Quantitative Research Flashcards
(43 cards)
Definition of Quantitative Research
Research methods dealing with NUMBERS and anything that is MEASURABLE IN A SYSTEMATIC WAY of investigation of phenomena and their relationships. It is used to answer questions on relationships within measurable variables with an intention to explain, predict and control a phenomenon.
Purpose of Quantitative vs Qualitative Research
Quantitative: Measuring outcomes. Generalize results from sample to population - How much?
Qualitative: Deep understanding of phenomenon (often exploratory) - What? Why?
Data Collection of Quantitative vs Qualitative Research
Quantitative: Standardized techniques (e.g. tests, scales, questionnaires).
Qualitative: Unstructured or semi-structured techniques (e.g. interviews, open ended questionnaires, focus groups) (i.e. less structure).
Data analysis of Quantitative vs Qualitative Research
Quantitative: Numerical comparisons and statistical inferences.
Qualitative: Themes from descriptions.
Research Question of Quantitative vs Qualitative Research
Quantitative: Clearly defined (PICO)
Qualitative: Not (always) clearly defined (PICo, SPIDER, SPICE)
Goal of Quantitative vs. Qualitative Research
Quantitative: Verify the theory, Test hypothesis.
Qualitative: Development of theory, hypothesis.
Key Characteristics of Quantitative Research
- Process is deductive (To test the idea/s)
- Data is numeric (To enable statistical analysis).
- Pre-specified methods are used (to ensure scientific rigour).
4 Steps of Quantitative Research
Theory - Hypothesis - Obeservation/test - Confirmation/rejection
You have a theory, from it you make a hypothesis, test the hypothesis, confirm or deny the hypothesis.
Key Objectives of Quantitative Research
- To describe (Impact/burden of the problem)
- To evaluate (Connection between the dependent and independent variable vs causation) (To test a treatment).
Key Objectives of Quantitative Research
- To describe (Impact/burden of the problem).
- To evaluate (Connection between the dependent and independent variable vs causation) (To test a treatment).
- To predict (Identify variables that predict outcomes).
- To compare (Identify differences between groups) (provide a base of evidence for practice).
Research Designs
Descriptive (PO)
- Survey/Case reports
- Qualitative
Analytical (PICO)
- Observational analytic
- Experimental
Descriptive Design
- Without an intervention (retrospective)
- Not to quantify relationships
- Reveal important findings - make a new hypothesis
- N (number of participants) can be small, but # variables can be large.
- Case reports, Case-series, single case design, qualitative studies and surveys (cross-sectional) studies.
Analytical Design
- Quantify relationship between two factors: Effect of intervention/exposure on outcome.
- Test hypothesis
- Measuring intervention/exposure (observational analytic design: case-control, cohort, cross-sectional…)
Or - Researcher manipulates intervention/exposure (experimental design: RCT).
Quasi Experimental
Test causality with sub-optimal variable control (when you cannot control every confounding factor).
- Before - after design
True Experimental
Test causality with optimal variable control (no confounding factors).
- Randomized Control Trial
Case-Study/Case-Series
- No control group
- Explore new treatment/topic on which limited knowledge exists
- Often qualitative (rare in quantitative: used when not enough participants are found (rare diseases))
Participant with condition of interest → Information about clinical outcome.
Case-Control Design
- Retrospective
- Two groups: one with desired outcome - one without. What might have caused the desired outcome? Compare the differences.
- Data already existing. Does not modify the data.
Advantages of Case-Control Design
- Quick and cheap
- Only feasible method for very rare disorders or those with long lag between exposure and outcome.
- Fewer subjects needed than cross-sectional.
Disadvantages of Case-Control Design
- Reliance on recall or records to determine exposure status.
- Confounders
- Selection of control groups is difficult
- Potential bias: recall, selection
- Have to trust that everything regarding the intervention was done correctly.
Cohort Design
- Adaption of RCT, for when you cannot do random sampling.
- Controlling confounding factors more important than random sampling.
- The results cannot be verified 100% because of confounding factors.
- Prospective (can also be retrospective)
Participants → Exposure to intervention → Outcome
→ No exposure to intervention
Advantages of Cohort Design
- Ethically safe
- Subjects can be matched
- Can establish timing and directionality of events
- Eligibility criteria and outcome assessments can be standardized.
- Administratively easier and cheaper than RCT.
Disadvantages of Cohort Design
- Controls difficult to identify
- Exposure may be linked to a hidden confounder
- Blinding can be difficult
- No Randomization
- For rare diseases, large sample size or long follow-up necessary.
Cross-Sectional Design
- One time measurement
- No groups comparison
- No intervention
- Used to understand a phenomenon
- Which factors influence particular outcome
- Exploratory
- No causality
Participants → Measurement of outcomes and other factors at the same time
Advantages of Cross-Sectional Design
- Cheap and simple
- Ethically safe
