The process of scientific research in psychology

Planning Research

• Researchers must plan their studies thoroughly.

Variables

Types of Questions to Answer

• WHAT shall we measure?

  • Human characteristics and conditions.

• HOW shall we answer our research question?

  • Specifying methodologies to test predictions.

• WHOM shall we study?

  • Identifying sample population.

• HOW will we find them?

  • Research design and sampling strategies.

• WHAT sort of evidence will we get?

  • Understanding the form of data collected.

Understanding Variables

• Definition of Variables:

  • Concepts that change and can be measured.

  • Representation can be quantitative (e.g., statistics) or qualitative.

• Characteristics of Variables:

  • Not constant; relevant to quantitative psychology.

  • Demonstrates variability:

    • Within individuals over time.

    • Between different individuals.

Examples of Measurable Variables

• Examples include: height, feelings towards significant others, attitudes towards societal issues, extroversion, and anxiety.

Case Study: Distribution of Heights

• Distribution illustrated through numerical data visualization, showing variations among individuals in terms of height.

Relationship Satisfaction Measurement

• Study by Fülöp et al. utilized a single-item measure assessing relationship satisfaction from 1 (not satisfied) to 5 (very satisfied).

Variability in Relationship Satisfaction

Data Overview

• Presented variability for different participants over two different periods.

Conceptual Framework of Variables

• Categorical Variables: Non-numerical and discrete categories (e.g., marital status).

• Measured Variables: Numerical scales reflecting degrees along a variable (e.g., survey ratings).

Measurement Challenges

• Variables like extroversion or anxiety are harder to quantify numerically compared to easily measurable ones like weight or height.

Operational Definitions

• A clear distinction between constructs and their measurements is critical.

  • Example: Aggression can be measured through observation or questionnaires.

Importance of Observations in Research

• The role of naturalistic observations, particularly in identifying bullying behaviors in youth settings, by videotaping playground interactions.

Inclusion Criteria for Samples

• Ensure accurate representation aligned with research goals, establishing clear criteria for participation.

Reliability and Validity in Research

• Reliability: Consistency of measurement results.

  Does the instrument provide consistent measurements/outcomes?

• Validity: Ensuring the instrument measures what it is supposed to.

  Does the instrument measure what it is intended to measure?

Advantages and Limitations of Survey Tools

• Easy measurements but may lack deeper qualitative insights.

• Using polygraphs for lie detection presents reliability but can struggle with validity.

The Importance of Sampling in Research Design

• Sample selection from a population can influence researchers' ability to generalize findings.

• Stratified sampling enhances representation across key demographics.

Research Design Considerations

Structural Framework

• Consider various study designs (cross-sectional, longitudinal, experimental).

• Carefully evaluate how each design supports answering specific research questions.

Differentiating Research Types

Cross-Sectional Studies

• Variables are measured at one time point but,

• causality cannot be definitively established.

Longitudinal Studies

• Measure outcomes over multiple time points,

• providing clearer insights into change and causality.

  Concept of variance: the average amount that the data varies from the mean. If two variables are related, then changes in one variable should be met with similar changes in the other variable.
  

• Positive covariance: as one variable deviates from the mean, the other variable deviates in the same direction

• Negative covariance: as one variable deviates from the mean the other deviates from the mean in the opposite direction.

Randomized Experiments

• Facilitate control over confounding variables through random assignment.

Formulating Hypotheses

Causal vs. Non-Causal Hypotheses

• Reflect on relationship expectations between variables, clearly stating the nature (directional/non-directional).

Statistical Hypothesis Testing

• Null hypothesis indicates no relationship while alternate hypothesis suggests a relationship exists.

• Consider one-tailed vs. two-tailed tests and the importance of pre-testing selections.