105 MC CONT 1 ETHICS

Chapter 2: Methods in Psychology

• This chapter outlines methods used in psychology, including empiricism, observation methods, explanation methods, critical thinking, and ethics.

Empiricism

• Scientific method: A set of principles guiding the appropriate relationship between ideas and empirical evidence.
• Empiricism: The belief that accurate knowledge is gained through observation, a crucial component of the scientific method.
• Dogmatism: The tendency to cling to one's beliefs. Euclid and Ptolemy's view on vision contrasted with al-Haytham's perspective.

The Scientific Method

• Procedure: Employing empirical evidence to establish facts.
• Theory: An explanation of a natural phenomenon; it cannot be definitively proven correct.
• Hypothesis: A testable prediction generated from a theory.

The Art of Looking

• Empirical method: Set of rules and techniques for observation.
• Observation difficulties: People's complexity, variability, and reactivity make them challenging to study.
• Kinds of methods: Methods for observation and explanation are categorized. The Muybridge photographs are an illustration. Examples include methods of observation and explanation.

Methods of Observation

• Observation: Using one's senses to learn about events and objects.
• Everyday observation limitations: Inconsistent and incomplete observations.
• Scientific techniques for accurate observation: Measurement and description are examples of methods for overcoming limitations of everyday observation.

What to Measure and How

• Operational definition: A description of a property in measurable terms.
• Construct validity: A measure of how well a thing being measured represents the property being studied.
• Reliability: A measure's consistency when measuring the same thing.
• Power: A measure's ability to detect conditions specified in its operational definition; key features are reliability and power for good detectors and instruments in measurement.
• Measurement Steps: Define the property, and then detect the property through the use of a reliable and powerful instrument for the property

Power and Reliability at the Olympics

• Reliability in judging performance: This is evident in sports like ice dancing, where judges consistently rate relevant qualities.
• Example of power in measurement: Usain Bolt's speed records highlight the importance of precise measurement in sports, as inaccurate measurement can lead to misleading conclusions

Demand Characteristics

• Definition: Aspects of an observational setting inducing people to behave as they think others want or expect.
• Impact on measurement: Demand characteristics make accurately measuring behavior difficult.

Naturalistic Observation

• Definition: Observing people unobtrusively in their natural environments.
• Practical Limitations: Some events or behaviors do not occur naturally or can only be observed via direct interaction.
• Potential for bias: Observer bias.

How Do Researchers Measure "Pace of Life"?

• Naturalistic observation method: Calculating average walking speeds in various cities.

Techniques for Avoiding Demand Characteristics

• Privacy: Conceal the observer's presence to avoid participants' altering their behavior.
• Control: The ability to manage the environment in such a way that the outcome of an experience is unlikely to be affected by extraneous factors.
• Unawareness: Design research so the participants are unaware of the true purpose of the study

Observer Bias

• Definition: Observers' expectations influencing their understanding and observations.
• Causes: Expectations influencing observations and potentially, reality
• Mitigation: Using double-blind studies

An Example of Observer Bias

• Illustration: Robert Parker's wine ratings and their potential impact on participants' evaluations.

Description

• Population: The complete set of people being studied (often immeasurable).
• Sample: A subset of people drawn from a population.
• Data analysis techniques: Psychologists use graphic representations and descriptive statistics to analyze data.

Graphic Representations

• Frequency distributions: Visual representations of how frequently measurements of a property occur.
• Normal distribution: Frequently used representations in which measurements cluster around the middle.

Frequency Distributions (Happiness)

• Graphical representation: Illustrating happy levels revealed using a frequency distribution with data on reported level of happiness.

Descriptive Statistics

• Central tendency: Identifying the center or midpoint of a data set using measures like mode, mean, and median.
• Skewed distribution: Data skewed away from the middle.

Calculating Descriptive Statistics

• Mode: Represents the most frequent value.
• Mean: The average of values; calculating it involves summation of values and division by the number of values present.
• Median: The middle value when values are arranged sequentially.
• Example: calculations illustrating different descriptive statistics.

Different Shaped Distributions

• Examples of positively skewed, negatively skewed, and normal distributions.

Variability

• Variability: A measure of how widely dispersed measurements are from each other.
• Range: Difference between the largest value and the smallest value in a data set.
• Standard deviation: The average difference between each measurement in a distribution and the overall mean of that distribution.

Distributions Can Differ in Variability or Central Tendency

• Visualization illustrating differences in variability or central tendency.

Methods of Explanation

• Correlation: A relationship between variables, where changes in one variable reflect related changes in another variable.
• Variable: A property that can have different values.

Hypothetical Data Showing Relationship

• Illustrative data showing a correlation between sleep hours and number of countries.

Synchronized Patterns of Variation

• Recognizing patterns: Identifying synchronized variations in variables.
• Predictive value: Using the correlation between variables for predictions.

