Overview
Most people learn analysis software under deadline pressure, from video tutorials that show a procedure but not when it is appropriate or how to read the result. We teach the reasoning first: what question the procedure answers, which assumptions it needs, and what each part of the output means. Then participants run it themselves, check it and write up a sentence of results in their field's reporting style.
Courses are available for five tools that cover most academic analysis. SPSS Statistics and Stata for point-and-click and syntax-based quantitative analysis, R for fully scripted and reproducible work, NVivo for qualitative coding and analysis, and SmartPLS for partial least squares structural equation modeling. Participants work with the version their institution licenses; we note differences between releases where they matter.
Training can be one-to-one, a small open group, or a closed group for a lab or department. If you would rather have an analysis delivered with an explanation, our SPSS analysis support and related data-analysis services may suit you better.
Who it’s for and the problems it solves
This is for you if you are…
- Master's and doctoral students who need to analyze their own data and defend the analysis
- Researchers switching tools, for example from SPSS to R for reproducibility or to Stata for panel data
- Qualitative researchers starting a first NVivo project
- Business, marketing and management researchers using SmartPLS for survey-based models
- Research teams who want consistent, documented workflows across members
Common problems we help with
- You can follow a tutorial but cannot tell whether you chose the right test
- Your SPSS output has dozens of tables and you do not know which to report
- You want to learn R but the learning curve has stopped you twice already
- You have imported interviews into NVivo and do not know how to structure codes or queries
- Your SmartPLS model runs, but you are unsure which criteria to report and in what order
What’s included — and what isn’t
Included
- A pre-course skills check and software/version confirmation
- Data preparation: importing, labeling, recoding, handling missing values and documenting changes
- Statistical reasoning alongside procedures: choosing tests, checking assumptions, effect sizes and confidence intervals
- Reproducible workflows: SPSS and Stata syntax or do-files, R scripts and projects, NVivo project logs and memos
- Reporting practice: turning output into tables and results sentences in APA or your field's style
- Exercises on sample datasets or participants' own anonymized data
- Reference sheets and annotated scripts to keep after the course
Not included
- Running participants' thesis or assignment analyses for them during training
- Completing graded statistics coursework or exams
- Software licenses — participants use their own or their institution's
- Accredited or vendor-certified qualifications; our certificates confirm attendance or completion only
What you receive
- Tailored syllabus
- A session plan for the chosen software, methods and level, with learning outcomes and practice datasets.
- Hands-on sessions
- Live, screen-shared or in-lab sessions in which participants run every procedure themselves.
- Annotated scripts and reference sheets
- Commented syntax, do-files or R scripts, NVivo or SmartPLS walkthrough notes, and a decision sheet for choosing procedures.
- Practice tasks with feedback
- Between-session exercises with worked solutions and feedback on participants' attempts.
- Certificate
- Optional certificate of attendance or completion listing topics and hours; not a vendor certification.
How it works
Skills and goals check
A short questionnaire or call on your data, research questions, software access and current skills.
Syllabus
We propose a sequence of sessions and datasets, and agree whether you will practice on your own data.
Training sessions
Interactive sessions with you at the keyboard, plus short practice tasks between sessions.
Consolidation
A final session in which you run a complete mini-analysis unaided, with feedback.
Course tracks and sample outlines
- SPSS Statistics: from data to report (5 sessions)
- Variable view and data cleaning; descriptives and graphs; t-tests, ANOVA and nonparametric alternatives; correlation and regression; reliability (Cronbach's alpha) and reporting. Syntax is pasted and saved from session one.
- R for researchers (6 sessions)
- RStudio projects and scripts; tidyverse data wrangling; ggplot2 graphics; common tests and linear models; mixed or generalized models as needed; reproducible reports with R Markdown or Quarto.
- Stata for applied research (5 sessions)
- Do-files and logs; data management and merging; regression with robust or clustered errors; panel data (xtset, fixed and random effects); tables and graphs for export.
- NVivo for qualitative analysis (4 sessions)
- Project set-up, cases and attributes; coding and codebook development; memos and annotations; queries, matrices and visualizations; keeping an audit trail. We discuss AI-assisted coding features and their limits.
