Dissertation Data Analysis and Statistical Support: From Raw Data to a Defensible Results Chapter

ScribeLabWriter analyzes your own collected data and turns it into a results chapter that survives examiner scrutiny. This is not meta-analysis of published studies. This is your dataset, your variables, and your research questions, analyzed by a PhD-qualified statistician who selects the correct test, verifies every assumption, runs the analysis in the software your program requires, and reports the output to APA 7th edition and journal reporting standards.

We work with PhD and professional doctorate candidates (DNP, DBA, EdD), master's and MSc dissertation students, and MSN and nursing capstone students, across the social sciences, health sciences, nursing, education, and business. Every analysis is delivered with the reasoning behind each decision and, where you want it, annotated R, Stata, or SPSS syntax so the work re-runs cleanly when your committee asks for a change. You retain full authorship and intellectual ownership of your work throughout.

• Test selection with written justification matched to your research questions, variable types, and study design, so you can explain in your defense why each test was the correct choice
• Full assumption testing with documented remedies (normality, homogeneity of variance, linearity, multicollinearity, independence, sphericity), not skipped and not hidden
• The analysis executed in your required platform (SPSS, R, Stata, SAS, jamovi, JASP, or Mplus), with your program's preferred output format
• Effect sizes and confidence intervals reported alongside every p-value, as APA 7th edition and journal reviewers now require
• APA-formatted tables and figures built to APA 7 table and figure standards, ready to drop into your chapter
• A written results chapter (Chapter 4) reporting the findings objectively, question by question, integrated with your methodology chapter
• Reproducible, annotated code or syntax (R script, Stata do-file, or SPSS syntax) so the analysis is transparent and re-runnable after supervisor or committee feedback
• A plain-language interpretation walkthrough so you understand and can defend what every statistic means
• Sample size and power justification using G*Power when your proposal or IRB requires an a priori calculation
• Unlimited revisions incorporating your supervisor's and committee's feedback until the chapter is approved

The single most consequential decision in your analysis is which test to run, and it is the decision students most often get wrong. The correct test is not a matter of preference. It follows from four questions: What is your research question asking (a difference between groups, or a relationship between variables)? What is the level of measurement of each variable (nominal, ordinal, interval, or ratio)? How many groups or conditions are involved? And are your observations independent, or are they paired or repeated on the same participants?

Those answers determine whether a parametric test is appropriate, and if its assumptions are not met, which non-parametric alternative to use instead. We build this logic explicitly for your study, document it, and use it to justify your analysis to your committee. The table below shows the core mapping.

If your question involves more than one dependent variable, a covariate to control for, or a factorial design, the analysis extends to MANOVA, ANCOVA, or factorial ANOVA. We identify the correct structure for your specific research questions rather than forcing your data into a familiar test.

Our statisticians work across the full range of quantitative methods used in doctoral and master's research, from foundational tests through advanced modeling:

Descriptive and foundational statistics

Central tendency and dispersion, frequencies, cross-tabulations, distribution diagnostics, and the descriptive tables that open every results chapter.

Group differences

Independent and paired t-tests, one-way and factorial ANOVA, ANCOVA, repeated-measures ANOVA, and MANOVA, with the matched non-parametric tests (Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Friedman) when assumptions are not met.

Relationships and prediction

Pearson and Spearman correlation; simple, multiple, and hierarchical linear regression; and logistic regression for binary, ordinal, and multinomial outcomes, reported with odds ratios.

Advanced and multivariate methods

Structural equation modeling (SEM) and path analysis, with model fit reported against the commonly cited thresholds (CFI and TLI at or above .95, RMSEA at or below .06, SRMR at or below .08; Hu and Bentler, 1999); exploratory and confirmatory factor analysis, including KMO and Bartlett's test of sphericity and factor retention by parallel analysis; mediation and moderation (conditional process analysis) using the PROCESS macro (Hayes); and multilevel and mixed-effects models for nested or repeated data.

Survival analysis

Kaplan-Meier estimation with the log-rank test and Cox proportional-hazards regression for time-to-event outcomes, with the proportional-hazards assumption checked rather than assumed.

Psychometrics and scale validation

Internal consistency (Cronbach's alpha; Cronbach, 1951), reliability and validity analysis, and the item and factor analyses required to defend an instrument.

Effect sizes and power

Cohen's d, eta-squared and partial eta-squared, odds ratios, and R-squared reported alongside significance tests, plus a priori sample size and power analysis in G*Power (Faul, Erdfelder, Lang, and Buchner, 2007) when your proposal or ethics board requires it.

Reporting a t-test or ANOVA without testing its assumptions is one of the fastest routes to a revision request. Every parametric test rests on conditions that have to hold for the result to be trustworthy, and examiners increasingly ask to see that those conditions were checked. Just as important is knowing when a violation actually matters and what to do about it, rather than abandoning the analysis at the first significant Levene's test.

We document every assumption check and every remedy in language you can restate in your defense, so that a question about normality or variance becomes one you are prepared for rather than one that catches you out.

Committees and journals expect the software and its version named in your methods, and some programs require SPSS output specifically. We deliver in the platform your program expects, and we provide reproducible code or syntax so your analysis is transparent and can be re-run after feedback.

If your program mandates a specific platform, tell us in your enquiry and we will match it. If you simply want the most defensible and transparent result, we recommend delivering the analysis with annotated code so every calculation can be reproduced.

