PSYCHOLOGY 1004 EXAM 1

Producer of Research

the scientists who design and conduct studies, analyze data, and publish findings. They create new knowledge in psychology

Consumer of Research

read and evaluate research to apply it to their work or lives. This includes practitioners (therapists, teachers), policymakers, and informed citizens. Most psychology students are primarily consumers who need to critically evaluate research claims.

Theory Data Cycle Steps

1. theory

2. research questions/hypothesis

3. research design

4. hypothesis testing

5. evaluate data

6. theory refinement

supported by data

the theory and data should be consistent - more supporting evidence = stronger theory

Falsifiable

the possibility that an idea, hypothesis, or theory can be proven wrong by observation or experiment

Parasimonous

Simpler explanations are preferred over complex ones when both explain the data equally well(Occam's Razor)

basic research

pure science that aims to increase the scientific knowledge base

example: studying how neurons communicate to understand memory formation

applied research

research that attempts to develop new or improved products

example: test whether a new therapy reduces PTSD symptoms in veterans

Universalism(merton's norms)

scientific claims are evaluated according to their merit, independent of the researcher's credentials or reputation. Good evidence is good evidence regardless of who produces it.

Communality (merton's norms)

scientific knowledge belongs to everyone. Researchers share findings openly so others can build on them

Disinterestedness (merton's norms)

Scientists should be unbiased and objective, not driven by personal gain. They follow data wherever it leads, even if it contradicts their predictions

Organized skepticism (merton's norms)

scientists critically evaluate all claims, including their own. Peer review and replication embody this norm

Empirical journal articles

Written by researchers for researchers

Peer-reviewed (experts evaluate before publication)

Include full methodological details

Present data, statistics, and limitations

Longer, technical language

Primary sources (original research)

Popular journalism

Written by journalists for general public

Not peer-reviewed

Simplified explanations, fewer details

May omit limitations or nuance

Shorter, accessible language

Secondary sources (report on others' research)

May sensationalize or misrepresent findings

Abstract - scientific article

Brief summary (150-250 words) of the entire study: purpose, method, key results, and conclusions. Helps readers decide if the full article is relevant

Introduction - scientific article

Provides background, reviews previous research, explains the theory being tested, and states the research question/hypothesis. Moves from general to specific

Method - scientific article

Detailed description of how the study was conducted:

Participants - Who participated, how many, demographics

Materials - What measures/instruments were used

Procedure - Step-by-step what happened

Design - Type of study (experiment, correlation, etc.)

Results - scientific article

Reports statistical analyses and findings, often with tables and graphs. States whether hypotheses were supported.

Discussion - scientific article

Interprets the results, explains what they mean, discusses limitations, and suggests future research. Moves from specific back to general

References - scientific article

Complete list of all sources cited

Review Papers (Narrative Reviews)

Authors summarize and synthesize existing research on a topic qualitatively. They describe patterns, contradictions, and gaps in the literature. Subjective interpretation by authors.

Meta-Analyses

a systematic method of evaluating statistical data based on results of several independent studies of the same problem.

Experience/Anecdote

limitations: Confounds (other factors mixed in), lack of comparison group, individual differences, selective memory

example: "I took vitamin C and didn't get sick" ignores that you might not have gotten sick anyway

Authority

limitations: Systematic biases (see below), overconfidence, can't test causation

example: "It just makes sense that..." isn't the same as empirical evidence

availability heuristic

We judge frequency/probability based on how easily examples come to mind. Vivid, recent, or emotional events seem more common than they are.

Example: People overestimate shark attack risk after seeing news coverage

present bias

We notice when both things we're looking for are present, but ignore other combinations. Leads to illusory correlations.

Example: Noticing when it rains after you wash your car, but not tracking all the times it doesn't rain after washing

confirmation bias

We seek out and pay more attention to information that confirms what we already believe, and dismiss contradictory evidence.

Example: Only reading articles that support your political views

bias blind spot

We recognize bias in others but believe we ourselves are objective and rational. Everyone thinks they're above average in avoiding bias.

measured variables

Variables that are observed and recorded as they naturally occur. Researchers assess them but don't control them.

Examples: height, IQ score, depression symptoms, reaction time

manipulated variable

Variables that researchers control by assigning participants to different levels/conditions. Used in experiments.

Examples: drug vs. placebo, 4 hours vs. 8 hours sleep, type of therapy received

Constants

Factors that are held steady for all participants to prevent them from affecting results.

Examples: same room temperature, same time of day, same instructions

conceptual variable

The abstract concept you're interested in studying.

Examples: "happiness," "intelligence," "stress," "aggression"

operational definition

The specific, concrete way you measure or manipulate that concept in your study.

Example for "happiness":

- Conceptual: subjective well-being

- Operational: score on the 20-item Positive and Negative Affect Schedule (PANAS)

Example for "aggression":

- Conceptual: hostile behavior intended to harm

- Operational: number of loud noise blasts given to another participant

frequency claims

Describe a single variable and how common or frequent it is in a population.

- Structure: "X% of people..." or "The rate of X is..."

- Examples: "40% of college students experience anxiety," "The average person sleeps 7 hours per night"

- Key words: percentage, prevalence, majority, rate, typical

association claim

Describe a relationship between two variables (they covary together).

- Structure: "X is related to Y" or "X goes with Y"

- Examples: "Students who sleep more get better grades," "Exercise is linked to lower depression"

- Key words: is related to, is associated with, linked to, correlated with, predicts, goes with

- Note: Association ≠ Causation!

casual claims

Claim that one variable causes changes in another.

- Structure: "X causes Y" or "X leads to Y"

- Examples: "Sleep deprivation causes impaired judgment," "Therapy reduces anxiety symptoms"

- Key words: causes, leads to, decreases, increases, affects, influences

- Requires experimental evidence

construct validity

How well do the operational definitions measure the conceptual variables?

- Questions to ask: Is this a good measure of the concept? Does it have measurement error? Is it reliable?

- Example: Does a multiple-choice test really measure "intelligence" or just test-taking skill?

- Relevant for: ALL claims

statistical validity

What is the strength and accuracy of the statistical relationship?

- Questions to ask: How strong is the effect? Is it statistically significant? What's the effect size? Could it be by chance?

- Relevant for: Association and causal claims

internal validity

Can we be confident that the independent variable (not some confound) caused changes in the dependent variable?

- Questions to ask: Are there alternative explanations? Were there confounds? Was it a well-controlled experiment?

- Relevant for: Causal claims only

external validity

How well do the findings generalize to other populations, settings, or times?

- Questions to ask: Would this work with different people? In different contexts? At different times?

- Relevant for: Frequency claims, and when you want to apply findings broadly

Covariance(correlation)

The variables must be related. When one changes, the other changes too.

- Example: People who exercise more DO have lower depression scores

- Can test with: Correlational studies or experiments

temporal precedence

The cause must come before the effect in time.

- Example: Exercise must happen BEFORE depression decreases (not the opposite)

- Can test with: Longitudinal studies or experiments

internal validity(no confounds)

There must be no plausible alternative explanations for the relationship.

- Example: It's not that healthier people (who are already less depressed) are more able to exercise

- Can test with: Only experiments with random assignment truly establish this