

How satisfied are you with your purchase?

with responses ranging from Very Dissatisfied to Very Satisfied is an ordinal variable because the responses have a meaningful order. 5.1.3 Scale Scale variables represent numeric measurements for which mathematical operations are meaningful. Examples include:

Age
Revenue
Number of purchases
Numeric scores
Slider values
Measurements

Example:

Question	Measurement Level
Customer type	Nominal
Satisfaction (1–5)	Ordinal
Age	Scale
Revenue	Scale

Note: The measurement level describes how the variable should be interpreted statistically. It is separate from the underlying data type.

6. Data Type

Data Type defines the underlying type of value stored for the question. Common data types include:

Numeric
String
Date
DateTime
Time

Example:

Question	Data Type
Age	Numeric
Customer Name	String
Purchase Date	Date
Interview Start Time	Time

The data type determines how the value is represented in the exported dataset and affects how statistical software can process it. For example, a numeric value can be used in mathematical calculations, while a string value is treated as text.

7. Date and Time Format

The Date and Time Format section is available for questions that collect date, time, or date-and-time responses. This setting determines how the value is formatted in the exported dataset. Depending on the question configuration, formats may include:

Date
Time
DateTime

Examples of SPSS formats include:

DATE11
DATETIME20
TIME8

The available format depends on the underlying question and input type.

Note: Date and time formatting affects how the value is represented in the exported dataset. It does not change the date or time entered by the respondent.

SPSS Metadata Based on Question Type

VoxDash automatically determines much of the SPSS metadata based on the selected question type and, where applicable, its specific input type. This means that two questions that look similar in the questionnaire can have different SPSS representations depending on their underlying configuration. For example:

A Number input is exported as a numeric scale variable.
A Text input is exported as a string variable.
A Date input is exported using an SPSS date format.
A Rating Scale is generally exported as a numeric ordinal variable.

Important: The question type determines the default metadata. Question-specific settings, administrator defaults, and export rules can further affect the final configuration.

The following sections summarize the default SPSS metadata for supported question types.

1. Choice and Rating Questions

Choice-based and rating questions are generally exported as numeric variables in SPSS. The numeric values represent the selected options, while the measurement level determines how those values should be interpreted statistically.

Question Type	SPSS Format	Data Type	Measurement Level
Radio Button Group	F10.0	Numeric	NOMINAL
Dropdown	F10.0	Numeric	NOMINAL
Checkboxes	F10.0	Numeric	NOMINAL
Multi-Select Dropdown	F10.0	Numeric	NOMINAL
Yes/No (Boolean)	F10.0	Numeric	NOMINAL
Image Picker	F10.0	Numeric	NOMINAL
Ranking	F10.0	Numeric	ORDINAL
Rating Scale — Labels	F10.0	Numeric	ORDINAL
Rating Scale — Stars	F10.0	Numeric	ORDINAL
Rating Scale — Smileys	F10.0	Numeric	ORDINAL

How to Interpret These Settings

Most choice-based questions are stored as numeric variables in SPSS. For example:

A Radio Button Group is generally Nominal because its categories do not inherently have an order.
A Rating Scale is Ordinal because the response categories have an ordered relationship.
A Ranking question is Ordinal because the rank positions have a meaningful order.

Note: A numeric representation does not automatically mean that the variable is a Scale variable. The Measurement Level determines how the variable should be interpreted statistically.

2. Single-Line Input Questions

Single-Line Input questions support multiple input types. Their SPSS metadata depends on the selected input type.

Input Type	SPSS Format	Data Type	Measurement Level
No input type	A255	String	NOMINAL
Text	A255	String	NOMINAL
Color	A255	String	NOMINAL
Email	A255	String	NOMINAL
Password	A255	String	NOMINAL
Phone Number	A255	String	NOMINAL
URL	A255	String	NOMINAL
Number	F10.0	Numeric	SCALE
Range	F10.0	Numeric	SCALE
Date	DATE11	Date	SCALE
Date and Time	DATETIME20	DateTime	SCALE
Time	TIME8	Time	SCALE
Month	A255	String	SCALE
Week	A255	String	SCALE

2.1 Why the Input Type Matters

A Single-Line Input is not a single SPSS data type. Its underlying input type determines how the response is stored. For example:

Text          → A255       → String
Number        → F10.0      → Numeric
Date          → DATE11     → Date
DateTime      → DATETIME20 → DateTime
Time          → TIME8      → Time

Therefore, when reviewing metadata or preparing an SPSS export, always consider both the question type and the input type.