An analysis is only as good as its reporting. We format your results to APA 7th edition and to the APA Journal Article Reporting Standards for quantitative research (JARS-Quant), which specify what a results section must contain: the test statistic and degrees of freedom, the exact p-value (reported as p < .001 rather than p = .000), an effect size, and a confidence interval. Reporting a p-value on its own, without an effect size, is one of the most frequently flagged omissions in both dissertation defenses and journal peer review.

A single result reported to standard looks like this: t(58) = 2.89, p = .005, d = 0.75, 95% CI [0.22, 1.28]. Statistical symbols are italicized, leading zeros are used only for values that can exceed one, tables carry no vertical rules, and every table and figure is referred to explicitly in the text. We produce the tables, the figures, and the written narrative that reports each finding objectively, question by question, in the past tense, keeping interpretation for your discussion chapter, exactly the separation examiners look for. This connects directly to the rest of your dissertation chapter support, so your results chapter reads as one continuous argument with your methodology and discussion.

• Thematic analysis following Braun and Clarke's six-phase process (2006, 2022)
• Interpretive phenomenological analysis (IPA)
• Grounded theory coding (open, axial, selective)
• Content analysis with coding framework
• NVivo or MAXQDA project files with coded data
• Theme tables with supporting quotes
• Thematic maps or concept diagrams
• Audit trail documentation for trustworthiness

• Joint display tables showing quantitative and qualitative findings side by side
• Integration narrative connecting both strands to your research questions
• Meta-inference development from merged findings

Data analysis for a DNP scholarly project is not the same as a PhD dissertation, and treating it as though it were is a common and costly error. A DNP project is practice-focused and translational: the goal is to evaluate whether an intervention improved an outcome in a real clinical setting, not to generate generalizable new knowledge. That changes the statistics you need and how they should be reported.

We support the methods that DNP and nursing capstone work actually calls for: pre- and post-intervention comparisons (paired t-tests, McNemar's test for paired categorical data, Wilcoxon signed-rank), run charts and statistical process control (SPC) charts that distinguish real change from ordinary variation over time within the Model for Improvement, and the descriptive and bivariate statistics common in nursing research. We frame the analysis around your PICOT question and your outcome measures, we report it in line with the SQUIRE 2.0 standard for quality improvement (Ogrinc et al., 2015), and we keep the distinction between statistical significance and clinical significance explicit, because your committee will expect you to speak to both. The work aligns with the analytical and outcome-evaluation expectations of the AACN Essentials.

If your project sits in the nursing cluster, this service pairs with our DNP scholarly project support and our wider nursing capstone services, and with our EBP and PICOT support for the evidence framing that precedes the analysis. If your work instead pools findings across already-published studies rather than analyzing your own data, that is a meta-analysis, and it is covered by our meta-analysis service.

If you are still at the proposal or ethics stage, the analysis begins before you collect a single data point. Committees and IRBs expect an a priori power analysis that justifies your sample size: given the effect you expect to detect, the significance level, and the power you want (conventionally .80 or higher), how many participants do you actually need? We run this in G*Power (Faul, Erdfelder, Lang, and Buchner, 2007) and write the justification for your methodology chapter, so your sample size is defensible rather than arbitrary, and so you are not left underpowered when the results come in. This connects naturally with your literature review and proposal work, where the expected effect is first established.

• The wrong test for the data. Applying a parametric test to ordinal data, or comparing three groups with repeated t-tests instead of an ANOVA, is caught immediately by a methodologically literate examiner.
• Unchecked or unreported assumptions. Running the analysis without testing normality, variance, or sphericity, and without documenting what was done when an assumption failed.
• A p-value with no effect size or confidence interval. Reporting significance without magnitude tells the reader nothing about whether the effect matters.
• Misreading a non-significant result. Treating p > .05 as proof that there is no effect, rather than as a failure to detect one.
• Analyzing until something turns significant. Running test after test, or adding participants conditional on the result, inflates the false-positive rate and is a recognized integrity problem.
• An underpowered study. No a priori power calculation, then a non-significant result that cannot be interpreted because the study never had the power to detect the effect.

We build the analysis to avoid each of these, and we tell you where your data have genuine limitations so you can address them candidly in your discussion rather than have an examiner raise them first.

1. Send us your data and your questions

Share your dataset (SPSS, Excel, CSV, or your platform's native file), your research questions or hypotheses, and your methodology chapter or proposal if you have one. If you are pre-data-collection and need a power analysis, send your proposal.

2. We scope and quote

A PhD statistician reviews your questions and data, confirms the correct analytical approach, and returns an itemized quote within 2 to 4 business hours, with no obligation.

3. We run the analysis

Test selection, assumption testing, the analysis itself, effect sizes, and APA-formatted output, delivered with annotated code or syntax where you want it.

4. We write and explain the results

You receive the results chapter, the tables and figures, and a plain-language walkthrough so you understand and can defend every finding. Revisions continue until your supervisor and committee are satisfied.

We provide statistical analysis and interpretation support. You retain full authorship and intellectual ownership of your dissertation or project, and we make sure you understand the analysis well enough to present and defend it as your own work. Our role is to execute the technical analysis correctly and to teach you what it means, not to replace your understanding of your own research.