Appraising

Quasi- experimental (no randomisation)

True-experimental (experiment/control; variable manipulation, random)

Experimental studies (applying an intervention to a group)

- Case-report (report of a symptom/signs/diagnosis/treat ment & follow up for an individual) - Case-serial reports (follow up a subject with specific exposure) - Cross-sectional studies - Ecological studies

Analytical design (test hypothesis, identify cause & effect, test

correlations); Frame research question using PICO

(population/patient/problem, intervention, comparison, Outcome)

Observational studies (understanding cause and effect, without any control on the independent variable/non- randomization/no intervention)

Ontology (what exists? What’s out there to know?)  Objectivism/realist ontology (existence independent of researcher)

Epistemology (the way we know things/perceived relationship with knowledge)  Positivist paradigm (truth out there to be discovered/truth is tested/truth is measured)

Methodology (how we can get the knowledge?)  Deductive approach (testing theories or hypothesis/from generalization to specific examples/activities

Method (exact procedure to acquire knowledge)  Quantitative approach (using numerical forms of the data in a systematic way to investigate a matter)

Descriptive design (describe a phenomena or population)

Types of quantitative variables (Independent/Dependant): Continuous: interval (temperature); ratio (meaningful zero point – e.g., height, weight) Discrete: Ordinal (educational level); nominal/categorical (gender, race); binary (pass/fail, yes/no)

Quantitative design

Data analysis - Univariate analysis (analysis of a single variable) - Bi-variate analysis (analysis of two variables/e.g., ANOVA, t-test, chi-square, correlation) - Multi-variate analysis (multiple linear, regression, logistic regression)

Analysis software: SPSS, Stata, SAS, AMOS, Lisrel

Randomised control trial (RCT) design: - Post-test only, pre-test-post- test only, Solomon four group design, factorial, randomised block, crossover or repeat measure - Randomization: Completely randomised, stratified, cross- over, cluster-randomised, cluster-crossover, step-wedge

Pre- experimental (no control group)

Data collection - Rating scales - Interview (structured – only choosing one response) - Questionnaires (self-developed) - Observation - Experiment - Secondary data collection - Physiological measurement (using self-report, observation, direct measurement, indirect measurement, laboratory tests, electronic monitoring)

- Case-control (retrospectively compare individuals with a health outcome/disease with those without that health outcome) - Cohort (prospective; retrospective or historical); longitudinal; panel - Cross-sectional - Ecological (population-based)

Sampling

Probability - Stratified - Cluster - Multi-stage - Random (simple/systematic)

Non-probability - Convenience - Purposive - Snowball - Quota

Non-randomised controlled study (NRC): non-randomised control trial, controlled before & after, interrupted time- series, historically controlled, cohort, case-control, ex-post- facto, single group pre-test, post-test

Figure 1 - Research process in positivist studies using quantitative methods

References:

Bowling, A. (2014). Research methods in health: investigating health and health services. McGraw-Hill Education (UK).

Christensen, L. B., Johnson, B., Turner, L. A., & Christensen, L. B. (2011). Research methods, design, and analysis.

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Davis, P., & Scott, A. (2007). Health research sampling methods (pp. 155-173). Sage, London. Greenhalgh, T. (2014). How to read a paper: the basics of evidence-based medicine. John Wiley & Sons. Harwell, M. R. (2011). ReseaRch Design in Qualitative/Quantitative. The Sage handbook for research in

education: Pursuing ideas as the keystone of exemplary inquiry, 147. Liamputtong, P. (2013). Research methods in health: foundations for evidence-based practice. McCrum-Gardner, E. (2010). Sample size and power calculations made simple. International Journal of

Therapy and Rehabilitation, 17(1), 10-14. doi:10.12968/ijtr.2010.17.1.45988 Prajapati, B., Dunne, M., & Armstrong, R. (2010). Sample size estimation and statistical power

analyses. Optometry today, 16(07), 10-18. Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. Sage. Smith, A. M. (2012). Research methodology: A step-by-step guide for beginners. Nurse Education in

Practice, 12(3), e25. Suresh, K. P., & Chandrashekara, S. (2012). Sample size estimation and power analysis for clinical research

studies. Journal of human reproductive sciences, 5(1), 7. Wahyuni, D. (2012). The research design maze: Understanding paradigms, cases, methods and

methodologies. Williams, C. (2011). Research methods. Journal of Business & Economics Research (JBER), 5(3). Yilmaz, K. (2013). Comparison of quantitative and qualitative research traditions: Epistemological,

theoretical, and methodological differences. European Journal of Education, 48(2), 311- 325.

This research process has been developed by Dr. Nasim Salehi, which can be beneficial in understanding the most common research designs for positivist studies. It will also help with conceptualisation and operationalisation of your research.