SmartPLS Analysis
Advanced PLS-SEM: measurement model, structural model, bootstrapping and mediation.
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Reliable analysis, accurate results — from raw responses to a defensible results chapter.
SPSS will run almost any test you ask it for, including the wrong one. It will not warn you that your data violates the assumptions, and it will not tell you that your significant result is an artefact of how you coded a variable.
That is really the whole case for having someone experienced run your analysis. The software is not the hard part — the hard part is choosing the right test for your design, checking that the data actually satisfies what that test assumes, and then reading the output correctly. A regression table is easy to produce and easy to misread, and examiners know exactly where to look.
The service covers the six areas on the poster: data cleaning, descriptive statistics, inferential statistics, hypothesis testing, regression analysis and detailed reporting. In practice the first of those takes the longest. Real survey data arrives with missing values, straight-line responses, reverse-coded items that were never reversed, and outliers that are sometimes genuine and sometimes a typing error.
You get the cleaned dataset, the syntax file, the output file and a written interpretation. The syntax matters more than people realise: it means the analysis can be re-run exactly, by you or by an examiner, and it means that if a reviewer asks you to add a control variable you are not starting from scratch.
The five service blocks on the poster, expanded into the actual steps.
Every parametric test rests on assumptions, and the assumptions are where most theses lose marks. Normality of residuals rather than of the raw variable. Homogeneity of variance across groups. Independence of observations, which is quietly violated whenever the same respondent answers twice. Linearity between predictors and outcome. Absence of severe multicollinearity.
We test each of these, report them, and if one fails we do the sensible thing rather than pretending it did not happen — a transformation, a robust standard error, a non-parametric equivalent, or a bootstrapped confidence interval. The chosen response goes into the methodology so that it is visible rather than hidden.
The same applies to sample size. If your design needs a larger sample than you have, we tell you before running the analysis, not after. An underpowered study is not made adequate by finding one significant coefficient in it, and an examiner who knows this will ask.
The raw file in any format, plus your objectives, hypotheses and questionnaire. We tell you free of charge whether the data can answer them.
Missing values, reverse-coded items, outliers and a proper codebook. You get the cleaned file back before analysis starts.
Normality, variance, independence, linearity and multicollinearity, each reported so it can go into your methodology.
The tests your objectives require, run in SPSS with the syntax saved so the whole thing can be reproduced exactly.
APA-formatted tables plus a written explanation of every coefficient, every p-value and every effect size.
A call in which we take you through the output until you can explain any of it without notes. This is the part that matters at the viva.
A results chapter is not a printout. Statistical significance is not the same as practical importance, an R-squared of 0.18 is a real finding and should be reported as one rather than dressed up, and a non-significant result is information rather than failure. We write the interpretation in that spirit, because an examiner respects a candidate who reports honestly far more than one who overclaims.
Effect sizes and confidence intervals go in alongside p-values as a matter of course. Tables follow APA conventions — decimal alignment, notes beneath, no leading zeros where the statistic cannot exceed one. Figures are redrawn rather than pasted as SPSS screenshots, because the default charts are not publication quality and reviewers notice.
If your work is heading for a journal rather than only a thesis, tell us at the start. The reporting requirements are stricter, some journals require the dataset to be deposited, and it is much easier to meet those standards from the beginning than to retrofit them later.
| What you receive |
|---|
| Cleaned dataset with a documented codebook |
| SPSS syntax file so the analysis can be reproduced |
| Full output file for your records |
| Assumption-testing results for the methodology chapter |
| APA-formatted result tables ready to paste in |
| Redrawn figures at publication resolution |
| Written interpretation of every table |
| A walk-through call before you submit |
Yes, and it is the single most useful call you can have. Sample size, scale selection and question wording all determine which analyses are possible later. Fixing an instrument before distribution costs an hour; discovering after four hundred responses that a construct cannot be tested costs a semester.
We will, and we will also tell you if the data does not support it. If a requested test is inappropriate we explain why in writing so you can take that back to your supervisor. We do not quietly substitute a different analysis without telling you.
Then that is the finding, and it gets reported. A rejected hypothesis with a sound method is a perfectly respectable thesis result and is often more interesting than confirmation. What we will not do is alter data, drop inconvenient cases without justification, or run tests repeatedly until something turns significant.
That is included in every engagement. You will be asked in the viva why you used one test rather than another and what a coefficient means, so we take you through it until you can answer without notes.
Advanced PLS-SEM: measurement model, structural model, bootstrapping and mediation.
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From raw data to real insight — descriptive, inferential, regression and multivariate work.
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Dashboards, charts and reports that make a finding visible in one look.
Read moreSend your topic, your dataset or one draft chapter. We will tell you honestly what it needs — before you pay anything. The first consultation is free.