Descriptive & Inferential Statistics Support: t-Tests, ANOVA, Chi-Square
Help describing your sample properly and choosing, running and reporting the group-comparison and association tests that match your research questions and data.
Quantitative Analysis
Statistical support for survey, experimental and observational data: describing your sample, testing hypotheses, validating scales and modeling relationships, with assumptions checked and effect sizes reported.
Choosing within quantitative analysis
The right quantitative method depends on three things: your research question (comparing groups, predicting an outcome, confirming a measurement structure, or testing a pathway), the measurement level of your variables, and the structure of your data (independent cases, repeated measures or nested groups). Group comparisons and simple associations belong under descriptive and inferential statistics; predicting an outcome from several variables calls for regression; checking that questionnaire items measure intended constructs calls for factor analysis.
When your model links several constructs at once, structural equation modeling tests the whole framework; when you want to know how or when an effect occurs, mediation and moderation analysis is the focus; and when participants are grouped in classes, clinics or teams, or measured repeatedly, multilevel modeling accounts for that structure. Many projects combine two or three of these, for example factor analysis followed by SEM.
If you…
- If you need to compare groups or test simple associationsDescriptive & inferential statistics
- If you want to predict an outcome from several variablesRegression analysis
- If you need to show your questionnaire scales are valid and reliableFactor analysis (EFA & CFA)
- If you are testing a whole conceptual model with latent constructsStructural equation modeling
- If your hypotheses involve mediators, moderators or bothMediation & moderation
- If your data are nested or measured repeatedly over timeMultilevel modeling
Services in this area
Regression Analysis Support: Linear, Logistic, Ordinal, Count and Hierarchical Models
Support choosing the right regression model for your outcome, checking its assumptions, interpreting coefficients correctly and reporting results that reviewers can follow.
Exploratory & Confirmatory Factor Analysis (EFA and CFA) Support
Support exploring the structure of a new or adapted scale with EFA, confirming an established structure with CFA, and reporting reliability and validity evidence properly.
Structural Equation Modeling (CB-SEM & PLS-SEM) Support
Support choosing between covariance-based and PLS-SEM, validating your measurement model, estimating the structural paths and reporting results your committee or reviewers can follow.
Mediation & Moderation Analysis with the PROCESS Macro and Beyond
Support specifying, running and interpreting mediation, moderation and conditional process models, including choosing the right PROCESS model number and reporting indirect effects correctly.
Multilevel & Hierarchical Linear Modeling (HLM, Mixed Models) Support
Support building multilevel (mixed-effects) models for nested and longitudinal data, from the intraclass correlation to random slopes, cross-level interactions and growth curves.
Not sure which service fits?
Tell us what you’re working on and where you’re stuck. We’ll recommend the right kind of help and send a quote.