Note: Some input types, such as Month and Week, are currently represented as strings while retaining a Scale measurement level according to the current metadata rules.

3. Text and Capture Questions

Text and capture questions generally store respondent-provided content as string variables.

Question Type	SPSS Format	Data Type	Measurement Level
Long Text	A255	String	NOMINAL
File Upload	A255	String	NOMINAL
Signature	A255	String	NOMINAL

3.1 Long Text

Long Text responses are stored as strings because respondents can enter free-form text rather than predefined numeric values.

3.2 File Upload

For File Upload questions, the exported value is represented as a string. The value can be used to identify or reference the uploaded file according to the application’s export behavior.

3.3 Signature

Signature is also represented as a string variable in the metadata model.

Note: String-based metadata describes how the exported value is represented. It does not necessarily mean that the original uploaded content itself is embedded directly inside the SPSS dataset.

4. Container and Non-Exported Question Types

Some questionnaire elements are designed to organize or structure other questions rather than collect an independent response. These elements do not necessarily create a standalone SPSS variable.

Question Type	SPSS Format	Data Type / Export Rule
Panel	—	Not exported
Dynamic Panel	—	Container
Single-Select Matrix	—	Per-cell metadata
Multi-Select Matrix	—	Per-column metadata
Dynamic Matrix	—	Per-column metadata
Multiple Textboxes	—	Per-sub-item metadata
HTML	—	Not exported
Image	—	Not exported
Expression	—	Not exported

4.1 Panel and Dynamic Panel

A Panel or Dynamic Panel acts as a container for other questionnaire elements. Because the container itself does not represent an independent respondent variable, it does not create a standalone SPSS variable.

4.2 Matrix Questions

Matrix questions require special handling because the matrix itself contains multiple response items. Depending on the matrix type, metadata can be applied to its individual components:

Single-Select Matrix → metadata is applied per cell.
Multi-Select Matrix → metadata is applied per column.
Dynamic Matrix → metadata is applied per column.

Therefore, do not expect one SPSS variable to represent the entire matrix.

4.3 Multiple Textboxes

A Multiple Textboxes question contains several individual input fields. Metadata is therefore handled per sub-item, with each sub-item potentially producing its own exported variable. For example, a question containing separate fields for:

First Name
Last Name
Email

may result in separate variables in the exported dataset.

4.4 HTML, Image, and Expression

HTML, Image, and Expression elements do not create standalone exported variables according to the current export rules. They are primarily used to:

Present content
Structure the questionnaire
Display information
Perform calculations or expressions within the questionnaire

5. Custom Question Types

VoxDash also supports custom question types that are registered separately from the standard Questionnaire Builder toolbox. Their SPSS metadata follows the application’s registered question-type rules.

Custom Question Type	SPSS Format	Data Type	Measurement Level
Multi Image Picker	F10.0	Numeric	NOMINAL
Button Group	F10.0	Numeric	NOMINAL
Emotions Rating	F10.0	Numeric	ORDINAL
Sortable List	F10.0	Numeric	ORDINAL
Currency	F10.0	Numeric	SCALE
Spin Button	F10.0	Numeric	SCALE
NoUiSlider	F10.0	Numeric	SCALE
Autocomplete	A255	String	NOMINAL
Autocomplete Text	A255	String	NOMINAL
Barcode	A255	String	NOMINAL
Colour Picker	A255	String	NOMINAL
Audio Recorder	A255	String	NOMINAL
Microphone	A255	String	NOMINAL
Date Picker	DATE11	Date	SCALE
DateTime Picker	DATETIME20	DateTime	SCALE

Custom Type Examples

Multi Image Picker behaves similarly to other image-selection questions and is exported as a numeric nominal variable.
Emotions Rating is exported as a numeric ordinal variable because the response represents an ordered rating.
Currency is stored as a numeric scale variable, allowing the resulting values to be used in numerical analysis.
Autocomplete and Barcode are represented as strings because their responses are text-based identifiers rather than measurements.
Date Picker and DateTime Picker use dedicated SPSS date/time formats rather than generic string formats.

