Variables Builder
Your variables are gathered from your project details, hypotheses and conceptual framework, so nothing needs retyping. For each one, work from concept to measurement: definitions, indicators, measurement level and scale. Every check explains itself, and nothing is written for you.
Project details
Enter what you already know. Each variable should be named exactly as elsewhere in your project, one per line.
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Your variables
Select a variable to edit it. The table lists every variable and where it came from.
No variables yet. Add them in your project details above, or add one below.
Checks across all variables
Independent variable Missing
No independent variable is listed. Most studies of relationships or effects need at least one; exploratory studies may not.
Dependent variable Missing
No dependent variable is listed. A study usually needs at least one outcome to measure.
Duplicate variables Looks aligned
Every variable has a distinct name.
Copy your variables
Copies a table of your variables, which pastes into word processors and spreadsheets.
Your project draft
This tool writes only the Variables section. Everything else is shown as you entered it.
Variable types and measurement levels
A reference for choosing types and levels. References for each will be added after academic review.
Variable types
Independent variable
The variable expected to influence or explain another, or that defines the groups being compared.
Example: Hours of screen time, when studying its effect on sleep.
Dependent variable
The outcome the study measures, expected to change with the independent variable.
Example: Sleep quality, when studying the effect of screen time on it.
Mediator variable
A variable through which the independent variable is expected to affect the dependent variable.
Example: Bedtime, if screen time affects sleep quality by delaying bedtime.
Moderator variable
A variable expected to change the strength or direction of the relationship between two others.
Example: Age, if screen time affects sleep more in younger students.
Control variable
A variable held constant or adjusted for, so that it doesn't distort the relationship being studied.
Example: Caffeine intake, recorded and adjusted for in the analysis.
Extraneous variable
Any variable other than those being studied that could affect the outcome if it isn't controlled.
Example: Room noise during a sleep study.
Confounding variable
A variable related to both the independent and the dependent variable, which can make a relationship appear that isn't there, or hide one that is.
Example: Stress, if it both increases screen time and reduces sleep quality.
Measurement levels
Nominal
Values are named categories with no order, such as types or groups.
Learn more about nominal
Examples
- Country of birth
- Blood group
Strengths
- Simple to record and to understand.
- Suits counts and percentages of each category.
Limitations
- Categories can't be ranked or averaged.
- Only a limited range of statistics applies.
References
References awaiting academic review.
Ordinal
Values are categories in a meaningful order, but the distances between them aren't known to be equal.
Learn more about ordinal
Examples
- Highest level of education
- Pain rated as mild, moderate or severe
Strengths
- Captures order as well as category.
- Easy for respondents to answer.
Limitations
- The size of the gap between categories is unknown.
- Averages can mislead, because categories may not be evenly spaced.
References
References awaiting academic review.
Interval
Numbers with equal distances between values, but no true zero point.
Learn more about interval
Examples
- Temperature in degrees Celsius
- Calendar year
Strengths
- Differences between values are meaningful.
- Suits a wide range of statistics, including means.
Limitations
- Ratios aren't meaningful: 20 °C isn't twice as warm as 10 °C.
- True interval data are less common in social research than often assumed.
References
References awaiting academic review.
Ratio
Numbers with equal distances between values and a true zero, so ratios are meaningful.
Learn more about ratio
Examples
- Monthly income
- Number of hours slept
Strengths
- Allows every arithmetic comparison, including “twice as much”.
- Supports the widest range of statistics.
Limitations
- Often needs precise measurement instruments.
- Values are often unevenly spread, with a few very large ones.
References
References awaiting academic review.
Binary
A variable with exactly two possible values.
Learn more about binary
Examples
- Passed or failed
- Employed or not employed
Strengths
- Simple and unambiguous to record.
- Easy to compare between groups.
Limitations
- Loses any detail between the two values.
- Many experiences don't divide neatly into two.
References
References awaiting academic review.
Likert
Answers on a rating scale, such as from “strongly disagree” to “strongly agree”. A single item is ordinal; several items are often combined into a scale score, which some researchers analyse as interval data.
Learn more about likert
Examples
- Agreement with “I feel supported by my manager”, on five points
- A job satisfaction scale made of ten rated statements
Strengths
- Captures attitudes and opinions in a standard way.
