1. Quantitative research design for researchers using questionnaires
  2. Essential statistical analysis using a questionnaire as an instrument
  3. Intermediate statistical analysis using a questionnaire as an instrument
  4. Interpretation and write-up of quantitative results generated from questionnaires
  5. Structural Equation Modeling and Confirmatory Factor Analysis for Questionnaire-Based Researchers
  1. Introduction to R: only online
  2. Essential statistical analysis using R
  3. Introduction to SPSS: only online
  4. Essential statistical analysis using the statistical package SPSS
  5. Intermediate statistical analysis using the statistical package SPSS
  1. Supervising a quantitative research project
  2. Introduction to Quantitative Research
  3. Integrating approaches: Unleashing the Power of Mixed-Methods Research: 2-days with 2 presenters
  4. Enhancing Quantitative Research with AI: half day
  5. PLS SEM modeling with SmartPLS available on request.

QUANTITATIVE RESEARCH WORKSHOP SERIES

1. Quantitative research design for researchers using a questionnaire as instrument: 1 Day.

WHY should you consider this workshop?

“To consult the statistician after an experiment is finished, is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”

This quote is from the Statistician R.A. Fisher, this workshop will assist with the research design to ensure that it is correctly applied.

One day workshop:

Who should attend: 

Everyone considering quantitative research including lecturers, students, as well as other quantitative researchers before data is gathered.

The goal of this workshop is to help prepare the researcher with the design of a research project to ensure that the research approach, research design, sampling and questionnaire is done in such a way that the research questions can be answered by conducting statistical analysis.

Here’s some things you’re going to learn:
  • The correct research approach, and design for the quantitative study.
  •  How to have valid and reliable constructs for the questionnaire.
  • How to sample in the correct manner and have a large enough sample size for analysis.
  • Which statistical techniques to use to test the research hypotheses.
  • Make sure the research hypotheses, questionnaire and statistical analyses are aligned.
  • What format the data must be in for statistical analysis.
  • How to distribute the questionnaire online.

 Contents:

  • The research process
  • The research problem
  • Research questions and hypotheses
  • Research approaches
  • Research design
  • Experimental design only focus on repeated measures design
  • The questionnaire: General issues
  • The questionnaire: Validation of constructs of scales
  • The questionnaire: Reliability of constructs of scales
  • Choose a validated scale
  • Calculating scores for variables
  • Online questionnaires and format
  • Sampling: Important concepts
  • Sampling: Types of sampling techniques
  • Sampling: Determine sample size
  • Intended statistical Analyses techniques for the questionnaire
  • The research consistency matrix aligning your research

2. Essential statistical analysis using a questionnaire as instrument: 1 Day

One day workshop:

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers.

The goal of this course is to help the researcher analyse data. The emphasis will be on the application of the techniques and not on the computer package employed. The mostly used statistical techniques for analysing questionnaires will be discussed. Note that this is not a Statistics course, but rather a practical approach. The structure of the course will follow the data analysis cycle. Please note that the analyses will be discussed but not done hands-on on a computer package.

Contents:

  • Read raw data into a statistical package
  • Cleaning and verification of data
  • Validation of the research instrument
  • Descriptive statistics and graphs
  • Exploratory analysis
  • How to decide on the statistical technique
  • Statistical analysis techniques
  • Interpretation of results

The techniques that will be discussed in the workshop are: Exploratory Factor Analysis, Cronbach Alpha Coefficient, independent T-test, The Pearson Chi-square test, Analysis of Variance, Correlation, Simple Linear Regression. There will be touched on Confirmatory Factor Analysis and Structural Equation Modeling briefly if time permits.

Outcomes:

  • Participant must be aware of all the pitfalls regarding the analysis process.
  • Participant must be able to follow the statistical analysis process.
  • Participant must understand how the statistical techniques works as well as the assumptions of the techniques

3. Intermediate statistical analysis using a questionnaire as instrument: 1 Day

Who should attend:

Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers. Follow up from essential data analysis.

The goal of this course is to help the researcher analyse data. The emphasis will be on the application of the techniques and not on the computer package employed. The mostly used statistical techniques for analysing questionnaires will be discussed. Note that this is not a Statistics course, but rather a practical approach. The structure of the course will follow the data analysis cycle. This is a discussion of all the steps and not done on the computer.

Contents:

  • The statistical analysis process will be followed.
  • Awareness of pitfalls in the analysis process.
  • How statistical techniques work as well as assumptions associated to the techniques. Remedies when assumptions of the statistical analysis cannot be met.
  • How to decide on a statistical technique.
  • Intermediate statistical techniques will be discussed.
  • Non-parametric techniques such as the KrusKal-Wallis Test and Mann Whitney test will be discussed.
  • TWO-WAY ANOVA with interactions will be discussed
  • Multiple Linear Regression, dummy variables in Regression, Hierarchical Regression will be duscussed
  • Touching on Confirmatory Factor Analysis and Structural Equation Modeling
  • Interpretation of results

Outcomes:

  • Participants must be aware of all the pitfalls regarding the analysis process.
  • Participants must be able to follow the statistical analysis process.
  • Participants must understand how the statistical techniques works as well as the assumptions of the techniques.