Measuring Direction and Strength of a Correlation

• Correlation direction: Positive or negative.
• Correlation strength: Ranges from -1.0 (strong negative) to +1.0 (strong positive), with 0 indicative of no relationship (no correlation).
• Correlation coefficient (r): A measure of the strength and direction of a correlation.

Graphing Correlations

• Visualizing correlations using graphs (illustrative of perfect positive, perfect negative, and no correlation). Correlations may be positive (high correlation), negative (low correlation), or nonexistent (no correlation), and this is illustrated using graphic plots.

Positive Correlations of Different Strengths

• Illustrative graphs showing varying strengths of positive correlations.

Causation and Interpretation

• Causality limitations: Correlations, while describing synchronization, do not imply causality.
• Third-variable Problem (Z): A third variable (hidden) may cause both observed variables.

Causes of Correlation

• Potential reasons for correlation: Direct relationship between variables, inverse relationship between variables, or influence of a third variable.

Experimentation

• Technique: A method for establishing cause-and-effect relationships.
• Elimination of differences: Comparing groups to pinpoint factors uniquely influencing the outcome.

Three Steps of Experimentation

• Manipulate: Changing an independent variable to create conditions (e.g., creating experimental and control groups).
• Measure: Evaluating the effect of the changed independent variable on the dependent variable.
• Compare: Analyzing differences in the outcome/dependent variable results across conditions/groups.

Manipulation: Making Conditions Different

• Actively changing a variable's value to establish its causal impact.

Random Assignment

• Randomly assigning participants to different groups in an experiment.
• Avoiding self-selection bias, allowing comparison of only the manipulated variable across groups

Statistical Testing

• Evaluating random assignment results: Measuring the statistical significance (e.g., lower than a 5% likelihood of chance results).
• Statistical significance: Results are statistically significant if the probability of obtaining similar results by chance is less than 5%..

The Real World: Unlikely Coincidences

• ESP beliefs: Survey results about the prevalence of belief in extrasensory perception (ESP).
• Misinterpreting coincidences: People often underestimate the likelihood of coincidences.

Drawing Conclusions

• Internal validity: An experiment's ability to show causal connections.
• External validity: An experiment's ability to generalize findings to other situations/populations.

The Generalizability Restriction

• Case method: A method of gathering scientific knowledge from a single individual.
• Random sampling: Ensures every population member has an equal chance of selection.
• Generalizing results: Drawing conclusions about a larger population from observations of a sample.
• Nonrandom sampling: An acceptable method when sample similarity to the population doesn't matter or is a reasonable starting point.

Is Nonrandom Sampling a Fatal Flaw?

• Two reasons why nonrandom sampling isn't invalid: Sometimes sample representation doesn't matter or may be a reasonable starting point.

The Reliability Restriction: Replication and Error Types

• Replication: Conducting a similar experiment again to check for consistency.
• Type I error: Concluding cause-and-effect relationships that do not exist.
• Type II error: Concluding absence of cause-and-effect relationships when they do exist.

Other Voices: Psychology Not in Crisis

• Replication failures: Addressing whether failure to replicate experiments is a common issue in science.

Thinking Critically About Evidence

• Critical thinking: Asking questions to assess the evidence's validity and unbiased interpretation.
• Evidence assessment: Evaluating evidence to understand the whole truth, not just the obvious.

We See What We Expect and Want to See

• Cognitive biases: Exploring how expectations can influence how evidence is interpreted.
• Skeptical stance: Scientists' effort to increase observation accuracy and rigor.

Hours Spent Partying or Studying

• Analyzing presented bar graphs of student reported time spent partying/studying at Canadian universities.

Ethics in Psychological Science

• The Tri-Council Policy Statement (TCPS): A set of principles guiding ethical research involving human participants. Principles include respect for persons, concern for welfare, and justice.
• Informed consent: Participants must freely agree to participate after understanding the research.
• Freedom from coercion: Participants' agreement to participate should not be coerced.
• Protection from harm: Research should maximize benefits and reduce risks to participants.
• Risk-benefit analysis: Evaluating risks and potential benefits to participants.
• Deception: In some cases, withholding information if absolutely essential to avoid influencing outcomes.
• Debriefing: Providing information to participants after the research.
• Confidentiality: Protecting participants' information.

Respecting Animals

• Canadian Council on Animal Care (CCAC): Guidelines for ethical use and care of animals. Standards of replacement, reduction, and refinement of animals in research are described.

Respecting Truth

• Honour System: Psychological research operates on a system of honesty.
• Truthful reporting: Data and results should be accurately reported.
• Ethical credit allocation: Participants' and researchers' contributions should be acknowledged accordingly.
• Shared data: Data should be accessible to other researchers and the scientific community.