Statistics Training and Analysis Review (SPSS, R, AMOS, SmartPLS)
Learn the right tests on your own data, run them yourself, and get your output and interpretation checked.
Most scholars reach the analysis after a year or more of data collection, and then everyone wants results quickly. The problems start from this hurry. Somebody runs a t-test on data which is clearly not suitable for it. A regression goes into the chapter and nobody checked its assumptions. An AMOS structural model is shown without any fit indices. Or the chapter has a table of p-values with no effect sizes and no simple reading of them. Examiners catch these things, and mostly they raise them in the viva-voce.
We teach you the analysis that your objectives and your data actually need, in SPSS, R, AMOS or SmartPLS, in one-to-one sessions on your own data file. We tell you which test fits each objective and why. We walk you through screening the data and checking the assumptions, step by step. You run the tests and models on your own laptop. Then we check your output, your syntax and your reading of the results, and explain anything unclear in simple words you can repeat. Because you write and save the syntax or script yourself, your guide or the examiner can rerun any number and get the same value.
Whatever the data show, that only goes into the report. If one of your hypotheses is not supported, it is also a finding, and we help you report and discuss about it properly. Your data file stays as you collected it. The tests are the ones written in the analysis plan, and nobody keeps trying tests to get p < 0.05.
Quick answers
- What do I get from the statistics training and review?
One-to-one sessions in SPSS, R, AMOS or SmartPLS on your own data file, advice on which test fits each objective, and walkthroughs of the assumption checks. You run the analysis and save the syntax, script or model file behind each table, so the numbers can be rerun exactly. We then check your output, effect sizes, data log and interpretation, and review the results chapter text you write.
- How long does it take?
If the data are clean and complete, the analysis plan and test selection needs 2–4 working days. Sessions and review for descriptive and inferential analysis need 1–2 weeks. For structural equation modelling it is 1–3 weeks, and the model and your own pace decide where in that range you fall. Each stage is listed in the table further down.
- Do you run the analysis for me?
No. You run the tests yourself, on your own laptop, and we teach, advise and check. In the viva the examiner asks why this test was chosen, and the answer has to come from you. Our ethics policy explains where we draw the line.
- I have already run the analysis. Can you just check it?
Yes, we can. Mostly the output review takes 3–5 working days. We look at which tests you chose, how you checked assumptions, and how you reported and interpreted the results. We mark wherever an examiner may raise a question.
- What do you need to quote?
Share your data file, your questionnaire or codebook and your objectives at a free consultation. The written quote then fixes the price and the revision rounds for every stage. If the data need far more preparation than expected, we stop and agree any change with you first.
Scope
What’s included
Every stage can be taken separately. If your analysis is finished and you only want a check before submitting to the university, the output review alone is mostly enough.
Analysis plan and test selection
For each objective and hypothesis we advise a suitable test or model and explain why it fits. The choice depends on your design, the type of variables, their level of measurement and your sample size. You write it down in the plan before running anything.
Data screening and preparation
In a session on your own file we show you how to do the coding and labelling, read missing-data patterns, check for outliers and decide how to handle them. You recode the reverse-scored items and compute composite scores yourself. Each step sits in your syntax, so you can see it and also undo it, and we check that syntax afterwards.
Descriptive, inferential and multivariate analysis training
Sessions start with descriptives, reliability and group comparisons. They go on to correlation, regression, ANOVA, MANOVA and factor analysis. Where your data need them, we teach the non-parametric alternatives also. You run each test and we go through the output with you.
Structural equation modelling training
We train you on CB-SEM in AMOS or in R (lavaan), or PLS-SEM in SmartPLS. Sessions cover assessing the measurement model, validity and reliability, testing the structural model, and mediation and moderation. You build and run the model, and we check that fit or quality criteria are reported the way your field expects.
Interpretation and results chapter review
We check the tables and figures you prepare against APA or the style your department follows, and see that effect sizes and confidence intervals sit beside the p-values. Then we read the results chapter text you write and mark where the interpretation says more, or less, than the output shows.
Output review
Have you run the analysis yourself already? Then we go through your output files and check the test choices, assumption checks, reporting and interpretation. Anything an examiner may question gets flagged.
Quality
How quality is verified
These are the checks every piece of work goes through before it reaches you.
Your syntax or script for every result
You keep the SPSS syntax, R script, AMOS model file or SmartPLS project behind every table, and we check that it reruns. Run it on your data file and the reported numbers come out exactly.
Assumptions checked and reported
We walk you through testing normality, homogeneity of variance, linearity, multicollinearity and independence, plus whatever the particular model needs, and check how you report them. If some assumption fails, we explain the remedies and you note down the one you used.
Effect sizes alongside p-values
Each inferential result carries a suitable effect size, and a confidence interval wherever possible. The reader then sees how much the result matters in practice, and not only if it is significant.
Data changes logged
If you recode, exclude or impute anything, the reason goes into a data log you keep, and we check the log against your syntax. Your original data file is never changed.
Second-analyst check
Before our review comes back to you, one more analyst goes through your test choice, output and interpretation. That person asks what an examiner would ask, before the examiner does.
Time
Realistic timelines
Time depends on the condition of your data and the number of objectives more than on software. These ranges are for clean, complete data that reach us at the start.
| Stage | Typical time | What it depends on |
|---|---|---|
| Analysis plan and test selection | 2–4 working days | Number of objectives and hypotheses |
| Data screening and preparation | 2–7 working days | Sample size, missing data and how the file was coded |
| Descriptive and inferential analysis training | 1–2 weeks | Number of variables and tests, and your own pace |
| Structural equation modelling training | 1–3 weeks | Model complexity, mediation or moderation, and measurement issues |
| Interpretation and results chapter review | 1–2 weeks | Number of tables and how many drafts you send |
| Output review | 3–5 working days | Size of the output and number of analyses |
Software
SPSS, R, AMOS or SmartPLS — which one?
