ADesignThatCanActuallyAnswerYourQuestion.
Consulting on quantitative research design — variables, sampling, instrument design, and statistical test selection. Getting this right before data collection prevents problems no amount of analysis can fix afterward.
Analysis can't fix a design that can't answer your question.
Once data is collected, a poorly chosen sample, an unreliable instrument, or a mismatched design are largely fixed problems. Most of the value in quantitative research consulting happens before you collect a single data point.
What It Is
Quantitative research uses numerical data and statistical analysis to test hypotheses and measure relationships between variables.
Design-First
Getting the design right (variables, sampling, instrument) before data collection prevents problems no amount of analysis can fix afterward.
Every Design Type
Experimental, quasi-experimental, correlational, and survey-based designs — each answers a different kind of question.
Four quantitative designs, and what each is for.
| Design | Purpose | Example |
|---|---|---|
| Experimental | Testing cause-and-effect by manipulating a variable under controlled conditions | Testing a new teaching method's effect on test scores |
| Quasi-Experimental | Testing cause-and-effect without full random assignment, using naturally occurring groups | Comparing outcomes between two existing school cohorts |
| Correlational | Measuring the strength and direction of a relationship between variables, without manipulation | Relationship between study hours and exam performance |
| Survey-Based | Collecting standardized data from a sample to describe or generalize to a population | Measuring employee satisfaction across a company |
From hypothesis to a design you can defend.
Hypothesis & Variable Definition
Clarifying independent, dependent, and control variables and turning a vague research question into a testable hypothesis.
Learn more about Hypothesis & Variable DefinitionSurvey & Instrument Design Review
Feedback on question wording, scale choice, and structure to reduce bias and improve reliability.
Learn more about Survey & Instrument Design ReviewSampling Strategy Design
Working out probability or non-probability sampling, sample size, and inclusion criteria examiners will accept.
Learn more about Sampling Strategy DesignStatistical Test Selection
Matching your hypotheses and data types to the correct test — with a direct line into SPSS Analysis for execution support.
Learn more about Statistical Test SelectionStatistical Power Analysis
Determining the sample size needed to detect a meaningful effect, with a defensible justification for your methodology chapter.
Learn more about Statistical Power AnalysisResults Reporting Guidance
Structuring a results chapter with correctly reported statistics, tables, and figures in your required style.
Learn more about Results Reporting GuidanceFrom a research idea to reportable results.
Define Hypotheses & Variables
Turning your research questions into testable hypotheses with clearly defined variables.
Choose Design & Sampling
Selecting a design and sampling strategy that can actually answer your hypotheses within your resources and timeline.
Collect & Clean Data
Guidance on instrument piloting, data collection logistics, and cleaning before analysis begins.
Analyze & Report
Connecting into statistical test selection and results reporting, with a direct path to SPSS Analysis support for execution.
Terms worth knowing before your first session.
- Independent / Dependent Variable
- The variable you manipulate or categorize (independent) and the outcome you measure (dependent).
- Hypothesis
- A specific, testable statement predicting a relationship between variables, derived from your research question.
- Population vs. Sample
- The population is the full group you want to draw conclusions about; the sample is the subset you actually collect data from.
- Reliability
- The consistency of a measurement — whether it produces the same result under consistent conditions.
- Validity
- Whether a measurement actually captures the concept it's intended to measure.
- Statistical Power
- The probability that a study will detect a real effect if one exists, given its sample size and design.
- Operationalization
- Turning an abstract concept (e.g. "job satisfaction") into something measurable (e.g. a specific survey scale).
- Confidence Interval
- A range of values likely to contain the true population value, at a stated level of confidence (commonly 95%).
Common questions.
This page covers the design stage — hypotheses, variables, sampling, and instrument design — that happens before you ever open SPSS. The SPSS Analysis page covers running and interpreting the actual statistical tests once you have data. Many people use both, in sequence.
It depends on whether you can (and ethically should) manipulate a variable. If you can randomly assign participants to conditions, an experimental design lets you make causal claims. If you're measuring naturally occurring variables without manipulation, you're in correlational or survey-based territory, and causal language needs to be avoided in your reporting.
Statistical power is the probability your study will detect a real effect if one exists. An underpowered study (too small a sample) risks a false negative — missing a real effect simply because you didn't have enough data to detect it. A power analysis, done before data collection, gives you a defensible target sample size.
Yes — we review question wording, scale type (Likert, semantic differential, etc.), and overall structure for common sources of bias and unreliability before you distribute it, which is far easier to fix at the design stage than after data collection.
We generally recommend using an existing validated scale where one exists for your construct — it saves you from having to establish reliability and validity from scratch, and we can help you find and correctly cite an appropriate one.
Tell us about your study.
Share your research questions and where you are in the design process — we'll respond within a few hours.