4. Interpretation and write-up of quantitative results: 1 DAY

One day workshop:

Who should attend: Everyone busy with quantitative research including lecturers, students as well as other quantitative researchers.

The goal of this course is to help the researcher interpret and write-up the results of quantitative analyses. The emphasis will be on the correct scientific write-up of the results for an article or dissertation. The most used statistical techniques for analyzing questionnaires will be discussed.

A pdf template will be provided that provides a standardized way of reporting statistical findings.

Outcomes:

  • Participant must be able to write-up the statistical techniques discussed in a scientific manner by using the provided template.
  • Participant must be able to interpret the discussed statistical techniques.

Contents:

Part I:
  • Introduction
  • Structure of reporting
  • Research methodology
Part II:
  • Reporting statistical techniques for comparing groups
  • Reporting statistical techniques for relating variables
Part III:
  • The write-up of a Structural Equation Model (SEM) will also be discussed.
  • Discussion of participants own write-up if time permits.

5. Structural Equation Modeling and Confirmatory Factor Analysis for Researchers: 1 – day

One day workshop:

Who should attend:

Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers. Please note that Structural Equation Modeling and Confirmatory Factor Analysis is more complex analyses, so it is advised to do this workshop after basic analysis workshops is attended.

The goal of this course is to help the researcher analyse data. The emphasis will be on the application of the techniques and not on the computer package employed. Note that this is not a Statistics course, but rather a practical approach.

Contents:

  • The data analysis cycle
  • Exploratory Factor Analysis
  • Confirmatory Factor Analysis
  • Structural Equation Modeling

ANALYSIS WITH A STATISTICAL PACKAGE SERIES

1. Introduction to R: only online

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers wanting to learn the free quantitative analysis packages.

Contents:

  • Installation of R for beginners.
  • Installation of the R Studio editor.
  • Installation of the different R libraries.
  • Activating libraries.
  • Importing raw data from, for example MS Excel data files, into R.
  • Working with datasets, variables and objects.
  • Choose variables and rows from the dataset.
  • Calculate descriptive statistics.
  • Do a basic plot.
  • Save output from R.

2. Essential statistical analysis using the statistical language R: 2-3 days

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers. Participants must have a laptop to install R and Rstudio.

The goal of this course is to help the researcher analyse data. The mostly used statistical techniques for analysing questionnaires will be discussed. Note that this is not a Statistics course, but rather a practical approach. The structure of the course will follow the data analysis cycle. Please note that the analyses will be done hands-on on a computer package.

Contents:

  • Reading raw data into a statistical package.
  • Cleaning and verification of data.
  • Validation of the research instrument.
  • Calculate descriptive statistics and graphs.
  • Draw tables.
  • Conduct Exploratory analysis.
  • Various statistical techniques will be applied in the Statistical language R: Construct validation (Exploratory Factor Analysis) and construct Reliability (Cronbach Alpha Coefficient).
  • Statistical techniques for comparing groups: The independent T-test, the Paired T-test, and Analysis of Variance (ANOVA).
  • Statistical techniques for testing relationships: The Pearson Chi-square test, Correlation and Simple Linear Regression.
  • Interpretation of results.

Outcomes:

  • Participants must be able to follow the statistical analysis process in R.
  • Participant must understand how the statistical techniques works as well as the assumptions of the techniques.

3. Introduction to SPSS: only online

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers wanting to learn the quantitative analysis package SPSS. A valid licence for SPSS is required.

Contents:

  • Import raw data, for example MS Excel data files, into SPSS.
  • Work with datasets and variables.
  • Select variables from a dataset.
  • Calculate descriptive statistics.
  • Conduct basic plots.
  • Save output from SPSS.

4. Essential statistical analysis using the statistical package SPSS: 3-days

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers. Participants must have a valid SPSS licence.

The goal of this course is to help the researcher analyse data. The mostly used statistical techniques for analysing questionnaires will be discussed. Note that this is not a Statistics course, but rather a practical approach. The structure of the course will follow the data analysis cycle. Please note that the analyses will be done hands-on on a computer package.

Contents:

  • Reading raw data into a statistical package.
  • Cleaning and verification of data.
  • Validation of the research instrument.
  • Calculate descriptive statistics and graphs.
  • Draw customized tables.
  • Conduct Exploratory analysis.
  • Various statistical techniques will be applied in the Statistical package SPSS: Construct validation (Exploratory Factor Analysis) and construct Reliability (Cronbach Alpha Coefficient).
  • Statistical techniques for comparing groups: The independent T-test, the Paired T-test, and Analysis of Variance (ANOVA).
  • Statistical techniques for testing relationships: The Pearson Chi-square test, Correlation and Simple Linear Regression.
  • Interpretation of results.

