Quantitative Methods and Research
SESSION 15: Quantitative Methods
Focus on numbers and empirical data.
Use of close-ended questions in data collection.
Goal: Test objective theories and explore relationships between variables.
Involves measurement with instruments and employing statistical procedures.
Follows a deductive approach; rooted in a positivist worldview.
Mixed Methods
Combines qualitative and quantitative approaches for comprehensive analysis.
The Process of Deduction
Start from a theory.
Derive hypotheses.
Collect data to test hypotheses.
Interpret results, typical of quantitative research.
Epistemology and Worldview of the Researcher
Positivism:
Theories must be tested and refined.
Break down broader problems into smaller, testable hypotheses.
Emphasizes cause-effect relationships with measurable outcomes.
Key Concepts of Positivism
Imperfect Evidence: Research evidence is always fallible.
Concept of falsification: cannot truly prove a hypothesis - only fail to reject it.
Karl Popper (1935):
Example: Observing white swans does not mean all swans are white; finding one black swan refutes that theory.
Relationships Between Quantitative Variables: Correlation
Types of Correlation:
Positive Correlation: As one variable increases, so does another.
Negative Correlation: As one variable increases, another decreases.
Zero Correlation: No observable relationship between variables.
Example: Time spent on social media vs. wellbeing.
Relationships Between Variables: Causation
Beyond correlation, researchers seek to understand causal relationships.
Other influencing factors include:
Time of year, parenting, family environment, and lurking variables.
Determining Causation
Difficult to assess causation through observational studies or surveys.
Easier in experimental studies that control for other variables through:
Randomization.
Balanced groups across conditions.
SESSION 16: Experimental Research
Flawed Study Example: Dr. Zimmo’s Sample Bias
Selected 25 students for feedback; only 10 responded positively.
Likely sampling bias due to non-random and incomplete sample.
Getting Primary Data
Ask People: Interviews, surveys, focus groups.
Watch People: Direct observation.
Test People: Conduct experiments.
Sampling
Process of selecting units (people, organizations, etc.) from a population.
Goal: Generalize findings from the sample back to the broader population.
Sample vs. Population
A good sample is representative of the population.
Each participant should have an equal chance of selection to avoid favoritism.
Inferential Statistics
Allows predictions about a population based on sample data.
Randomization Techniques
Simple Random Sampling:
Similar to a lottery; involves number assignment and random selection.
Proportional Stratified Sampling:
Divides population into strata and samples accordingly to maintain proportional representation.
Qualities of a Good Sample
Representativeness and equal chances of being selected help reduce bias.
Bias can lead to inaccurate conclusions.
Examples of Biased Sampling
Voluntary Response Sample: May not represent the entire population due to self-selection.
Convenience Sample: Easy access to participants, but not representative.
Non-Response Bias Example
Example of survey on extramarital affairs where only 4.5% responded, potentially skewing results due to the behavior of non-responders.
Sampling Summary
Challenges in obtaining representative samples.
Random sampling is ideal but practically difficult due to biases:
Selection bias from voluntary/convenience samples.
Non-response bias.
Measurement in Research
Measurement: Assigning numbers to objects based on defined rules.
Variable: A measurable characteristic that varies among individuals (e.g., hours studied, screen time).
Types of Variables
Quantitative Variables: Numeric values (e.g., counts, measurements).
Categorical Variables: Qualitative differences that do not imply quantity.
Scales of Measurement
Nominal Scale: Numbers as labels only; no order (e.g., category labels).
Ordinal Scale: Numbers imply order without equal spacing (e.g., rankings).
Interval Scale: Ordered with equal intervals; no true zero (e.g., temperature).
Ratio Scale: Ordered, equal intervals, with a true zero point (e.g., weight).
Evaluating the Quality of Quantitative Research
Key Concepts:
Objectivity: Findings should be independent of the researcher.
Reliability: Consistency of measurements over time - described by the formula: X = T + E (observed score = true score + error).
Validity: Measures how well an instrument measures what it intends to.
Validity & Reliability Combined Using Target Analogy
Reliable but not valid.
Valid on average but not reliable.
Neither valid nor reliable.
Both reliable and valid.