RESEARCH SUPPORT
SPSS Analysis of Psychology Experiments
Within-Subjects and Factorial Designs
Experimental data from within-subjects, between-subjects and mixed designs analyzed in SPSS: the model that matches how participants were assigned, interactions interpreted first, and effects reported with intervals and effect sizes.
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How Are Psychology Experiments Analyzed in SPSS?
SPSS analysis of psychology experiments starts from the design: how participants were assigned, which factors were varied within each person and which between groups, and what was measured on each trial or occasion. SPSS within-subjects designs call for a model that accounts for the repeated scores from the same participants, while SPSS factorial designs add interactions, which are read before the main effects they qualify. Condition order, counterbalancing, outliers and missing cells all bear on the choice of model. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides this support: an in-house PhD-level statistician analyzes the agreed experiment and explains each decision. The theoretical reading of each effect belongs to the researcher who designed the experiment.
WHEN TO ENGAGE
When an Experiment Raises Analysis Questions
An experiment's analysis questions surface early, while the design is open, and again after a pilot, on first output and in review.
Design Still Being Fixed Conditions, the number of trials and the order of presentation are still open, and the analysis the design implies should be known before the first participant is run.
Data From a Pilot A few participants have been run, and the file layout, the coding of conditions and a plan for outliers and missing trials need settling before the full sample arrives.
Output With Many Tables The model output lists several tests, adjustments and contrasts, and it is unclear which row answers the hypothesis or which result belongs in the report.
A Reviewer Questions an Interaction A reviewer asks how a significant interaction was followed up, whether order effects were checked or why one effect size was chosen over another.
WHAT THE ANALYSIS COVERS
How the Design Becomes a Model in SPSS
An experimental model is dictated by the design, beginning with how the data file is laid out and ending with the sentence that reports an effect.
Data Layout
Within-subject conditions sit in separate columns for the SPSS repeated measures procedure while between-subject factors sit in one, so the file is arranged to match the design before any model is specified.
Within-Subjects Models
Factors varied within each participant are analyzed as in Repeated Measures ANOVA, with sphericity examined and the adjusted or multivariate result chosen accordingly.
Mixed Designs
A group factor crossed with a repeated one is handled as in Mixed ANOVA, where the group-by-condition interaction usually carries the hypothesis.
Interactions and Simple Effects
In a factorial layout, a significant interaction is followed by simple effects or planned contrasts, often requested through syntax, because main effects alone may describe no single cell.
Effect Sizes and Intervals
A standardized effect size is reported with contrast estimates and confidence intervals, and the index is named so that readers can compare it with values from earlier studies.
Planning and Reporting
Sample size reasoning and the write-up follow the conventions of Experimental Psychology, including APA 7 style where the outlet requires it.
COMMON EXPERIMENT ISSUES
Mistakes That Weaken an Experimental Analysis
Most errors in an experiment's analysis come from a mismatch between how the data were collected and how they were entered, more than from anything SPSS does.
Within Factor Run as Between Repeated scores from the same participants are stacked and compared as independent groups, which ignores the correlation between a person's scores and can widen the intervals.
Main Effects Over an Interaction A significant main effect is reported and explained while an interaction in the same model shows that the effect differs by condition, so the headline finding is misleading.
Accuracy Near Ceiling Proportion-correct scores close to the maximum are analyzed as if unbounded, which compresses differences between conditions and can create or mask an interaction, so the distribution is checked before the model is chosen.
Condition Codes Mislabeled Condition codes exported from the presentation software are relabeled by hand, and a single swapped code reverses a contrast without any error message, so codes are verified against the stimulus log first.
Unequal Cells Taken as Equal Cell sizes differ after exclusions, and the default sums of squares are accepted without asking how unequal cells change the hypothesis each main-effect test addresses.
Trial Data Averaged Unseen Many trials are averaged to one score per condition without checking trial counts, outliers or response-time skew, which hides variation that the measure depends on.
DELIVERABLES
What the Experiment Analysis Delivers
A collaborator or reviewer can follow the analysis from the coding of each condition to the reason a particular follow-up test was chosen.
