Characteristics and Methods of Scientific Psychological Research

Characteristics of Scientific Psychological Research

• Conceptual Framework: Psychological research seeks to understand human nature and predict behavior, similar to daily human interactions but using more systematic and sophisticated tools.

• Example Scenario (Marco):

  • Marco wants a deadline extension but fears a negative impression from his lecturer.

  • Theory: Based on observations (lecturer looks angry at late students; inflexible scheduling), Marco theorizes the lecturer is rigid.

  • Hypothesis: Marco hypothesizes she will refuse the extension.

  • Experimentation/Measurement: He tests this by casually mentioning a "friend" needing more time and observing her response (facial expressions, words, response time).

  • Revised Understanding: The lecturer smiles and grants a week, surprising Marco and requiring a theory adjustment.

• The Scientist's Tools: Research requires multiple methods (conceptualized as a "carpenter's tools") to lay an empirical foundation. Key features include:

  • Theoretical Framework: Systematic organization and explanation of observations.

  • Standardized Procedures: Consistent procedures for all participants except for variables being tested.

  • Generalisability: Use of representative samples to apply findings to broader populations.

  • Objective Measurement: Using reliable and valid measures to assess variables objectively.

Ethical and Cultural Considerations in Research

• Indigenous Partnership: Research involving Indigenous communities (individuals, families, and communities) must prioritize respectful relationships, benefit the people concerned, and foster substantive participation (Muir & Dean, 2017).

• Cultural Competence: Defined as systematic, responsive inquiry cognizant of cultural context. It involves:

  • Understanding and appreciating the cultural context.

  • Framing the epistemology of the evaluation.

  • Employing culturally and contextually appropriate methodology.

  • Using stakeholder-generated interpretive means for results (SenGupta, Hopson, & Thompson-Robinson, 2004).

• Methodological Appropriateness: While qualitative research allows Indigenous people to express themselves in their own words, quantitative research (e.g., Longitudinal Study of Indigenous Children - LSIC) can be appropriate if it includes Indigenous leadership, community involvement, and local interviewers.

Quantitative vs. Qualitative Research Methods

• Quantitative Research:

  • Process: Uses experiments or surveys to gather data for statistical analysis to test predetermined hypotheses.

  • Features: Large sample sizes, representative of the population, high reliability through replication, and objective analysis.

  • Approach: Deductive and objective; conclusions are drawn from scientifically controlled testing.

• Qualitative Research:

  • Process: In-depth analysis of few participants for a richer understanding. Techniques include interviews, observations, and case studies.

  • Features: Inductive and subjective; researchers often develop hypotheses during the process rather than beginning with them.

  • Approach: Interpreting human phenomena through patterns or themes.

• Mixed Methods: A research approach collecting, analyzing, and integrating both quantitative and qualitative data in a single study (Creswell, 2013).

The Theoretical Framework and Variables

• Theory: A systematic way of organizing and explaining observations, containing propositions about relationships among phenomena. It is an "imperfect rendering of reality" or a "mental construction."

• Hypothesis: A tentative belief about the relationship between two or more variables, predicting findings if a theory is correct.

• Variable: Any phenomenon that can differ or vary (e.g., IQ scores).

  • Continuous Variable: Placed on a continuum (e.g., degree of optimism, intelligence, shyness, heart surgery recovery rate).

  • Categorical Variable: Comprised of specific groupings or categories (e.g., Australian states, species, having a heart attack).

• Research Example (Optimism and Health):

  • Theory: Pessimism promotes poor health because pessimists ignore self-care and maintain a constant state of physiological alarm.

  • Study: Scheier & Carver (1993) found that optimistic patients undergoing coronary artery bypass operations recover faster than pessimistic patients.

Standardized Procedures and Generalizability

• Standardized Procedures: Ensuring all participants experience similar conditions to ensure results aren't due to variation in the procedure itself.

  • Lumley and Provenzano (2003) Study: Compared students writing about traumatic experiences (experimental group) vs. time management (control group). Both groups wrote for $15-20$ minutes on $4$ consecutive days. Results showed the emotional disclosure group had significantly better grade point averages (GPAs).

• Population: The larger group to whom research findings are applicable.

• Sample: A subgroup representative of the population. Individuals in the sample are called participants or subjects.

• Sampling Bias: Occurs when a sample is not representative, leading to over-representation or under-representation of certain elements.

• Internal Validity: The extent to which methods convincingly test the hypothesis (validity of the design itself).

• External Validity: The extent to which findings can be generalized to real-world situations outside the laboratory.

Objective Measurement: Reliability and Validity

• Measurement: A concrete way of assessing a variable (e.g., using a rating scale of $1-7$).

• Popularity Study (Rodkin et al., 2000):

  • Quantified teacher observations via an $18$-item questionnaire (items like 'popular with girls', 'lots of friends').

  • Identified two types of popular boys: "Model Citizens" (academic, friendly, athletic) and "Aggressive" (good looking, athletic, but striking for aggression).

• Reliability: The ability of a measure to produce consistent results.

  • Retest Reliability: Similar scores for the same individual over time.

  • Internal Consistency: Different ways of asking the same question yield similar results.

  • Interrater Reliability: Different observers/raters produce similar scores for the same individual.

• Validity: The ability of a measure to assess the variable it is intended to assess.

  • Validation Research: Relates a measure to an objective criterion or other validated measures.

  • Example: IQ tests are validated by their ability to predict school performance.

• Test Bias: Exists if mean scores differ systematically between groups and the scores make incorrect predictions in real life.