Outcomes:

  • Participants must be able to follow the statistical analysis process and content described in SPSS.
  • Participant must understand how the statistical techniques works as well as the assumptions of the techniques.

5. Intermediate statistical analysis using a questionnaire as instrument: 1 Day

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers. Follow-up from essential analysis in SPSS.

The goal of this course is to help the researcher analyse data. The intermediate statistical techniques for analysing questionnaires will be discussed. Note that this is not a Statistics course, but rather a practical approach. The structure of the course will follow the data analysis cycle. Please note that the analyses will be done hands-on on a computer package.

Contents:

  • Awareness of pitfalls in the analysis process.
  • Reading raw data into a statistical package.
  • The statistical analysis process followed in SPSS.
  • How statistical techniques work as well as assumptions associated to the techniques. Remedies when assumptions of the statistical analysis cannot be met.
  • How to decide on a statistical technique.
  • Intermediate statistical techniques will be applied in the statistical package SPSS.
  • Non-parametric techniques such as the KrusKal-Wallis Test and Mann Whitney test will be applied in the statistical package SPSS.
  • TWO-WAY ANOVA with interactions will be applied in the statistical package SPSS.
  • Multiple Linear Regression, dummy variables in Regression, Hierarchical Regression will be applied in the statistical package SPSS.
  • Interpretation of results

Outcomes:

  • Participants must be aware of all the pitfalls regarding the analysis process.
  • Participants must be able to follow the statistical analysis process in SPSS.
  • Participants must understand how the statistical techniques works as well as the assumptions of the techniques.

ADDITIONAL WORKSHOPS

 1. Supervising a quantitative research project: 1-day

Who should attend: Supervisors working with students or intending to work with students following the quantitative or mixed methods research approaches.

Contents:

  • The research processes.
  • The research problem
  • Research questions and hypotheses
  • Research approaches
  • Research design
  • Experimental design only focusses on repeated measures design.
  • The questionnaire:
    • General issues
    • Validation and reliability of constructs
    • Choosing a validated scale
  • Calculating scores for variables
  • Online questionnaires and format
  • Sampling:
    • Important concepts
    • Types of sampling techniques
    • Determine sample size.
  • Statistical Analyses techniques for the questionnaire
  • The research consistency matrix to align the research.
  • Statistical packages
  • Choose a statistical technique.
  • How to conduct a proper statistical analysis

2. Introduction to quantitative research: 1-day

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other researchers.

Contents:

  • The importance of correct design and alignment of the research
  • A definition of quantitative research
  • Types of data in quantitative research
  • What is Quantitative research and when to use it
  • What is Qualitative research and when to use it
  • Mixed methods strategies
  • Types of research design in the quantitative approach
  • Measuring instruments and experiments
  • Validity and reliability of questionnaires in quantitative research
  • Variables and levels of measurement
  • Sampling in quantitative research
  • Gathering data – online questionnaires and data format
  • Quantitative Analysis packages
  • The Data analysis cycle
  • Graphical presentations of data
  • Choose a statistical technique
  • Criteria for a quantitative research Hypotheses
  • Hypothesis testing
  • An example analysis in quantitative research
  • An reporting example in quantitative research

3. Integrating approaches: unleashing the power of Mixed-Methods research: 2-days 2 presenters

Who should attend: Everyone embarking on mixed methods research including lecturers, students as well as other researchers.

Join us for an immersive workshop on mixed-methods research, where participants will explore the integration of qualitative and quantitative approaches.

Contents:

  • This comprehensive workshop provides an overview of quantitative and qualitative design while offering a deep understanding of the principles, design considerations, and practical applications of mixed-methods research.
  • Participants will gain insights into the synergistic potential of combining qualitative and quantitative data collection and analysis techniques.
  • Through interactive discussions, case studies, and hands-on exercises, participants will learn how to strategically design and execute mixed-methods studies, analyze and synthesize data, and effectively report findings.
  • This workshop equips researchers with valuable tools and skills to harness the strengths of both qualitative and quantitative methods, fostering a comprehensive understanding and empowering them to generate richer insights and deeper understanding in their research endeavours.

4. Enhancing Quantitative Research with AI: half day

Who should attend: Everyone embarking on quantitative research including lecturers, students as well as other quantitative researchers.

  1. What is artificial Intelligence(AI)?
  2. AI Assistants & Large Language Models(LLM)
  3. Prompts
  4. AI to do analysis?
  5. Requirements for statistical analysis
  6. How to do a proper statistical analysis
  7. AI for statistical analysis
  8. AI for definitions
  9. AI for assumptions
  10. AI application with SPSS for assumptions
  11. AI application with R
  12. AI for reporting and write-up
  13. AI limitations & Ethics