Design Coding Sheet Every factor with its levels, whether it varies within or between participants and the variable names used in the file, so the model can be checked against the design.
Data Screening Notes Outlier, missing-cell and trial-count checks with the decision taken on each, recorded before any hypothesis test was inspected, so the screening cannot look selective.
Model Syntax With Comments SPSS syntax for each model, with the within and between factors and each requested contrast commented, so the output can be regenerated and checked line by line.
Interaction Follow-Up Tables Simple effects and planned contrasts with adjusted p-values and confidence intervals, laid out beside a plot of the cell means and their uncertainty.
Effect Size Summary Effect sizes with intervals for each hypothesis, with a note on which index was used and how it compares with values reported in related studies.
Annotated Output Tables Annotated tables for each test, marking the estimate, its interval and the effect size in APA style so that each can be read and reported accurately.
ILLUSTRATIVE EXAMPLE
A Mixed Design With Missing Sessions
An illustration of how a mixed experimental design moves from data screening to a follow-up of its interaction that can be defended.
ILLUSTRATIVE EXAMPLE · EYEWITNESS MEMORY EXPERIMENT RESEARCH · UNITED STATES
A doctoral researcher in psychology compared recognition accuracy for faces after short and long delays between two groups, one given a standard instruction and one given a cautionary instruction before the test.
Delay was varied within participants and instruction between groups, so a mixed design was specified and the group-by-delay interaction was treated as the main test. The complication appeared in the data file: a few participants had missed the second session, and the SPSS repeated measures procedure uses only participants with a score at every level. The analysis was run on complete cases, then repeated in a model that uses every available observation, and both were reported so that a reader could see whether the conclusion depended on the exclusions. Simple effects were planned in advance and adjusted for multiple comparisons, and the order of the stimulus sets was checked.
Outcome: The researcher received the interaction test and simple effects with 95% confidence intervals, the effect sizes, a comparison of the complete-case and all-observations results, the SPSS syntax and the annotated output tables.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Programs Used Alongside SPSS for Experiments
Experimental data come from presentation software and go into an analysis program, so the choice depends on the trial structure and on what the laboratory already uses.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
Statistical Work Within Your Experimental Program
OnlineSPSS analyzes the experiment as it was designed, and the theory behind the hypotheses, the reading of the effects and the conclusions belong to the researcher.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your experiment, manuscript or report. Authorship remains the researcher's.
Your Research Decisions The hypotheses, the theoretical interpretation of the effects, the discussion and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Questions About Experiment Analysis in SPSS
Condition order, missed sessions and departures from a preregistered plan each need a decision in a within-subjects or factorial experiment.
What does the analysis of a psychology experiment in SPSS include?
SPSS analysis of psychology experiments at OnlineSPSS covers the data layout, screening, the model that matches the design, the follow-up of interactions, and the reporting of effects with intervals and effect sizes. How many of these steps your experiment needs is agreed before work starts.
Should the order of conditions be included in the model?
When conditions were counterbalanced, order is usually examined as a between-participants factor or checked in a supplementary model, since an order effect and a condition effect produce the same pattern in the cell means unless order is modeled. Counterbalancing balances order across participants on average, but it does not show whether order mattered. If it did, the finding is reported and its bearing on the main result explained.
Why does SPSS exclude a participant who missed one condition?
The standard repeated measures procedure uses only participants with a score at every level of the within-subject factor, so one missing cell removes that participant from the model. Models that use every available observation, such as a mixed model, avoid this under an assumption about why the data are missing. Which route suits the design is decided in the plan, and exclusions are reported either way.
What if the data call for a different analysis than the preregistered one?
The preregistered analysis is still reported, whatever it shows. A departure, for example a different model because an assumption was untenable, is labeled and justified, and additional analyses are marked as exploratory. Both can appear in the same paper as long as a reader can tell them apart.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the design description, the data file and any output you already have, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
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Describe the
Design You Ran
Which factors were crossed, which of them varied within participants and what was recorded on each trial: those three facts decide the model.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014