Statistics dissertation help & statistical analysis services
Statistical consulting services
Choose the area closest to your project, from analysis planning and data preparation to advanced models and results chapters. Scope, timing and price are agreed in writing before any work starts.
Request a project estimateMost quantitative chapters stall on the same questions: does the method suit the design, are the assumptions met and what do the estimates actually say? We work through those questions with you, starting from your approved proposal rather than a software menu.
Support can be coaching, an independent review of work you have done or analysis your program permits. Every engagement ends with code, a decision log and an explanation session, so you can defend each choice yourself.
Approved proposal or research questions, your institution’s rules on outside help, a de-identified dataset, codebook and any committee comments.
Raw exports rarely arrive analysis-ready. Headers need fixing, scales need scoring, duplicates and impossible values need finding and missing responses need a deliberate strategy. Each step is scripted so the whole pipeline can be rerun.
You receive a documented dataset, a codebook and a log explaining every exclusion, recode and imputation choice. That record is what lets a supervisor or examiner trust the numbers that follow.
The raw export or source files, the instrument or data dictionary, a note of known problems and which dataset version is authoritative.
Model choice follows the outcome and the structure of your observations: linear for continuous outcomes, logistic or count models for binary and count data, and mixed models when people sit inside schools, clinics or repeated waves.
Diagnostics cover linearity, variance, influence and multicollinearity. Interpretation states units, adjustment sets and intervals, and keeps associations separate from causal claims your design cannot support.
The outcome and predictors, how categories are coded, any nesting or repeated measures, the planned models and existing scripts or output.
Comparing groups starts with the design: independent or repeated measurements, one factor or several, covariates or none. That decides between one-way, factorial, repeated-measures, mixed-design ANOVA, ANCOVA or a regression framing.
For interventions and quasi-experiments, we discuss baseline adjustment, difference-in-differences and propensity score approaches, and report effect sizes and planned contrasts rather than a single omnibus p value.
Group definitions, outcomes, the timing of measurements, covariates and the hypotheses or contrasts you planned in advance.
A structural model is only as strong as its measurement model. We test factor structure first, check identification and choose an estimator that suits ordinal or non-normal items before estimating structural paths.
Mediation and moderation questions get bootstrapped indirect effects, simple slopes and plots. Fit is judged across several indices, and any modification is justified by theory and reported in full.
A path diagram, the hypotheses behind each arrow, scale items, sample size and the software you or your committee prefer.
Survey projects need clean exports, quality screening, consistent coding and correctly scored scales before any test is run. Single Likert items and multi-item scale scores are handled differently, and the reasoning is written down.
Reliability is checked in your sample with alpha or omega, validity with factor analysis, and weights or design features are applied when the sampling plan calls for them. Results are reported with bases and response details.
The questionnaire, export file, codebook, reverse-scored items, sampling method and response information.
Test selection follows the hypothesis, the measurement level and whether observations are independent, paired or clustered. Where assumptions fail, robust or nonparametric alternatives are chosen and explained.
Interpretation focuses on effect sizes and confidence intervals as well as p values, so you can describe what a result means in practice and what it cannot establish.
Your exact hypotheses, variable types, study design and any output or code you want reviewed.
Planning before data collection prevents the most expensive problems. We work through effect size justification, design effects for clustered samples, attrition and the exact test your analysis plan will use.
Outputs include a written calculation with every input and source, a sensitivity table and wording suitable for an ethics application, grant proposal or methods chapter.
Your research question, design, primary outcome, planned analysis and any prior evidence about likely effect sizes.
We work in the software your committee expects, including R, Stata, SPSS, SAS, Python, JASP and jamovi. Help can be a code review, debugging a stubborn error, converting an analysis between packages or building reproducible reports.
For coursework, support is tutoring and explanation that follows your course’s academic integrity rules. We teach the reasoning so you can complete assessed work yourself.
Software and version, scripts or syntax, the full error message, a small example dataset and any course rules on outside help.
Doctoral projects bring longer timelines, more complex models and closer methodological scrutiny. Support focuses on justifying choices, comparing alternatives and preparing for examiner questions.
We regularly support nursing, psychology, public health, education, business and social science projects, as well as DNP, EdD and DBA programs. Field fit is confirmed before any work begins.
Your field, degree level, project stage, design summary and any supervisor or reviewer feedback.
Each estimate reflects the number of research questions, the condition of the data, model complexity, the explanation you need and your timeline. Send those details and you will receive a written scope before paying. Requested dates are confirmed, not assumed.
Request a statistical analysis quoteChoose the support that matches your research questions, study design and dataset. Our service directory covers dissertation statistics, data preparation, ANOVA, regression, questionnaires, hypothesis testing and interpretation. Confirm the scope and software before placing an order.
Compare SPSS data analysis pricing or contact the team about your project. External support must follow your university’s academic-integrity rules.
