Tshwane University of Technology A GPS to navigate succesfull primary quantitative research Dr Marthi Pohl

The journey from planning to execution What is the best road to take in order to achieve the intended result ( arrive at destination) within the best time frame?

Context Where do I begin? - Statistical Requirement The departure point = survey methodology The pivotal role of your sample and questionnaire design Questionnaire design Revisit your statistical requirement What methods can I use to achieve my objectives? Basic inferential statistics Multivariate analysis Planet of other quantitative techniques/ models

Statistical Requirements Differences Relationships Prediction Classification Data reduction Profiling/ segmentation Pattern Recognition Time series analysis ( or time and e.g. company – panel data)

Objectives of research The departure point = survey methodology Sample method Sample size Data collection Questionnaire design Objectives of research

Sample and questionnaire: The corpus callosum of Sample and questionnaire: The corpus callosum of quantitative primary research Corpus Callosum: Links the two brain hemispheres to align a person’s actions with his/her visions and intentions Objectives and Literature Review Results, conclusions

Sample methods Probability samples Random? Key for generalization Very difficult to achieve in a survey Stratified random sampling vs cluster sampling Systematic sample Representative? Critical to ensure unbiased samples Know or acknowledge the existence of bias if not representative

Sample methods Non-probability Purposive Convenience Snowball Acknowledge shortcomings Multi- stage (select eg departments and then individuals)

Sample size Determined by Key pitfall: The magic 30? Margin of error and confidence level required Statistical Techniques to be applied Key pitfall: Determining sample size without keeping non-response into account The magic 30? Realized sample vs initial planned sample When it make sense to use a census – total population

Data scale type fundamentally impact analysis methods that can be applied

Questionnaire design – scales The key to enable analysis required to answer research question Need different map types for different destinations (roads, rivers, flight paths) Types Dichotomous Rating (3 -7, 10) - absolute Semantic differential scales (opposite words) Side by side matrix (importance and satisfaction asked next to each others) Ranking (relative) Tick all that apply

An example of a semantic differential scale

Data collection Method of execution Online But beware = exit survey at any point in time Structured interviews But beware availability Fieldworker knowledge and skills Email But beware the dangerous delete button Weigh the advantages and disadvantages of each method before making a final decision (might result in a cul de sac!)

Statistical Requirements (revisit) Differences – between two or more groups w.r.t a variable of interest Relationships – between two variables Prediction – direction of relationship and potential multiple predictors Classification – can group membership (eg voters/non-voters) be predicted by means of a list of variables Data reduction – Can a large number of variables actually be represented by a much smaller number of underlying factors Profiling/ segmentation Pattern Recognition Time series analysis ( or time and e.g. company – panel data)

A peek into Statistical Techniques Parametric and non-parametric test Parametric Key to ensure assumptions are met Assumption violation - determine to what extent test to apply is robust w.r.t violations of the assumptions Non parametric techniques Distribution free Can be applied to small samples

Generally well -known statistical techniques Means tests ( t test, ANOVA, MANOVA, Mann-Whitney, Kruskal- Wallis) Relationships ( Correlation, Pearson Chi-square test for independence, several measures of associations) Regressions ( a multitude of types) Discriminant analysis Exploratory and confirmatory analysis Structural equation modelling Cluster analysis

The lesser known ones Game Theory Neural Networks Self Organising Maps (type of clustering) Data mining techniques for fraud detection and control

A view of techniques applied in fraud detection and control

But beware, data manipulation… Get the facts first, then you can distort them as much as you please ( Mark Twain) To be continued….techniques and reporting