The statistics guide
Clear, practical answers to the statistical questions that come up while you analyse and write up your dissertation or thesis, grouped by the stage you have reached, from designing the study to reporting it correctly.
These guides map the whole arc of a quantitative dissertation, from the design choices that shape the analysis to the regression and modelling methods, the meaning of the numbers, and the final write-up. Work through a cluster in order, or jump to the question in front of you. When you would rather hand the analysis over, our analysis help for your results chapter covers the full write-up, the free statistics calculators handle the quick numbers in the browser, and one-to-one consulting sessions talk the plan through with you before you commit to it.
Research design and variables
The decisions that come before any analysis: the type of study, what each variable does, and the framework that holds the design together.
June 17, 2026 · 7 minQuantitative vs qualitative researchNumbers or words, measuring or understanding: here is how quantitative and qualitative research differ, and how to choose the right one for your question.Read article June 11, 2026 · 6 minDescriptive vs inferential statisticsDescriptives summarise your sample; inferentials generalise to the population. Here is what each covers and why a results chapter needs both.Read article June 15, 2026 · 9 minIndependent vs dependent variables, explainedThe independent variable is what you change; the dependent variable is what you measure. Here is how to tell them apart and why it matters.Read article June 15, 2026 · 6 minCorrelation vs causation in researchA strong correlation is not proof of cause. Here are the alternative explanations to rule out and how to phrase your findings safely.Read article June 14, 2026 · 7 minHow to build a conceptual frameworkA conceptual framework is the study-level map of how your variables connect. Here is what it contains, how it differs from a theoretical framework, and how to write one.Read article June 15, 2026 · 7 minWhat is a theoretical framework?A theoretical framework grounds your study in an established theory that explains why your expected relationships should exist. Here is how to choose and write one.Read article
Choosing and running the right test
Matching the test to your question and data, then running the group-comparison procedures a dissertation relies on most.
June 16, 2026 · 7 minHow to choose the right statistical test for your dissertationMatch your research question and variable types to the correct test, with a decision table for the analyses most dissertations need.Read article June 16, 2026 · 6 minParametric vs nonparametric testsParametric tests assume a normal distribution; nonparametric tests do not. Here is how to check the assumptions and choose the right family for your data.Read article June 14, 2026 · 6 minANOVA vs t-test: when to use whichA t-test compares two means; ANOVA compares three or more at once. Here is when to use each test and why several t-tests inflate your error rate.Read article June 16, 2026 · 7 minANCOVA: analysis of covariance explainedANCOVA is an ANOVA with a regression adjustment that controls for a covariate, sharpening the comparison between groups. Here is when and how to use it.Read article June 17, 2026 · 7 minMANOVA: multivariate analysis of varianceMANOVA analyses several outcomes jointly to find group differences in the overall pattern, controlling the error rate a stack of separate ANOVAs would inflate.Read article June 16, 2026 · 7 minRepeated measures ANOVA explainedWhen the same people are measured in every condition, a repeated measures ANOVA removes individual differences from the error term. Here is how it works.Read article
Regression and modelling
Relationships, prediction, and latent structure: the regression and modelling methods that go beyond simple group differences.
June 10, 2026 · 7 minLinear regression assumptions, checkedA regression always prints numbers, even when its assumptions are violated. Here is how to check linearity, independence, equal variance, and normal residuals before you interpret a coefficient.Read article June 7, 2026 · 7 minInterpreting logistic regression resultsOdds ratios above 1 raise the odds, below 1 lower them. Here is how to read logistic regression output and judge whether the model fits.Read article June 7, 2026 · 7 minMediation vs moderation analysisA mediator carries an effect; a moderator changes its size. Here is how to tell mediation and moderation apart and model each one correctly.Read article June 13, 2026 · 8 minStructural equation modeling explainedSEM tests an entire theory as one system, correcting for measurement error and modelling many relationships at once. Here is how it works and when to use it.Read article June 6, 2026 · 8 minCronbach's alpha and factor analysisFactor analysis reveals a scale's hidden dimensions; Cronbach's alpha tests their reliability. Here is how to run and read both for a dissertation survey.Read article
Inference and significance
What the numbers actually mean: p-values, confidence intervals, effect sizes, and the power that decides whether a study can detect them.
June 12, 2026 · 6 minUnderstanding p-values and significanceA p-value tells you how surprising your data are if nothing is going on, not how big or how important the effect is. Here is how to read and report it.Read article June 11, 2026 · 6 minHow to interpret a confidence intervalA confidence interval reports your best estimate and the uncertainty around it. Here is what the level really means and how to read the bounds correctly.Read article June 10, 2026 · 6 minEffect size explained, and why it mattersA p-value says whether an effect is real; the effect size says how much it matters. Here is what each metric covers and how to report it in your thesis.Read article June 8, 2026 · 6 minWhat is statistical power?Statistical power is your chance of detecting a real effect when one exists. Here is what it protects against and the levers that move it.Read article June 8, 2026 · 7 minPower analysis and sample sizeA power analysis turns a guessed sample size into a justified one. Here are the four inputs it needs and how to size a finite population.Read article
Working with data and software
Getting a dataset ready and reading what the software returns, in SPSS, R, or Stata.
