Data Analysis
Wherever your project currently stands — a raw, unfiled dataset or a finished analysis you're not sure how to interpret — our statisticians can pick it up from there. We handle statistical data analysis end to end, or step in at just the stage where you're stuck.
Statistical Data Analysis — From Raw Numbers to Meaningful Results
Statistical analysis is only as reliable as the process behind it. Skip data cleaning and a regression model can be thrown off by a handful of bad entries; pick the wrong statistical analysis method for your research design and even a perfectly clean dataset will produce results that don't answer your actual question. We treat data analysis as a full pipeline — not just the moment a test gets run — so that what you receive at the end is defensible from the ground up.
The Data Analysis Process
Data collection
- Where needed, we help generate the data itself — designing and running written or phone surveys, structuring experiments, or working with existing process/operational data you already have on hand.
- Data preparation
- Before any statistical analysis begins, we clean, check, and structure the dataset — flagging missing values, inconsistent coding, and outliers that would otherwise distort results.
- Method selection
- We match the analysis to your data and your research question — choosing from statistical analysis methods such as regression, ANOVA, correlation, or factor analysis rather than defaulting to one approach.
- Statistical analysis
- The core analysis is carried out in SPSS, R, Stata, SAS, Python, Excel, Jamovi, SmartPLS, etc., whichever best fits your dataset, discipline, and required output format.
- Questionnaire analysis
- End-to-end support for survey-based data, from manual data entry and coding through full questionnaire evaluation.
- Interpretation & Reporting
- We don't hand back raw output — every statistical data analysis comes with a written interpretation explaining what the results mean for your original question.
- Meta-analysis
- For projects synthesizing existing research, we conduct meta-analyses from literature search through effect-size calculation and full reporting.
Statistical Methods we Apply
Our statistical analysis work spans the full range of standard methods, matched to whether your project is exploratory (generating hypotheses) or confirmatory (testing them):
- Regression analysis — linear, multiple, and logistic regression
- Variance analysis — ANOVA and repeated-measures ANOVA
- Correlation & correlational analysis — measuring relationships between variables
- Factor & factorial analysis — identifying underlying structure in multivariate data
- Path analysis — modeling hypothesized causal pathways
- Descriptive & inferential statistics — from summary statistics through hypothesis testing
- Multivariate statistics — analyzing several outcome variables together
- Power analysis — determining the sample size needed to detect a real effect
A quick distinction that shapes which of these we use: if your study is meant to generate hypotheses, the analysis typically centers on descriptive statistics and data aggregation. If it's meant to test a hypothesis you've already formed, inferential statistics — the regression, ANOVA, and correlation methods above — take the lead instead.
Software we use
We run statistical data analysis in whichever program fits your project and your institution's or industry's expectations:
- SPSS — the standard for most academic and social-science research; covers regression, ANOVA, factor analysis, and descriptive/inferential statistics
- R — for custom statistical programming and reproducible analysis pipelines
- Stata — common in econometrics, epidemiology, and panel-data research
- SAS — for large-scale, regulated, or clinical/pharmaceutical data
- RapidMiner — for data mining and predictive-analytics workflows
SPSS Analysis, Specifically
Since SPSS analysis is the most frequent request we get on this page, here's what it typically covers when we run it for you:
- Descriptive and inferential analysis
- Linear and multiple regression analysis
- Logistic regression analysis
- Analysis of variance (ANOVA), including repeated-measures designs
- Correlation and factorial analysis
If you need something narrower than a full data analysis project, such as targeted SPSS help on a single test or output, that's available as a standalone service too.
Who This Page Is For
- Researchers with a completed dataset who need it analyzed and interpreted
- Businesses with survey, operational, or customer data that needs statistical treatment
- Students and PhD candidates with a data chapter that needs full analysis support
- Teams running a meta-analysis and needing structured literature synthesis
Your Personal Consultant
Angela Kasemi (Statistician & Academic Consultant)
+44 7848 117 104
excellentstatistics@outlook.com
Request a free consultation for your project right away, and receive a free non-binding quote.
Send us your data, we'll tell you how we can support you
Not sure whether your dataset needs cleaning, a specific statistical analysis method, or is already ready for interpretation? Send it over and we'll assess it as part of your free quote.