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Data Analysis: Practical Tips & Best Practices

Plan, analyse, and report with confidence — from cleaning to models, validation, and publication-ready outputs.

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Reliable Methods. Reproducible Results. Clear Reporting.

These field-tested tips cover the full analysis lifecycle: scoping, cleaning, EDA, assumptions, modelling, validation, interpretation, and write-up—across SPSS, STATA, R, Python & MATLAB.

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Cleaning EDA Assumptions Modelling Validation Reporting

Overview

Good analysis is a chain of defensible decisions: plan → prepare → explore → test/model → validate → report. Document every step so your work is reproducible and publication-ready.

Core Steps

1. Plan
  • Define RQs/hypotheses & variables
  • Power/sample strategy
  • Pre-specify analysis plan
2. Prepare
  • Clean, code, label, recode
  • Handle missing/outliers
  • Document a codebook
3. Explore
  • Distributions & relationships
  • Visual EDA (box/violin/heatmap)
  • Feature engineering ideas
4. Test/Model
  • Choose appropriate tests/GLM
  • Check assumptions & diagnostics
  • Effect sizes & CIs
5. Validate
  • Cross-validation/bootstrapping
  • Sensitivity analyses
  • Pre-registered robustness
6. Report
  • APA/Harvard tables & figures
  • Plain-English interpretation
  • Share code + data (as allowed)

Common Pitfalls (and Fixes)

p-Hacking & HARKing
  • Pre-register plan
  • Correct for multiplicity
  • Separate confirmatory/exploratory
Bad Assumptions
  • Normality/linearity/homoscedasticity
  • Transformations or robust methods
  • Diagnostics + residual plots
Collinearity
  • VIF/condition indices
  • Centering or dimensionality reduction
  • Regularization (ridge/lasso)
Data Leakage
  • Split before preprocessing
  • Use pipelines for CV
  • Strict train/test separation

Software-Specific Tips

SPSS & STATA
  • Syntax/do-files for reproducibility
  • Value/variable labels & codebooks
  • Export APA-style tables
R & Python
  • Projects/venvs & lockfiles
  • Notebooks + scripts; tidy logs
  • Pipelines (tidymodels/sklearn)
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Quick Checklists

Before Analysis
  • Lock RQs, variables, plan
  • Clean & label; codebook ready
  • Decide on missing/outlier rules
  • Set version control & folders
Before Submission
  • Diagnostics & robustness done
  • APA/Harvard tables & figures
  • Plain-English interpretation
  • Zip code + data (as allowed)

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Frequently Asked Questions

Use what your field supports: SPSS/STATA for social/health; R/Python for flexibility and ML; MATLAB/EViews for specialised needs.

Diagnose MCAR/MAR/MNAR. Prefer multiple imputation or model-based methods over listwise deletion unless missingness is trivial.

Yes—match checks to your method (e.g., normality/linearity for OLS, proportional hazards for Cox). Report diagnostics briefly.

Report both. Emphasise effect sizes and CIs for practical interpretation; p-values alone are insufficient.

Use cross-validation, regularization, and keep features parsimonious. Reserve a hold-out set if sample size allows.

Use clean tables/plots with clear labels. Align sections to RQs/hypotheses. Provide plain-English takeaways before technical detail.

Report the primary model plus key robustness checks. Avoid flooding with minor variations—summarise in an appendix if needed.

Yes, if justified by scale/assumptions. Pre-specify your decision rules and keep your story coherent.

When policies allow, yes—share a de-identified dataset and scripts/notebooks. Improves credibility and reproducibility.

Yes—cleaned data, labeled code, diagnostics, and APA/Harvard-ready tables/figures with interpretation notes.