- SmartPLS for PLS-SEM (4 sessions)
- Model drawing and data import; measurement model assessment (loadings, reliability, AVE, HTMT); structural model (paths, R², f², bootstrapping); predictive assessment and reporting order.
- Statistics foundations (optional add-on)
- Distributions, sampling error, hypothesis testing, confidence intervals and effect sizes, taught with simulations so p-values stop being mysterious.
Learning outcomes
Outcomes are adjusted to the track, but every course aims for participants to work independently and explain their analysis to a supervisor or examiner.
- Prepare and document a dataset or qualitative project so someone else could follow every change
- Choose an appropriate procedure for a research question and check its assumptions
- Run the analysis from saved syntax, scripts or a logged project rather than unrecorded clicks
- Identify the parts of the output that answer the research question
- Report results with effect sizes and confidence intervals in the required style
Keeping pace with software versions
Vendors release new versions regularly: IBM SPSS Statistics, Stata (including its StataNow rolling updates), NVivo from Lumivero and SmartPLS 4 all change menus and add features over time. Institutions often lag a version or two behind. We confirm your version before the course and teach the concepts and workflow in a way that transfers across releases, pointing out where menus differ.
Newer AI-assisted features, such as automated coding suggestions in qualitative software, are covered critically: what they can speed up, and why the analyst still needs to review and justify every code.
Training versus delivered analysis
Training is right when you need to own the analysis, answer questions about it and possibly repeat it. If your deadline leaves no time to learn, a delivered analysis with an interpretation walkthrough may be more realistic, and we will say so. Many people combine them: a delivered analysis followed by sessions in which they rerun it themselves from the annotated scripts.
What the work can look like
Illustrative example — fictional data
Illustrative one-to-one R course plan
A doctoral student in public health had used SPSS for a pilot study and wanted to move to R for the main analysis of a longitudinal cohort dataset. The plan below is an invented illustration.
Starting point
Comfortable with regression in SPSS menus, no programming experience. Goal: run and document mixed-effects models on repeated measures and produce publication-quality figures.
Six-session plan
Projects and scripts; importing and reshaping data from wide to long; ggplot2 for trajectories; linear models recap; mixed-effects models with lme4; Quarto report combining code, tables and text.
Consolidation task
Using a synthetic dataset with the same structure as the cohort data, the student built the full analysis script independently and received written feedback on code clarity and model reporting.
Illustrative example only. The participant and plan are fictional.
What you’ll need to provide
- Access to the software and version you will use (licensed SPSS, Stata, NVivo or SmartPLS; R and RStudio are free)
- Your research questions and a description of your data, or permission to use sample datasets
- An anonymized copy of your data if you want to practice on it
- Any required reporting style or program guidelines
- Preferred schedule and format
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Software track and number of sessions
- One-to-one, open group or closed group delivery
- Whether training uses your own data (requiring preparation) or sample datasets
- Level of written feedback on practice tasks
- Online or in-person delivery
Turnaround depends on
- Software access and installation on participants' machines before session one
- Preparation time if we tailor exercises to your dataset
- Scheduling across participants' availability
Every project is quoted individually. How we price our work.
Questions people ask before starting
Which software should I learn?
Usually the one your supervisor, lab or target journals use, subject to what your institution licenses. R is free and fully reproducible but has a steeper start; SPSS is quicker to begin with; Stata is strong for econometrics and panel data. We can advise in a short call.
Can I use my own thesis data in training?
Yes, anonymized. Practicing on your own data is motivating, but you run the procedures and make the decisions. Where we demonstrate, we often use a synthetic dataset with the same structure.
Do I get an official SPSS or NVivo certification?
No. We issue certificates of attendance or completion that describe the topics and hours covered. They are not vendor certifications or accredited qualifications.
I'm a complete beginner in statistics. Is that a problem?
No. Add the statistics foundations sessions first, which cover sampling, hypothesis testing and effect sizes using visual simulations before any software procedures.
Can you train our whole lab?
Yes. Closed-group courses can use your lab's data structure and coding conventions. For department- or faculty-wide programs, see our institutional services.