- Combining several items gives a more reliable measure.
Limitations
- Whether combined scores can be treated as interval data is debated.
- Answers can be affected by response styles, such as always agreeing or avoiding the extremes.
References
References awaiting academic review.
Continuous
A numerical variable that can take any value within a range, limited only by the precision of measurement.
Learn more about continuous
Examples
- Height
- Reaction time
Strengths
- Keeps the full detail of the measurement.
- Suits many statistical techniques.
Limitations
- Measurement error can be hard to see.
- Grouping it into categories later loses information.
References
References awaiting academic review.
Categorical
A general term for variables whose values are categories. Nominal and ordinal variables are both categorical.
Learn more about categorical
Examples
- Type of school
- Preferred mode of transport
Strengths
- Suits characteristics that are naturally grouped.
- Easy to summarise in tables.
Limitations
- The term doesn't say whether the categories are ordered; state which.
- Categories with few members can be hard to analyse.
References
References awaiting academic review.
Multiple response
A question where respondents may choose more than one answer, such as “tick all that apply”.
Learn more about multiple response
Examples
- Which social media platforms do you use?
- Which support services have you contacted?
Strengths
- Captures situations where several answers are true.
- Quick for respondents to answer.
Limitations
- Each option usually has to be analysed as a separate yes/no variable.
- Percentages across options don't add up to 100.
References
References awaiting academic review.
Open-ended
Answers in the respondent's own words, without set options.
Learn more about open-ended
Examples
- What made you choose this course?
- Describe a typical working day.
Strengths
- Captures answers the researcher didn't anticipate.
- Gives rich detail and context.
Limitations
- Answers must be coded or analysed qualitatively, which takes time.
- Some respondents write little or nothing.
References
References awaiting academic review.
Next: Conceptual framework
How your variables relate, drawn as a figure.
What should I do next?
Next in the research journey: data.
Questionnaire Builder
Assembles a structured questionnaire from your variables and indicators, explains where every question comes from, and exports it to Word, PDF and Markdown.
SPSS Research Lab
Connects a research question and selected analysis to IBM SPSS Statistics procedures, output tables, assumptions and reporting tools.
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Research Table Builder
Builds publication-ready research tables in APA, IEEE, Harvard, Chicago or MLA style, from your data, your results or your project, ready to paste into Word.
SPSS: From data preparation to reporting
A connected guide to preparing research data, choosing a supported analysis, running its IBM SPSS Statistics procedure, checking assumptions, reading output and reporting results.
How the builder works
- The builder reads your project draft: research question, aim, objectives, hypotheses and conceptual framework. Enter them in the first step; later versions will bring them across from the other research tools.
- Every variable named anywhere in your project is imported once, and the builder shows where each one came from.
- Unknown information stays as a placeholder in square brackets, such as “[conceptual definition]”. The builder never writes definitions, indicators or items for you.
- The builder writes only the variables section of your project draft. Later tools will read your variables from there instead of keeping their own copies.
What this tool can't do
- It can't write definitions, indicators or questionnaire items for you. It shows placeholders until you add them, and draft items name only the indicator and answer format.
- It can't check that a definition matches the literature, or that an indicator measures what it should. That needs your reading and, ideally, an established instrument.
- It can't test reliability or validity. Those need data and the right statistical checks.
- Links to your question, objectives, hypotheses and framework are found by matching variable names, so a variable described in other words won't be matched.
- Nothing is saved unless you start a project in your research workspace, which keeps it in this browser only. Otherwise your variables are lost when you leave the page.
Awaiting review
- Academic review: the definitions and examples of each variable type, and references for them.
- Measurement review: the definitions, examples, strengths and limitations of each measurement level, references for them, and which levels are treated as combining.
- Indicator review: the guidance on indicators and the draft questionnaire item formats.
- Operational definition review: the checks comparing conceptual definitions, operational definitions and indicators.
Privacy
Everything stays in your browser. Nothing is sent anywhere or saved, and it is cleared when you leave the page. If you start a project in your research workspace, your work is also saved in this browser, and only this browser, until you delete the project.