RESEARCH DISCIPLINES
Statistical Consulting Across Research Disciplines
Academic, Clinical & Professional Research
Statistical consulting for researchers starts from the conventions of the field, from clinical, behavioral and social research to business, biological and sports science.
WHAT YOU WILL FIND
Analysis support in 6 research fields
The designs and measures common in your field
Reporting to your journal's standard
WHAT YOU WILL FIND
Analysis support in 6 research fields
The designs and measures common in your field
Reporting to your journal's standard
Statistical consulting for researchers, organized by research field, is design, analysis and reporting support that starts from the conventions of the study’s discipline: the study designs, units of analysis, outcomes, measurement systems, data structures and reporting standards that differ from one field to the next. A clinical trial, a psychometric survey, an economic panel and a laboratory assay each pose different analytical problems, so the same workflow is never applied to every dataset. OnlineSPSS is a specialist statistical consultancy for academic, clinical and professional research, and the analysis is carried out in-house by PhD-level statisticians.
RESEARCH FIELDS
Statistical Analysis Across Research Fields
Statistical consulting by research field starts from six families of disciplines, each with its own study designs, measurement conventions and reporting expectations. Begin with the closest family and open it to see every field it contains.
01
Medical, Health & Clinical Research
For clinical, medical, epidemiological, and health research
Trials, cohort and case-control studies, registries and diagnostic accuracy work, where outcomes are clinical events, test results or patient-reported measures. Time-to-event endpoints, repeated visits and incomplete follow-up shape the model, and a diagnostic study is judged by its accuracy against a reference standard.
02
Psychology & Behavioral Sciences
For behavioral, psychological, cognitive, and mental health research
Laboratory and online experiments, questionnaire studies and clinical or developmental samples, where constructs are measured by scales and not observed directly. Reliability and factor structure come before group comparisons, and within-subject designs need models that respect the correlation between occasions.
03
Education & Social Sciences
For education, social, policy, and population research
Surveys, administrative and national datasets, program evaluations and mixed-methods projects, where respondents sit inside classrooms, neighborhoods or countries. Multilevel structure, survey weights and panel attrition drive the analysis, and policy questions call for careful wording about association versus effect in the final report.
04
Business, Management & Economics
For organizational, economic, financial, and market research
Panel and time-series data on firms, markets and employees, consumer and organizational surveys, and archival financial records. Unobserved firm-level differences and serial correlation matter in the model, and causal claims need an explicit identification strategy such as instrumental variables or difference-in-differences.
05
Life & Biological Sciences
For biological, biomedical, laboratory, and environmental research
Bench experiments, cell and animal studies, field ecology, microbial assays and sequencing data, where replicates may be technical, biological or both. Dose-response designs, small groups and high-dimensional measurements are common, and only independent biological replicates count toward the sample size.
06
Sports & Exercise Science
For performance, physiology, rehabilitation, and exercise research
Repeated performance and physiological tests, training interventions and crossover or within-athlete designs, often with small samples of trained or elite athletes. Measurement reliability, individual response to training and the smallest worthwhile change matter alongside the group mean, with uncertainty reported as intervals.
RESEARCH CONTEXT
Why Statistical Requirements Differ Across Disciplines
Six factors separate the statistical needs of one field from another, and each is settled with the researcher before a method is named or a model is fitted.
Study Design Trials, cohorts, experiments, surveys and case-control studies answer different questions, and the design decides which comparisons are valid, which observations are independent and what the results can support.
Outcome & Data Type A time-to-event outcome, a psychological scale score, a count of events and a repeated physiological measurement each call for a different model, so the outcome type narrows the method family.
Measurement Biomarkers, clinical endpoints, questionnaires and survey instruments carry different measurement error and different validity questions, which the analysis has to examine and not assume.
Data Structure Patients within hospitals, pupils within schools, repeated visits and firms across years are correlated by design, and the standard errors have to reflect that dependence.
Reporting Standards Standards differ by field: CONSORT 2025 for randomized trials, STROBE for observational studies, PRISMA 2020 for systematic reviews, COREQ for interviews, APA JARS in psychology, and data policies in economics.
Substantive Research Context A model can fit well and still miss the question. Each estimate is read on the field's own scale, such as a clinically important difference or the smallest worthwhile change in performance.
DISCIPLINE & METHOD
How Research Context Shapes the Analysis
Each field returns to a few recurring designs, and each design narrows the choice to a family of methods. The table summarizes research statistics by discipline as a starting point, never as a fixed menu.
| Research field | Common research structures | Common analytical needs |
|---|---|---|
| Medical & Clinical | Trials, cohorts, patient follow-up, diagnostic studies | Survival analysis, longitudinal models, regression, diagnostic accuracy |
| Psychology & Behavioral | Experiments, surveys, psychometric scales | Regression, factor analysis, SEM, reliability, repeated measures |
| Education & Social Sciences | Surveys, nested samples, longitudinal studies | Multilevel models, regression, mixed methods, scale analysis |
| Business & Economics | Cross-sectional, panel, market and organizational data | Regression, panel models, forecasting, SEM |
| Life & Biological Sciences | Experimental, laboratory and biological measurements | Comparative methods, regression, repeated measures, multivariate analysis |
| Sports & Exercise Science | Repeated performance and physiological measurements | Repeated measures, mixed models, reliability, comparative analysis |
STATISTICAL METHODS
Methods Selected for the Research Question and Design
No field owns a method: a mixed model can serve a trial, a classroom study and an athlete-monitoring project alike. What changes is the design, the outcome and how the result must be interpreted, and the method follows from those.
