SPSS Data Analysis Help in Malaysia — Run, Interpreted and Written Up

It follows from your variable types and how many groups you are comparing. Two independent groups on a continuous outcome means an independent t-test; three or more means ANOVA with a post-hoc test. Two categorical variables means chi-square. Predicting a continuous outcome from several variables means multiple regression. A conditional relationship means moderation, an explanatory pathway means mediation. Send your hypotheses and variable types and we will name the tests.

You report them. Theses are marked on whether the design was sound and the reporting honest, not on whether relationships turned out significant, and plenty of accepted Malaysian dissertations have several unsupported hypotheses. We do check first whether the non-significance is technical — a reverse-coded item never recoded, poor reliability, or the wrong test for your data type. If it is genuine, it gets reported and explained in the discussion, which strengthens Chapter 5 rather than weakening it.

No. Deleting cases until p falls below 0.05, adding responses that were never collected, or quietly dropping a failed hypothesis is data fabrication. It is also easier to detect than students expect, because the numbers stop reconciling with the raw file an examiner can ask to see. We are asked this regularly and the answer does not change — but the honest route out is almost always better than the one people fear.

First check for reverse-coded items that were never recoded, which is by far the most common cause. Then examine item-total correlations — removing one or two weak items is defensible if you report it and the construct still has enough items to be meaningful. If alpha stays low after that, it is reported as a limitation. What is not acceptable is deleting items one at a time until the number crosses 0.7.

No, it changes which tests are appropriate. Mann-Whitney replaces the independent t-test, Wilcoxon replaces the paired version, Kruskal-Wallis replaces ANOVA, and Spearman replaces Pearson. Larger samples are also more tolerant of moderate non-normality than most students assume. The important thing is to justify the switch in writing rather than run a parametric test and hope nobody checks.

Yes — the .sav data file, the .spv output and the syntax, at every price point. It matters more than students expect. If your supervisor asks you to rerun something with one variable excluded you can actually do it, and the files are the evidence that the analysis was genuinely performed on your data.

Yes. SmartPLS suits complex models with several latent constructs, formative measures or smaller samples, and is reported in two stages — measurement model then structural. AMOS is used where your faculty expects covariance-based SEM with fit indices such as CFI, TLI and RMSEA. Tell us which your programme accepts, because faculties differ and it is not a free choice.

It can describe procedures, but it cannot run your analysis on your file, and asking it to interpret pasted output is where students get hurt. It will produce confident numbers that do not appear anywhere in your dataset, invent effect sizes, and misreport which model in PROCESS you used. Everything in Chapter 4 must reconcile with the .sav file, and generated results do not. That is also the reason we hand over the output — so every figure can be traced back.

Findings chapters are unusual because the tables cannot be paraphrased and should not be. The generated text sits in the interpretation between tables, and that is what has to be rewritten — replace general statements with the specific figures from your own output, name the test and the value, and say what it means for the objective it answers. Running the chapter through a humaniser is worse than useless here, because it corrupts statistical terms and reported numbers.

Yes, and ordering both together is cheaper than separately. Chapter 5 interprets each finding against the literature you reviewed, addresses the hypotheses that were not supported, and draws out theoretical and practical implications. It is difficult to write well without having run the analysis, which is why most students who order Chapter 4 come back for it.

Excel or CSV is fine, and a Google Forms export works directly. Send it exactly as collected without cleaning it first — the screening decisions need to be documented, and a file that has already been tidied hides the information we need to justify your final sample count. Include your questionnaire so items can be matched to constructs.

One rerun round is included with every order. Supervisor comments on a findings chapter are normal — usually an extra control variable, a split by demographic group, or a different post-hoc test. Further reruns beyond the first are RM 200, and we will tell you upfront if what is being asked for is a genuinely new analysis rather than a revision.

Completely. Your file is used only for your analysis, never reused as demonstration data, never shared beyond the analyst assigned to you, and deleted once your order closes. Respondent-level data carries obligations you accepted when you collected it, and we treat those as binding on us as well.