Statistical Dissertation Consulting: Analysis That Holds up at Your Defense

Statistical dissertation consulting is expert guidance on planning, running and reporting the quantitative analysis in your dissertation. Its purpose is analysis that answers your research questions and survives committee questioning.

This guide covers what consulting includes at each stage, how to match statistical tests to research questions, the assumption and missing-data checks committees expect, and how to report results in APA style. It also sets out what consulting must never replace.

Where Statistical Consulting Fits

The earlier consulting starts, the cheaper the fixes. A sample too small for the planned model is easy to address at the proposal stage and very hard after data collection.

The Alignment Principle

Every research question needs a hypothesis, defined variables at a known level of measurement and one named analysis. Committees look for this chain more than for sophisticated methods.

When the chain breaks, for example a question about relationships tested with a group comparison, the committee will notice.

Matching Tests to Questions

This table is a starting point, not a rule. Clustered data, such as patients within hospitals, may need multilevel models, and complex designs may need specialist methods.

Sample Size and Power Analysis

An a priori power analysis estimates how many participants you need to detect an effect of a given size. Committees usually expect one in the proposal.

• Choose the test you will actually run.
• Set alpha, conventionally .05, and power, commonly .80.
• Justify the expected effect size from prior studies or a meta-analysis, not convention alone.
• Allow for expected attrition or non-response.
• Report the software used, such as G*Power, and every input.

Avoid post hoc power analysis to explain a non-significant result. Report effect sizes and confidence intervals instead, which show the precision of your estimate.

Assumptions, Outliers and Missing Data

Every parametric test has assumptions, and committees often ask how you checked them. Document each check and what you did about any violation.

• Normality: inspect histograms and Q-Q plots; formal tests are oversensitive in large samples.
• Homogeneity of variance: Levene's test, with Welch's correction where variances differ.
• Linearity and multicollinearity: scatterplots and variance inflation factors in regression.
• Outliers: identify them, check for data entry errors and report how you handled them.
• Independence: confirm the design does not cluster or repeat observations unexpectedly.

Missing Data

Describe how much data are missing and the likely mechanism: missing completely at random, missing at random or missing not at random. Listwise deletion is simple but can bias results; multiple imputation is often preferred when data are missing at random.

Reporting Results in APA Style

APA 7th expects exact p values (to two or three decimals, with p < .001 for very small values), effect sizes and, where possible, confidence intervals. Statistical symbols such as t, F, p and r are italicized.

Notice the italic symbols, the degrees of freedom in brackets and the absence of a leading zero for p, which cannot exceed 1. Report the result first in words, then the statistics, and keep interpretation for the discussion chapter.

Tables and Figures

Use APA-style tables with numbered titles, clear column headings and notes explaining abbreviations. Do not repeat every value from a table in the text; highlight the findings that answer each research question.

Statistical and Practical Significance

A significant p value says an effect is unlikely to be zero under the model's assumptions; it does not say the effect matters. With large samples, trivial differences become significant.

Interpret effect sizes in context, using field-specific benchmarks where they exist rather than generic labels alone. In clinical research, discuss whether the difference would change practice.

Preparing to Defend Your Analysis

Committee members often ask a small set of predictable questions. Prepare clear, short answers to each.

• Why did you choose this test over alternatives?
• How did you check the assumptions, and what did you find?
• How did you handle missing data and outliers?
• Was your sample large enough, and how do you know?
• What do the effect sizes mean in practical terms?
• What are the main statistical limitations?

Quantitative, Qualitative and Mixed Methods

Statistical consulting is for quantitative strands. Qualitative analysis, such as thematic analysis or grounded theory coding, needs a methodologist with qualitative expertise.

In mixed methods dissertations, the statistical consultant can support the quantitative strand and its contribution to integration, while you or a qualitative specialist handle the rest.

What Statistical Consulting Does Not Replace

• Your own data collection: consultants never generate or alter data.
• Your interpretation: you must understand and defend every result.
• Your chair's or committee's guidance on methods.
• Your university's rules: some programs restrict or require disclosure of statistical help.

Keep your syntax or script files and your output. They show your committee exactly what was done and let you rerun any analysis on request.

Common Mistakes to Avoid

• Choosing a test because it is familiar rather than because it fits the question.
• Skipping the power analysis, then finding the sample is too small.
• Running many tests without addressing multiple comparisons, for example with a Bonferroni or Holm correction or a planned set of contrasts.
• Reporting p values without effect sizes.
• Claiming causation from correlational data.
• Letting the discussion interpret results the analysis did not test.

How IvyDrafts Helps with Statistical Dissertation Consulting

Send your research questions, hypotheses, variable list, analysis plan and any output or anonymized data. A statistics consultant checks alignment, power and assumptions, explains test choices and reviews your APA reporting, so you can defend each decision.

We never invent or alter data or results. Your identity is never shared with the consultant, the price is shown before you pay, and free revisions within the scope of your original request are available.