  • Guenole, Englert, and Taylor (2003): Maori job applicants scored $0.55$ to $1.79$ standard deviations lower than Europeans on cognitive tests due to business terminology knowledge, not lack of ability.

• Error: The discrepancy between the phenomenon as measured and as it really is. Multiple measures serve as a "safety net" to catch measurement errors.

Experimental Research: Logic and Methodology

• Goals of Scientific Approach:

  • Description: Summarizing data and relationships.

  • Prediction: Identifying future outcomes under similar circumstances.

  • Understanding: Identifying causal factors (why events happen).

• Causation: Experiments establish cause and effect by manipulating one variable to see if it causes changes in another.

• Variables in Experiments:

  • Independent Variable ($IV$): The variable manipulated by the experimenter, independent of participant actions.

  • Dependent Variable ($DV$): The response measured to see the effect of the manipulation.

• Harlow & Zimmerman (1959) Attachment Study:

  • Research Question: Is attachment based on food or comfort?

  • Method: Infant monkeys given choice between a wire "mother" (food source) and a cloth "mother."

  • Result: Monkeys preferred the cloth mother regardless of food source, concluding comfort is the basis of attachment.

Steps in Conducting an Experiment

1. Framing a Hypothesis: Predicting relationships between variables (e.g., Bower's hypothesis that mood affects memory quality).

2. Operationalising Variables: Turning abstract concepts into concrete, testable forms (e.g., operationalizing "mood" via hypnosis and "memory" by the number of recalled facts).

3. Developing a Standardized Procedure: Setting up conditions and control groups.

   • Control Group: A group exposed to a neutral condition to provide a baseline.

   • Demand Characteristics: Participants responding in ways they think the researcher wants.

   • Placebo Effect: Participants perceiving improvement simply because they believe a treatment is effective.

   • Blind Studies: Single-blind (participant unaware) or double-blind (both participant and researcher unaware) to prevent bias.

4. Selecting and Assigning Participants:

   • Random Assignment: Essential for internal validity to minimize systematic differences between groups.

   • Confounding Variables: Features that produce effects confused with the $IV$.

5. Applying Statistical Techniques:

   • Descriptive Statistics: Summarizing essential features of the data.

   • Inferential Statistics: Determining if findings are meaningful or due to chance (statistical significance).

6. Drawing Conclusions: Evaluating if the hypothesis was supported and suggesting future research.

Study Example: Test Anxiety (Sansgiry and Sail, 2006)

• Context: Investigation of test anxiety, course load, and time management in pharmacy students ($N = 198$).

• Results:

  • Second-year students perceived the highest course load.

  • Final-year students perceived the lowest.

  • Test anxiety correlated positively with perceived course load (r = 0.24, p < 0.01) and negatively with time management (r = -0.20, p < 0.01).

• Significance Check: Differences in anxiety between second and third-year students were significant (p < 0.05).

Statistical Controversy: The $p$-value ($p$)

• The Problem: Significance levels are heavily influenced by sample size ($N$). A massive sample ($N = 4000$) could find a tiny, clinically meaningless difference (e.g., $0.1\%$ difference in symptoms) to be "statistically significant" (p < 0.0001).

• New Statistics: Movement advocating for techniques beyond $p$-values, including:

  • Effect sizes.

  • Confidence intervals.

  • Meta-analysis.

Limitations and Variations of Experimental Research

• Ethical/Practical Constraints: Complex issues (like divorce or poverty) cannot be experimentally manipulated.

• Quasi-experimental Designs: Share the logic of experiments but lack full control over variables (e.g., no random assignment). Participants are taken "as they are" (based on characteristics like gender or family status).

• Simulation Research: Use of technology like driving simulators (Traffic and Road Safety Group at University of Waikato) to study hazards Safely.

Descriptive Research Methods

• Case Study: In-depth observation of a small group or individual.

  • Uses: Exploring complex/rare phenomena, interpretive (hermeneutic) analysis of meanings (e.g., motivations behind suicide).

  • Limits: Small sample size makes generalization difficult; high risk of observer bias.

• Naturalistic Observation: In-depth observation in a natural setting.

  • Examples: Jane Goodall (apes), Frans de Waal (reconciliation in chimps), Jean Piaget (children's "collective monologues").

  • Limits: Observation can alter behavior (observer effects); cannot manage independent variables.

• Survey Research: Asking large samples about attitudes/behaviors via interviews or questionnaires.

  • Sampling Types:

    • Random Sample: Every member of a population has an equal chance of selection.

    • Stratified Random Sample: Specifies percentages from population categories (age, race, etc.) to ensure proportional representation.

  • Limits: Relies on self-report accuracy; people often show self-presentation bias or misjudge their own attitudes.

Correlational Research

• Premise: Determining the degree to which two or more variables are related to predict one from the other.

• Correlation Coefficient ($r$): Summarizes the relationship from $-1.0$ to $+1.0$.

  • Positive Correlation: Both variables increase or decrease together (e.g., height and weight).

  • Negative Correlation: As one variable increases, the other decreases (e.g., socioeconomic status and high school dropout rates).

  • Zero Correlation: No relationship; one variable predicts nothing about the other (e.g., intelligence and interpersonal trust).

• Correlation Matrix: A table showing correlations across multiple variables.

  • Shiner (2000) Example: Childhood extroversion correlates with later social functioning ($r = 0.35$) but not well with conduct ($r = -0.14$). Agreeableness and achievement motivation are stronger predictors of adult success.

• Causality Warning: Correlation does not equal causation. Relationships may be influenced by a third variable.

Comparison of Research Methods Summary Table