In the viva mostly the questions are on your tests, and hardly anyone asks about software. Still the four tools work differently, so know that before starting.
SPSS
SPSS works through menus, and Indian universities teach it widely, so supervisors find it easy to follow. It covers most of the things a thesis need. But menu clicks are not recorded by themselves, so in the sessions we teach you to paste and save the syntax for every step. That syntax is what makes your analysis repeatable.
R
R is free and works with scripts. It is very flexible, and there are packages for nearly every method, like lavaan for SEM, lme4 for multilevel models and psych for scale analysis. Since everything is a script, rerunning it is built in. Learning R is harder, though, so we go through your script line by line with you and you add your own comments.
AMOS and SmartPLS
Both do structural equation modelling, but for different purposes. AMOS does covariance-based SEM. It is suited for confirming theory that is already well established, and it gives fit indices such as CFI, TLI and RMSEA. SmartPLS does partial least squares SEM. Scholars many times choose it for prediction-oriented or exploratory models, and for formative constructs. It judges the model on criteria such as composite reliability, AVE and HTMT, and not on global fit. Pick as per your research aim, as examiners do ask why AMOS and not SmartPLS.
What we need from you
We need your data file (Excel, CSV, SPSS or similar), your questionnaire or codebook, your objectives and hypotheses, and any analysis your supervisor has already asked for. If the design is still not settled (sampling, instruments, sample size), better to sort that out first through research methodology consulting. If your data come from interviews and focus groups, look at qualitative and mixed-methods analysis.
Pricing
How pricing works
We give the quote only after seeing your data file, objectives and instrument. A descriptive study from one survey is a very different job from a multi-group SEM with mediation. After we agree it in writing the quote stays fixed for the stages you choose, and then work starts.
Sometimes the full data need much more preparation than the sample you sent us suggested. Then we inform you before continuing. The price will change only if you agree to it.
What the quote depends on
- How many objectives, hypotheses and variables you have
- Sample size, and what condition the data file is in
- Techniques you need, for example SEM, multilevel or longitudinal models
- Which software you will work in
- Whether you want the results chapter review also
Every quote includes
- A written scope listing exactly what will be delivered
- A fixed price agreed before any payment
- Payment in stages, tied to delivery
- Revision rounds stated in the scope
Process
How it works
Free consultation
A call or message with a specialist in your subject. We review your material and say plainly whether we are the right fit.
Written scope and quote
You get a written scope, a timeline and a fixed price. Work and payment begin once you agree to it.
Staged delivery
Work arrives in stages you review, each with a similarity report where text is involved. You pay stage by stage.
Revisions and hand-over
Revision rounds are included. At the end you get the final files, and an explanation of anything you’ll need to defend.
Keep reading
Guides and related services
Choosing the right statistical test
Match your question and data type to the right test, including non-parametric options.
Research Methodology Consulting
Design, sampling, instruments and analysis plan checked before you collect data, while changes are still easy.
Qualitative and Mixed-Methods Analysis Guidance
Review and guidance while you code, build themes and join the strands, with an audit trail your examiner can follow.
APA 7 formatting guide for theses
In-text citations, reference examples, headings, tables and figures — the APA 7 rules scholars get wrong.
FAQ
Frequently asked questions
Can you just make my results significant?
The results have to reflect your data as you collected them. Suppose somebody edits the values in your file or removes respondents with no reason given in advance. That comes under falsification or p-hacking, and so does running tests till one “works”. We can check if the tests you picked are the right ones. Sometimes a genuine mistake in the analysis hides a real effect. If there is still no effect, we help you report the non-significant result properly.
Do I need to know SPSS or R before the sessions?
No. Many scholars come with only the basics, and some with none. We start from opening your data file and saving syntax, and go at your pace. If you are learning R from zero, plan for one or two extra sessions in the beginning.
My data aren’t normal. Is that a problem for my study?
Mostly it is not. When the sample is of reasonable size, moderate non-normality does not disturb many tests much. If they don’t, non-parametric alternatives are there, or transformations, or bootstrapped estimates. The examiner wants to see that you checked, made a sensible choice and reported what you did.
My supervisor wants SPSS, but I’ve heard R is better. Which should I use?
Go with the software your supervisor and examiners can follow. The only exception is a method SPSS cannot do. For standard tests both of them give identical answers. We can train you in SPSS or R, or in both.
Can you collect the data or fill in responses for me?
No. Collecting data is your part of the work. Making up responses is fabrication, and filling blanks in somebody’s half-done form is also fabrication. We can help design the instrument and plan the sample, and once you collect the data, we show you how to prepare it.
What if the examiner asks about my analysis at the viva?
By then you will have your own full set of scripts, a data log and the written reasoning behind every test choice. You ran every test yourself and went through each one with us. For more practice we can ask you examiner-style questions on your methods and results.
Is my data kept confidential?
Yes. Only the trainer and reviewer on your project open the data file. It is used for your project only, and after hand-over we delete it if you ask. Before sending, please remove names and other direct identifiers if you can, and we can tell you how to do that. If you want an NDA, we will sign one.
Talk it through with a specialist for free
Send us where you are and what you need. We’ll reply within one working day with clear advice, a written scope and a fixed quote. No obligation.
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