Psychological Research Methods and Statistics

Research Designs and Methodologies in Psychology

• Overview of Research Designs:

  • Psychologists utilize descriptive, correlational, experimental, and other research designs to observe, measure, and explain human and animal behavior.

  • Statistics serve as the tools that allow researchers using these designs to measure variables and accurately interpret their results.

• Detailed Comparison of Primary Research Approaches:

  • Descriptive Research Method:

    • Basic Purpose: To observe and record behavior.

    • How Conducted: Executed by conducting case studies, naturalistic observations, or surveys.

    • Factor Manipulation: Nothing is manipulated.

    • Weaknesses: Features no control of variables; single cases may be misleading.

  • Correlational Research Method:

    • Basic Purpose: To detect naturally occurring relationships and assess how well one variable predicts another.

    • How Conducted: Executed by collecting data on two or more variables with no manipulation.

    • Factor Manipulation: Nothing is manipulated.

    • Weaknesses: Cannot specify cause and effect.

  • Experimental Research Method:

    • Basic Purpose: To explore cause and effect.

    • How Conducted: Executed by manipulating one or more factors and using random assignment.

    • Factor Manipulation: The independent variable(s) are manipulated.

    • Weaknesses: Sometimes not feasible; ethical limitations may restrict application.

• Understanding Everyday Behavior:

  • Focus of Psychological Science: Psychological science focuses less on specific behaviors than on revealing general principles that help explain many behaviors.

  • Group Predictions vs. Individual Outcomes: Although psychological principles may help predict behaviors for groups of people, they more faintly predict behavior for an individual in any given situation.

  • Vocabulary Example: Knowing students' ages may clue an observer to their average vocabulary level, but individual students' word power will vary.

Correlation, Illusory Correlation, and Regression Toward the Mean

• Fundamentals of Correlation:

  • Variable: Defined as anything that can vary and is feasible and ethical to measure.

  • Correlation: Defined as a measure of the extent to which two factors vary together, and thus of how well either factor predicts the other.

  • Correlation Coefficient: Defined as a statistical index of the relationship between two things (from $-1.00$ to $+1.00$).

• Assessing Correlational Range and Strength:

  • Numerical Range: A correlation coefficient can range from $-1.0$ to $+1.0$ and reveals the extent to which two things relate.

  • Rule of Strength: The closer the score gets to $-1$ or $+1$, the stronger the correlation.

• Scatterplots and Visual Interpretation:

  • Scatterplot: Defined as a graphed cluster of dots, each of which represents the values of two variables.

  • Direction of Relationship: The slope of the points suggests the direction of the relationship between the two variables.

  • Strength of Relationship: The amount of scatter suggests the strength of the correlation.

• Illusory Correlation and Perception of Personal Control:

  • Illusory Correlation: Defined as perceiving a relationship where none exists, or perceiving a stronger than actual relationship.

  • Illusion of Control: Illusory correlations can feed an illusion of control, which is the fancy that chance events are subject to human personal control.

• Regression Toward the Mean:

  • Regression Toward the Mean: Defined as the tendency for extreme or unusual scores or events to fall back (regress) toward the average.

  • Explaining Behavioral Normalization: When a fluctuating behavior returns to normal, explanations for why it does so are probably wrong, as regression toward the mean is likely at work.

Descriptive Statistics and Data Measurement

• The Need for Statistics:

  • Function in Research: For psychologists using descriptive, correlational, experimental, and other research designs, statistics are the tools that allow them to measure variables and then interpret results.

  • Skepticism of Undocumented Numbers: Doubt big, round, undocumented numbers. If individuals want to emphasize a problem, they will be motivated to guess high; if they want to minimize a problem, they will guess small.

• Fundamentals of Descriptive Statistics:

  • Descriptive Statistics: Defined as numerical data used to measure and describe characteristics of groups. Includes measures of central tendency and measures of variation.

  • Visual Scale Inspection: When viewing graphs, read the scale labels and note their range.

  • Histogram: Defined as a bar graph depicting a frequency distribution.

• Measures of Central Tendency:

  • Analytical Caveat: Always note which measure of central tendency is reported. If it is a mean, consider whether a few atypical scores could be distorting it.

  • Mode: Defined as the most frequently occurring score(s) in a distribution.

  • Mean: Defined as the arithmetic average of a distribution, obtained by adding the scores and then dividing by the number of scores.

  • Median: Defined as the middle score in a distribution; half the scores are above it and half are below it.

  • Skewed Distribution: Defined as a representation of scores that lacks symmetry around their average value.

• Measures of Variation:

  • Range: Defined as the difference between the highest and lowest scores in a distribution.

  • Standard Deviation: Defined as a computed measure of how much scores vary around the mean score.

  • Normal Curve: Defined as a symmetrical, bell-shaped curve that describes the distribution of many types of data; most scores fall near the mean and fewer and fewer near the extremes.

Inferential Statistics and Evaluating Observed Differences

• Inferential Statistics:

  • Inferential Statistics: Defined as numerical data that allow one to generalize—to infer from sample data the probability of something being true of a population.

• Reliability Criteria for Observed Differences:

  • Condition $1$ (Representative Samples): Representative samples are better than biased samples. Smart thinkers are not overly impressed by generalizations based on a few unrepresentative cases.

  • Condition $2$ (Variability): Less-variable observations are more reliable than those that are more variable.

  • Condition $3$ (Sample Size): More cases are better than fewer.

• Statistical Significance:

  • Definition and Scope: Statistical significance indicates the likelihood that a result could have happened by chance.

  • Practical Limitation: Statistical significance does not say anything about the importance of the result.

Ethics and Values in Psychological Science

• Studying and Protecting Animals:

  • Purpose of Animal Research: Many psychologists study nonhuman animals to understand how different species learn, think, and behave; psychologists also study animals to learn about people.

  • Shared Psychological Processes: Humans are complex, but some of the same processes by which humans learn are present in animals.

• Studying and Protecting Human Participants:

  • Informed Consent: Defined as giving potential participants enough information about a study to enable them to choose whether they wish to participate.

  • Debriefing: Defined as the postexperimental explanation of a study, including its purpose and any deceptions, to its participants.

• Values in Psychological Science:

  • Perceptual Bias: Values can color observations and interpretations; sometimes people see what they want or expect to see.

  • Descriptive Language: The words used to describe human traits and tendencies can reflect underlying values.

  • Application and Limitations of Psychology: A science of behavior and mental processes can help people reach their goals. Psychology cannot address all of life's great questions, but it speaks to some mighty important ones.