Comparative & Hypothesis Testing
Group, condition and time-point comparisons
Regression & Predictive Modeling
Associations, adjusted effects and prediction
Multivariate, Scale & Reliability
Scales, factors, reliability and agreement
Structural & Latent Variable Modeling
Latent constructs, paths and measurement models
Longitudinal & Multilevel Analysis
Repeated, clustered and nested observations
Clinical, Survival & Epidemiological
Time-to-event, diagnostic and observational designs
Evidence Synthesis & Meta-Research
Pooled effects, heterogeneity and study bias
Bayesian & Computational Methods
Priors, resampling, simulation and missing data
Qualitative & Mixed Methods
Coding, themes and integration of findings
SOFTWARE
Software for Research Analysis
The platform follows the design, the method, the data structure and the outputs a project needs, with syntax or scripts provided where applicable.
ANALYSIS SERVICES
Where OnlineSPSS Supports a Research Project
The research field sets the context of a study, and the six services below name the stages at which OnlineSPSS takes part, from designing the study and analyzing the data to reviewing an analysis already run.
Research Planning & Study Design
Statistical planning before data collection: study design, outcomes and variables, sample size and power, and the Statistical Analysis Plan for protocols, grants and IRB submissions.
Quantitative Data Analysis
Analysis of quantitative research data: preparation, descriptive statistics, hypothesis tests and regression models, with diagnostics, effect estimates and publication-ready results.
Qualitative & Mixed Methods Analysis
Systematic coding and thematic, framework or content analysis, with mixed methods integration and the analytical rigor that each qualitative approach requires.
Results Reporting & Interpretation
Completed analyses turned into structured results: statistical interpretation, tables, figures and reporting standards, presented in a publication-ready form.
Meta-Analysis & Evidence Synthesis
Effect-size synthesis across studies, with heterogeneity assessment, sensitivity and subgroup analysis, meta-regression, risk of bias assessment, evidence tables and forest plots.
Independent Statistical Review
A second statistical look at existing work: method appropriateness, assumptions, calculations and reporting, including reviewer or supervisor statistical comments where within scope.
RESEARCHER VOICE
What Researchers Say
Feedback from two research leads, one working across several research areas and one in health research, on field-specific design, data structure and reporting standards.
“Our team works across several research areas, so we needed statistical support that could adapt to very different study designs rather than apply the same template every time. The advice was consistently grounded in the field, the data structure, and the reporting standard expected for each project. That breadth was the main reason we kept coming back.”
“The main value was having statisticians who understood clinical study design, patient-level data, repeated measures, missing follow-up, and reporting standards. We did not have to explain why those details mattered, and the analysis was structured around the clinical question rather than around the software.”
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Consulting Across Research Fields
Short answers to what researchers ask before sharing a project, from method choice and cross-field work to completed analyses, confidentiality and the assessment process.
Do statistical methods differ between research disciplines?
Many methods are shared across fields, so a regression or a mixed model is not tied to one discipline. What differs is the design, how variables are measured, how the outcome is structured, which reporting standard applies and how a result is interpreted. A trial, a psychological scale study and an economic panel can all use regression and still need different handling.
Do I need to know which statistical method I need before contacting you?
No. The research question, study design, variables and available data are reviewed first, and the method follows from them. If an advisor, a reviewer or a protocol has already suggested a method, we confirm whether it fits. Our Statistical Methods section shows how the families of analysis are chosen.
Can you work with research that combines several disciplines?
Yes. A behavioral intervention with health outcomes, or a business study built on psychometric scales, draws on more than one methodological tradition. A research statistician reviews the design as a whole, so the analysis and the reporting meet the conventions of each field involved.
Can you review statistical analysis that has already been completed?
Yes. An analysis already run, in a thesis chapter, a manuscript or a response to reviewers, can be examined against its design, its assumptions and the reporting standard of the field, whether it came from software defaults or an AI tool. Independent Statistical Review is the service built for that; any changes are agreed with you, and the conclusions remain yours.
How do you protect research data and confidentiality?
Research data and project details are handled confidentially, and the names of researchers and institutions are not used publicly without permission. Project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
Tell us about the research question, the design and the data you have, and a statistically trained Research Coordinator reviews the request and replies the same day. Before any work starts, the scope, deliverables, timeline and a single fixed project fee are agreed, so the timeline follows from that scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Need Statistical
Support in Your Research Field?
Share your research question, study design and available data. We review the analytical requirements in the context of your field and identify an appropriate direction before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014