Psychology CH 2 - Lecture notes Chapter 2 PDF

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Why Research? ● It reduces error in production of knowledge ● Control for human biases ○ Heuristics: mental shortcuts to thinking ■ Representative Heuristic: used when making judgement about the probability of an event under uncertainty ■ Availability Heuristic: Immediate example that comes to mind when making a decision ( inaccuracy can come from the fact that the examples differ among people based on their individual past experiences) ○

Hindsight Bias: “ I knew it all along”

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Overconfidence: overestimating your ability to predict events correctly

Methods of Research Method Naturalistic Observation

Description Observation of an organism in their natural environment

Advantage

Disadvantage

High external validitywe can generalize our findings to the real world

Low internal validityunable to draw a cause and effect inferences

Provides rich description of behaviour

Can’t generalize beyond the specific context No control over variables

Case Studies

Examine one person or a small number of people over an extended period of time

Provide existence proof Allow us to study rare or unusual phenomena

Unable to determine cause-and-effect relationships Researcher bias Poor generalizability

Can offer insight for later systematic testing Self Report

Asses a variety of characteristic, such as personality traits, mental illnesses and interest

They are easy to administer They information

Positive impression management (tendency to make ourselves look better

comes directly from the person themselves

than we are) Malingering (tendency to make ourselves appear psychologically disturbed with aim of achieving a clear-cut personal goal) Unreliable

Uses to measure people’s opinions and attitudes (questionnaires)

Large pool sample = better generalization of results to broader population

Unrepresentative samples can lead to poor generalization

Correlational Designs

Research design that examines the extent to which two variables are associated

Helps us control problems of illusion correlation (perceiving a correlation where none exists)

Doesn’t necessary allow us to infer causation

Experimental Designs

Manipulate an independent variable, in order to see if it has an effect on a dependent variable

Surveys

Allows us to infer causation

Numerous pitfalls ( Placebo Effect, Nocebo Effect, Experimenter Expectancy Effect, Demand Characteristics)

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Ratings: questionnaires rating individuals ○ Halo Effect: Rating someone better overall because they like one aspect of that person ○ Horns Effect: Rating someone poorly overall because they dislike one aspect of that person ○ Leniency Effect: Tendency to rate someone better because you know them personally ○ Central Tendency: The tendency to pick central rating across the board

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Correlation Design: Measure a number of variables, and then look for relationships among those variables ○ Correlations can be positive, zero, or negative. ■ Positive Correlation- The value of one variable changes, the other goes in the same direction Ranges 0.0 to +1.00

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Zero Correlation- The variables don’t go together at all Negative Correlation- Value of one variable changes, and the other goes in the opposite direction Ranges -0.0 to -1.00

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Pitfalls of Experimental Design ○ Placebo Effect: improvements resulting from the mere expectation of improvement. ○

Nocebo Effect: harm resulting from the mere expectation of harm

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Experimenter Expectancy Effect: occurs when researcher’ hypotheses lead them to unintentionally bias the outcome of the study

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Demand Characteristics: When participants picks up cues from an experiment that allows them to generate guesses regarding experiments’ hypotheses. This is a problem because when participants think they know how experimenter wants them to act, so they may alter their behaviour according.

Ethical Issues in Research Design ● Informed Consent: Must tell subjects what they are getting into before asking them to participate ● Debriefing: Full debriefing be performed that the conclusion of the research, which means that researchers must inform participants what the study was about Statistics: Application of mathematics to describing and analyzing data 1. Descriptive Statistics: Describes data a. Central Tendency: Gives us a sense of the “ central” score in our data set or where the group tends to cluster i. Mean (Average): The total score divided by the number of people ii. Median: Middle score of our data set, this by done by lining up our scores in order and finding the middle one iii. Mode: Most frequent score in our data set b. Variability ( Dispersion): Gives us a sense of how loosely or tightly bunched the scores are i. Range: Difference between the highest and lowest scores ii. Standard Deviation: Is the average amount that individuals data points differ from the mean. It is used to depict variability. 2. Inferential Statistics: Allows us to determine how much we can generalize findings from our sample to the full population a. Statistical Significance: Figuring out whether the difference we’ve observed in our sample is a believable one (certain that the results didn’t

just happen by chance but that the results are due to a real relationship among the variables) b. Practical Significance: The usefulness of the data results in the realworld...