June 9, 2026 · 7 minCleaning your data in SPSSCleaning your dissertation dataset in SPSS means checking variables, fixing codes, screening outliers, and handling gaps before you run a single test.Read article June 9, 2026 · 7 minHandling missing data in your dissertationGaps in your dataset distort every analysis unless handled properly. Here is how to classify why data is missing, choose a method, and document it for your thesis.Read article June 13, 2026 · 6 minHow to test for normality (and what to do when data is not normal)Check normality with the Shapiro-Wilk test, a Q-Q plot, and skewness together, and learn the legitimate routes when your data is not normal.Read article June 12, 2026 · 7 minHow to interpret your SPSS outputSPSS gives you a stack of tables but only a few cells answer your question. Here is how to read each box and turn it into supervisor-ready prose.Read article June 6, 2026 · 7 minSPSS vs R vs Stata for your dissertationSPSS is fastest to learn, R is the most flexible and reproducible, Stata suits panel data. Here is how to pick the right one for your thesis.Read article
Running your analysis in SPSS
Step-by-step tutorials for the procedures a dissertation runs most, from getting oriented in SPSS to the menu path, syntax, output, and reporting for each test.
June 15, 2026 · 9 minHow to use SPSS: a beginner's orientationGet oriented in SPSS: Data View versus Variable View, measurement levels, importing and labelling data, and why pasting syntax makes your analysis reproducible.Read article June 16, 2026 · 9 minHow to run a t-test in SPSSRun an independent, paired, or one-sample t-test in SPSS, read the Levene's and Welch rows correctly, and report it with an effect size.Read article June 17, 2026 · 9 minHow to run a one-way ANOVA in SPSSRun a one-way ANOVA in SPSS, choose Tukey or Games-Howell by your Levene's result, find an effect size the dialog hides, and report it cleanly.Read article June 19, 2026 · 9 minHow to run a two-way ANOVA in SPSSRun a two-way ANOVA in SPSS, read the interaction before the main effects, decompose it with simple effects, and report each result with partial eta squared.Read article June 20, 2026 · 9 minHow to run a chi-square test in SPSSRun a chi-square test of independence in SPSS, check the expected-count assumption, pick Pearson or Fisher's exact, and report it with an effect size.Read article June 21, 2026 · 8 minHow to run a correlation in SPSSRun a Pearson or Spearman correlation in SPSS, check linearity with a scatterplot, control a confound with partial correlation, and report r correctly.Read article June 23, 2026 · 10 minHow to run a multiple regression in SPSSRun a multiple regression in SPSS, check every assumption with the diagnostics SPSS provides, watch for multicollinearity, and report B, Beta, and R squared.Read article June 24, 2026 · 10 minHow to run a logistic regression in SPSSRun a binary logistic regression in SPSS, set the outcome and reference categories correctly, check model fit, and report Exp(B) odds ratios with intervals.Read article June 25, 2026 · 10 minHow to run a factor analysis in SPSSRun an exploratory factor analysis in SPSS, check factorability, pick extraction and rotation, retain factors with parallel analysis, and read the pattern matrix.Read article
Running your analysis in R
Step-by-step tutorials for the procedures a dissertation runs most in R, from the model code and assumption checks to the effect size and the reported result for each test.
June 23, 2026 · 10 minHow to run a one-way ANOVA in RFit a one-way ANOVA in R with aov(), check the assumptions, pick Tukey or Games-Howell, add the effect size R hides, and report it cleanly.Read article June 24, 2026 · 10 minHow to run a two-way ANOVA in RFit a two-way ANOVA in R, read the interaction first, use Type III sums of squares when cells are unbalanced, and break it down with emmeans.Read article June 25, 2026 · 10 minHow to run a repeated measures ANOVA in RRun a repeated measures ANOVA in R with afex: reshape to long format, check Mauchly's test, apply the Greenhouse-Geisser correction, and follow up with emmeans.Read article June 22, 2026 · 10 minHow to run a linear regression in RFit linear and multiple regression in R with lm(), check the assumptions with plot(model), test VIF for multicollinearity, compare models, and report it cleanly.Read article June 21, 2026 · 10 minHow to run a logistic regression in RFit logistic regression in R with glm(), convert log-odds to odds ratios with exp(), check fit and AUC, watch for separation, and report the odds ratios.Read article June 26, 2026 · 11 minHow to fit a mixed effects model in RFit a mixed effects model in R with lme4 lmer(): choose random intercepts or slopes, get p-values with lmerTest, check the ICC, and report it cleanly.Read article June 27, 2026 · 10 minHow to run a survival analysis in RRun survival analysis in R: code censoring with Surv(), plot Kaplan-Meier curves, run the log-rank test, fit a Cox model, and check proportional hazards.Read article
Measurement and qualitative analysis
Turning scales and non-numeric responses into analysable, defensible findings.
June 5, 2026 · 6 minAnalysing Likert-scale survey dataTreat single Likert items as ordinal and validated scales as interval. Here is how that choice sets your test and how to report the results.Read article June 5, 2026 · 7 minQualitative data analysis methodsThematic, content, grounded theory, framework, and discourse analysis explained, with the shared coding steps and how to pick the right one for your thesis.Read article June 17, 2026 · 8 minThematic analysis: how to do it properlyA working guide to thematic analysis: the difference between a code and a theme, the six phases, the three types, and how to make your analysis defensible.Read article
Reporting your results
Writing the methods and results sections and presenting every statistic in the format your committee and journal expect.
June 20, 2026 · 9 minHow to write the methods section of a research paperWrite a reproducible methods section: participants, measures, procedure, and a correct data analysis plan, with a worked example and the common mistakes to avoid.Read article June 28, 2026 · 9 minHow to write the results section of a research paperWrite a clean results section: order it by research question, report each statistic with an effect size, use tables well, and keep interpretation for the discussion.Read article June 14, 2026 · 7 minHow to write your dissertation results chapterStructure your results chapter by research question, report descriptives then inferential tests, and keep findings separate from interpretation. Here is how.Read article June 13, 2026 · 7 minHow to report statistical results in APA styleThe fixed order, the leading-zero rule, and copy-ready templates for t-tests, ANOVA, correlation, chi-square, and regression.Read article