Psychological Research Overview

Psychological Research - Overview

•  Psychological research is a systematic process used to gather and analyze information about human behavior and mental processes.
•  Research goals include understanding, explaining, predicting, and potentially controlling behavior.
• This process includes specific steps like observation, hypothesis formation, experiments, and analysis.
•  The scientific method is a key approach in psychological research involving systematic study.

Objectives of Psychological Research

•  Introduction to the scientific method.
•  Introduction to conducting psychological research.
•  Exploring ethical principles of research.

Importance of Research

• Every aspect of human life is based on research and exploration.
• Research plays a crucial role in validating claims and ensuring accurate understanding of the world, avoiding misinformation and inaccuracy.
• Scientific research is empirical; relying on objective and tangible evidence that can be validated regardless of the observer.

What is Scientific Research?

• A method for learning about the world through the application of critical thinking and tools like observation, experimentation, and statistical analysis.

Observation

•  Scientific process starts with observations of natural phenomena
•  Observation leads to questions and problems to be addressed.
•  Inductive and deductive reasoning are critical for forming a theory

Theory

•  Well-established explanation of phenomena based on interconnected concepts and propositions.
• Aims to explain, predict, and understand observed patterns and behaviors within a specific domain.
• Usually, supported by multiple studies and considered generally accepted within the field.
• Essential for interpreting and understanding data.

The Process of Scientific Research

Inductive Reasoning

• Drawing a general conclusion from specific observations.
• Example: Observing many fruits grow on trees and concluding that fruits generally grow on trees

Deductive Reasoning

• Making specific predictions based on general principles or premises.
• Example: All living things require energy, ducks are living things; therefore, ducks require energy.

Scientific Method Application

• Scientists use deductive reasoning to form theories and hypotheses.
• Hypotheses are tested through empirical observations.
• Inductive reasoning is used to draw conclusions from observations, potentially leading to new theories, hypotheses, or generalizations.

Research Process

• Prediction: Researchers make specific predictions about outcomes based on the hypothesis; these predictions allow for empirical testing.

Hypothesis Formation

•  Tentative prediction or explanation based on observations and existing knowledge.
•  Serves as a basis for further investigation.
•  A good theory must have a testable prediction or hypothesis.
•  Hypothesis are often "if-then" statements.
•  Hypotheses must be falsifiable; meaning they can be disproven.

Hypothesis

•  All hypotheses must be falsifiable; meaning they can be disproven.
•  Falsifiability means that if a hypothesis is false, some observations or experiments can produce results contradicting the hypothesis.

Experimentation

• Researchers design and conduct experiments to test predictions.
• Experiments manipulate variables while keeping other factors constant, measuring the effect on another variable.
• Variables are elements identified by hypotheses.
• Two main types of variables: independent variable and dependent variable.

Variables

Independent Variable

• Variable that's manipulated or controlled by the experimenter.
• Key differentiator between an experimental group and a control group.
• Factor expected to cause an effect or change in the dependent variable.

Dependent Variable

• The variable measured by the researcher.
• Shows the effect or outcome of changes made to the independent variable.
• The researcher examines this variable to determine the independent variable's influence.

Validity

• Experiment goal is to make sure it's valid.
•  Validity refers to the extent to which a test measures what it is supposed to measure.

Data Collection and Analysis

• Researchers collect data during experiments to assess outcomes and test predictions.
• Statistical techniques are used to evaluate results and determine if they support or reject the hypothesis.

Types of Data Collection

• Primary: Collecting data directly from individuals (e.g., surveys, interviews, observations)
• Secondary: Using existing data from other studies (e.g., government reports, published studies)

Data Collection Methods

• Surveys
• Interviews
• Observation
• Focus groups
• Document analysis
• Social media monitoring

Analyzing the Results

• Data analysis involves statistical techniques that evaluate collected data to interpret its meaning
• Conclusions are drawn based on results and alignment with original research questions and hypotheses.
• Factors considered include statistical significance, potential biases, and research design.

Key Steps to Analyzing Data

• Data Cleaning & Organization: Identifying errors and inconsistencies; Organizing data.
• Descriptive Statistics: Calculating measures like mean, median, mode, and standard deviation, for data summary.
• Inferential Statistics: Applying statistical tests to determine if observed relationships between variables are statistically significant, allowing for generalizations to a larger population.

Interpretation of Results

• Meaning of results interpreted based on context of research questions and established literature.
• Patterns, trends, and relationships within data are identified.
• Conclusions regarding the hypothesis are drawn by researchers.
• If the results support the hypothesis, it may be considered provisionally accepted.
• If the results do not support the hypothesis, the hypothesis may be revised, rejected, or new hypotheses may be developed for further investigation.

Important Considerations when Analyzing Research Results

• Research Design: How the design (e.g., experimental, correlational) affects the interpretation of results.
• Sample Representativeness: Assess if the study reflects the intended population.
• Bias: Identify potential sources of bias in data collection, analysis, and interpretations, and discuss their impact on findings.
• Effect Size: Practical significance of findings (beyond statistical significance)