Understanding SPSS Format Notation

The SPSS format displayed in the metadata provides information about how a value is represented in the exported dataset. For example:

F10.0

means:

F → Fixed numeric format
10 → Field width
0 → Number of decimal places

Similarly:

A255

represents a string field with a width of 255 characters. Date and time values use dedicated formats such as:

DATE11
DATETIME20
TIME8

The exact format depends on the underlying data type and export configuration.

Complete Example

Suppose you create the following questionnaire question:

How satisfied are you with your recent online shopping experience?

Respondents answer using a 1–5 rating scale. You configure the metadata as follows:

Property	Value
Variable Label	Customer Satisfaction
Numeric Width	10
Numeric Decimals	0
Numeric Format	F
Measurement Level	Ordinal
Data Type	Numeric
Missing Value	98 = Don’t Know
Missing Value	99 = Refused to Answer

Because this is a rating-scale question, VoxDash represents it as a numeric ordinal variable. When exported to SPSS, the variable may appear similar to:

Variable Name	Variable Label	Type	Missing Values
Q5	Customer Satisfaction	Numeric	98 = Don’t Know, 99 = Refused to Answer

An analyst can therefore immediately determine:

What the variable represents
That the responses are numeric
That the variable is ordinal
Which values represent special or missing responses
How the numeric values are formatted

This reduces the amount of manual preparation required before statistical analysis.

Metadata and Question Type: Important Considerations

When reviewing Question Metadata, keep the following principles in mind.

1.Question Type Affects Default Metadata

VoxDash uses the selected question type to determine the default SPSS representation. For example:

Radio Button Group → Numeric / Nominal
Rating Scale       → Numeric / Ordinal
Number             → Numeric / Scale
Text               → String / Nominal
Date               → Date / Scale

2.Input Type Can Change the Underlying Data Type

This is particularly important for Single-Line Input questions. A Single-Line Input configured as Text is fundamentally different from one configured as:

Number
Date
Date and Time
Time
Range

Always review the input type when checking the expected SPSS representation.

3.Containers Do Not Necessarily Create Variables

Elements such as Panels, Matrices, and Multiple Textboxes can contain multiple response items. Their metadata is therefore handled according to their internal structure rather than always producing one standalone variable.

4.Export Metadata Is Different from Respondent-Facing Configuration

Metadata determines how collected data is represented in the exported dataset. It does not necessarily change:

The appearance of the question
The question text
The respondent experience
The way the question behaves during questionnaire completion

5.Missing Values Should Be Standardized

If your organization uses standard codes such as 97, 98, and 99, apply them consistently across questionnaires where appropriate. This makes datasets easier to combine, analyze, and maintain.

Best Practices

Follow these recommendations when configuring Question Metadata:

Use clear and descriptive Variable Labels.
Keep variable labels consistent with your organization’s data dictionary.
Avoid changing default SPSS formatting unless there is a specific requirement.
Use the appropriate Measurement Level for statistical analysis.
Verify the Data Type before exporting the questionnaire.
Use consistent Missing & Special Values across questionnaires.
Make sure missing-value codes cannot be confused with valid responses.
Review numeric Width and Decimals for numeric questions.
Use an appropriate String Width for text-based variables.
Remember that the Input Type matters for Single-Line Input questions.
Review matrix and container questions separately because they may generate multiple variables rather than one.
Check metadata before exporting to SPSS, especially when the exported dataset will be analyzed by another team.

