The Research Enterprise in Psychology - Introduction to General Psychology | PSYC 1101, Study notes of Psychology

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Chapter 2: The Research

Enterprise in Psychologyp y gy

The Scientific Approach: A Search for Laws

• Basic assumption: events are governed by some lawful order
• Goals:
  • Measurement and description
  • Understanding and prediction
  • Application and control

Figure 2.1 Theory construction

Figure 2.2 Flowchart of steps in a scientific investigation

The Scientific Method: Terminology

• Operational definitions are used to clarify precisely what is meant by each variable
• Participants or subjects are the organisms whose behavior is systematically observed in a study
• Data collection techniquesq allow for empirical observation and measurement
• Statistics are used to analyze data and decide whether hypotheses were supported

The Scientific Method: Terminology

• Findings are shared through reports at scientific meetings and in scientific journals – periodicals that publish technical and scholarly material - Advantages of the scientific method: clarity of communication and relative intolerance of errorf
• Research methods : general strategies for conducting scientific studies

Table 2.1 Key Data Collection Techniques in Psychology

Experimental Research: Looking for Causes

• Experiment = manipulation of one variable under controlled conditions so that resulting changes in another variable can be observed - Detection of cause-and-effect relationships
• II ndependent variable (IV)d d t i bl (IV) = variablei bl manipulated
• Dependent variable (DV) = variable affected by manipulation - How does X affect Y? - X = Independent Variable, and Y = Dependent Variable

Experimental and Control Groups: The Logic of the Scientific Method

• Experimental group
• Control group
  • Random assignment
  • Manipulate independent variable for oneManipulate independent variable for one group only
  • Resulting differences in the two groups must be due to the independent variable
• Extraneous and confounding variables

Figure 2.5 The basic elements of an experiment

Experimental Designs: Variations

• Expose a single group to two different conditions
  • Reduces extraneous variables
• Manipulate more than one independent variable
  • Allows for study of interactions betweenAll f d f i i b variables
• Use more than one dependent variable
  • Obtains a more complete picture of effect of the independent variable

Figure 2.6 Manipulation of two independent variables in an experiment

Strengths and Weaknesses of Experimental Research

• Strengths:
  • conclusions about cause-and-effect can be drawn
• Weaknesses:
  • artificial nature of experiments
  • ethical and practical issues

Descriptive/Correlational Methods: Looking for Relationships

• Methods used when a researcher cannot manipulate the variables under study - Naturalistic observation - Case studies - Surveys
• Allow researchers to describe patterns of behavior and discover links or associations between variables but cannot imply causation

Figure 2.10 Comparison of major research methods

Statistics and Research: Drawing Conclusions

• Statistics – using mathematics to organize, summarize, and interpret numerical data - Descriptive statistics : organizing and summarizing data - Inferential statistics : interpreting data and drawing conclusions

Descriptive Statistics: Measures of Central Tendency

• Measures of central tendency = typical or average score in a distribution
• Mean : arithmetic average of scores
• Median : score falling in the exact center
• Mode : most frequently occurring score
  • Which most accurately depicts the typical?

Figure 2.11 Measures of central tendency

Descriptive Statistics: Variability

• Variability = how much scores vary from each other and from the mean - Standard deviation = numerical depiction of variability - High variability in data set = high standard deviation - Low variability in data set = low standard deviation

Figure 2.12 Variability and the standard deviation

Descriptive Statistics: Correlation

• When two variables are related to each other, they are correlated.
• Correlation = numerical index of degree of relationshiprelationship - Correlation expressed as a number between 0 and 1 - Can be positive or negative - Numbers closer to 1 (+ or -) indicate stronger relationship

Figure 2.14 Interpreting correlation coefficients

Correlation: Prediction, Not Causation

• Higher correlation coefficients = increased ability to predict one variable based on the other - SAT/ACT scores moderately correlatedy with first year college GPA
• 2 variables may be highly correlated, but not causally related - Foot size and vocabulary positively correlated - Do larger feet cause larger vocabularies? - The third variable problem

Figure 2.15 Three possible causal relationships between correlated variables

Inferential Statistics: Interpreting Data and Drawing Conclusions

• Hypothesis testing: do observed findings support the hypotheses? - Are findings real or due to chance?
• Statistical significance = when the probability that the observed findings are due to chance is very low - Very low = less than 5 chances in 100/. level

Evaluating Research: Methodological Pitfalls

• Sampling bias
• Placebo effects
• Distortions in self-report data:p
  • Social desirability bias
  • Response set
• Experimenter bias
  • the double-blind solution

Figure 2.16 The relationship between the population and the sample

Ethics in Psychological Research: Do the Ends Justify the Means?

• The question of deception
• The question of animal research
  • Controversy among psychologists and they g p y g public
• Ethical standards for research: the American Psychological Association - Ensures both human and animal subjects are treated with dignity

Figure 2.17 Ethics in research