We provide statistical and quantitative data analysis services using SPSS and other appropriate analytical methods. The work is undertaken by statisticians, econometricians, mathematicians, and research professionals with experience across a range of statistical and computational techniques, from descriptive and basic inferential analyses to more advanced modelling.

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The analysis is selected according to the research objectives, study design, characteristics of the data, and research questions. Where appropriate, relevant assumptions and diagnostic tests are also considered.

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The scope of quantitative data analysis may include:

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1. Data preparation and coding
   Converting data into numerical form where required and preparing datasets for statistical analysis.

2. Data management
   Merging, consolidating, and organising datasets into a structured database.

3. SPSS data preparation
   Preparing SPSS datasets for the analysis of nominal, ordinal, interval, and ratio-level data.

4. Data quality assessment
   Reviewing datasets for data-capturing errors, inconsistencies, missing values, and other issues that may affect the reliability of the analysis.

5. Statistical analysis
   Selecting and conducting appropriate statistical tests in accordance with the research objectives and characteristics of the data. Depending on the requirements of the study, these may include:

   • Reliability and normality testing: Cronbach’s Alpha and appropriate tests of normality.

   • Tests of association: Chi-square, Fisher’s exact test, Phi, and Cramer’s V, where appropriate.

   • Comparative analyses: Independent and paired-samples t-tests, One-Way ANOVA, Mann-Whitney U, and Kruskal-Wallis tests, as appropriate.

   • Correlation analyses: Pearson’s, Spearman’s, and Kendall’s correlation coefficients to examine relationships between variables.

   • Regression analysis: Linear, binary logistic, and multinomial logistic regression to examine relationships between variables and, where appropriate, estimate effects or predictive relationships. Relevant diagnostic and assumption tests are also conducted where required.

   • Advanced statistical and computational modelling: Other statistical, computational, or modelling techniques may be applied where they are appropriate to the research design and analytical objectives. This may include specialised methods such as the Implicit Association Test (IAT), hierarchical cluster analysis, principal component analysis, and factor analysis.

6. Interpretation and reporting
   Preparing a clear written report or Excel-based presentation of the analysis, including an interpretation of the statistical findings in relation to the research questions and objectives

 

The specific analyses undertaken depend on the nature of the study and the data available. Statistical methods are selected on the basis of methodological appropriateness rather than applied as a fixed set of tests.