Tip: If your organization has centralized SPSS metadata standards, configure the administrator defaults first and use question-level overrides only when necessary.

Summary

The Question Metadata panel controls how questionnaire responses are represented in exported statistical datasets. It provides control over:

Variable labels
Value labels
SPSS formats
Numeric width and decimals
String width
Measurement levels
Data types
Missing and special values
Date and time formats

VoxDash automatically applies metadata rules based on the selected question type and, where applicable, its input type. Understanding these rules is particularly important when exporting questionnaires to SPSS because the same-looking response control can represent different types of data depending on its configuration. Correctly configured metadata helps you:

Produce cleaner SPSS datasets
Maintain consistent variable definitions
Reduce manual data preparation
Correctly distinguish valid and missing responses
Improve statistical analysis
Make exported datasets easier for other analysts to understand
Maintain standardized data structures across projects and questionnaires

In most cases, the default metadata is sufficient. Review and customize Question Metadata when you need to comply with specific research, statistical, reporting, or organizational data standards.

​Accessing Question Metadata

​Question Metadata Sections

​1. Label Preparation

​1.1 Question Full Text

​1.2 Variable Label

​Best Practices for Variable Labels

​1.2.1 Bytes Counter

​1.3 Value Label

​2. Advanced SPSS Settings

​2.1 Numeric Width

​2.2 Numeric Decimals

​2.3 Numeric Format

​2.4 String Width

​3. SPSS Format

​3.1 Configuration Level

​4. Missing & Special Values

​4.1 Add a Missing Value

​4.2 Reset Missing Values

​5. Data Type Information

​5.1 Measurement Level

​6. Data Type

​7. Date and Time Format

​SPSS Metadata Based on Question Type

​1. Choice and Rating Questions

​How to Interpret These Settings

​2. Single-Line Input Questions

​2.1 Why the Input Type Matters

​3. Text and Capture Questions

​3.1 Long Text

​3.2 File Upload

​3.3 Signature

​4. Container and Non-Exported Question Types

​4.1 Panel and Dynamic Panel

​4.2 Matrix Questions

​4.3 Multiple Textboxes

​4.4 HTML, Image, and Expression

​5. Custom Question Types

​Custom Type Examples

​Understanding SPSS Format Notation

​Complete Example

​Metadata and Question Type: Important Considerations

​1.Question Type Affects Default Metadata

​2.Input Type Can Change the Underlying Data Type

​3.Containers Do Not Necessarily Create Variables

​4.Export Metadata Is Different from Respondent-Facing Configuration

​5.Missing Values Should Be Standardized

​Best Practices

​Summary

Accessing Question Metadata

Question Metadata Sections

1. Label Preparation

1.1 Question Full Text

1.2 Variable Label

Best Practices for Variable Labels

1.2.1 Bytes Counter

1.3 Value Label

2. Advanced SPSS Settings

2.1 Numeric Width

2.2 Numeric Decimals

2.3 Numeric Format

2.4 String Width

3. SPSS Format

3.1 Configuration Level

4. Missing & Special Values

4.1 Add a Missing Value

4.2 Reset Missing Values

5. Data Type Information

5.1 Measurement Level

6. Data Type

7. Date and Time Format

SPSS Metadata Based on Question Type

1. Choice and Rating Questions

How to Interpret These Settings

2. Single-Line Input Questions

2.1 Why the Input Type Matters

3. Text and Capture Questions

3.1 Long Text

3.2 File Upload

3.3 Signature

4. Container and Non-Exported Question Types

4.1 Panel and Dynamic Panel

4.2 Matrix Questions

4.3 Multiple Textboxes

4.4 HTML, Image, and Expression

5. Custom Question Types

Custom Type Examples

Understanding SPSS Format Notation

Complete Example

Metadata and Question Type: Important Considerations

1.Question Type Affects Default Metadata

2.Input Type Can Change the Underlying Data Type

3.Containers Do Not Necessarily Create Variables

4.Export Metadata Is Different from Respondent-Facing Configuration

5.Missing Values Should Be Standardized

Best Practices

Summary