Chs 1-3 Final Review

Producers vs consumers of research - why are both important?

• Producer - used for coursework in psychology, grad school, and working in a research lab

• Consumer - used for psychology classes and your future career; used by reading printed or online news stories based on research

Empiricism

Descartes (1st empiricist); idea that all learning comes from only experience and observation

Explain the theory-data cycle

• theory → hypothesis → data → empiricism

• Theory - general statement about relationship between variables

  • Ex. There’s a relationship between studying and your grades (very non-specific)

  • You don’t test theories, you test hypotheses

• Hypothesis - a testable prediction; can be based on experience and past research

  • Ex. the more you study, the better your grade in the class (can be as specific as you want)

• Data - study observations; either supports or doesn’t support hypotheses; make conclusions (does data support prediction?)

• Empiricism - Descartes (1st empiricist); idea that all learning comes from only experience and observation

Applied vs basic vs translational research (goals and examples of each)

• Basic research - goal is to gather information, expand knowledge

  • Ex. studying how high school students stress levels affect their behavior, causing them to cheat

  • Ex. What parts of the brain are activated when experienced meditators are meditating?

• Translational research - the use of lessons from basic research to develop and test applications to health care, psychotherapy, or other forms of treatment and intervention; represents a dynamic bridge from basic to applied research

  • Ex. In a laboratory study, can meditation lessons improve college students’ GRE scores?

• Applied research - research used to tackle a practical/real-world problem

  • Ex. find ways to reduce stress levels, which will hopefully result in less cheating

  • Ex. Has our school’s new meditation program helped students focus longer on their math lessons?

Explain the difference between: Research (probabilistic) vs experience (no comparison group)

• direct vs indirect experience

• Experience has no comparison group, no control group, and no experimental group

• Experience is confounded and could involve extraneous variables which can influence results, findings, and cause doubt on your results

• Research is probabilistic

direct vs indirect experience

• Direct - ex. Seeing a movie yourself and thinking it’s good

• Indirect - ex. A friend tells you a movies is good; this is more common

extraneous variables vs confounds

• extraneous variables - can affect your results, the researcher is aware of them, they can try to minimize their effect, but you can’t eliminate their effect completely 

  • Ex. a participant gets a promotions before the study (will change their behavior)

• confounds - something that systematically changes with your independent variable

  • Ex. Newspaper reported positive effects of exercise in older adults on their cognitive functions: G1 - attended group exercise class, G2 - no exercise

    • G1 improved cognitive functioning, but had a confounding variable (G1 involved group interaction)

What does it mean when we say research is probabilistic?

findings aren’t expected to explain all cases all the time

Research vs intuition: intuition is ___

biased

In what ways is intuition biased?

• Being swayed by a good story

• Availability heuristic - we think what’s available in our minds is more likely to happen

• Confirmation bias - search for information that confirms our preconceptions

• Bias blind spot - people think they won’t fall prey to biases

Name and define the components of an empirical journal article:

• Abstract - general overview; grabs attention; summary of article

• Introduction - theoretical/empirical background; mentions current study; contains hypothesis

• Method - includes information about participants and procedure; also operationally defines variables

• Results - includes statistics (both numbers and descriptive), charts, and tables

• Discussion - recap results in words, not numbers; explains results and includes alternative explanations and limitations encountered

• References - cite sources

When reading with a purpose, what questions should you ask yourself when reading?

• empirical journal articles - What is the argument? What is the supporting evidence?

• chapters and review articles - What is the argument? What is the supporting evidence?

Variables (manipulated vs measured) vs constants

• Variables - anything that changes in your study

  • Manipulated variable - variable that the researcher purposely changes (often in an experiment); aka predictor variable; think random assignment; often is the independent variable

  • Measured variable - variable that the researcher is looking out for; could come in a questionnaire; ie religion, gender, age (things you can’t change yourself)

• Constants - things that stay the same in the study

Operational vs conceptual definitions (variables) (examples)

• Operational definitions - quantifiable definitions (when possible); definition that gives people enough information to replicate your study if desired

• Conceptual definitions - vague, dictionary definitions; doesn’t give enough information to replicate the study

• Ex. conceptual variables - school achievement; operational variables - self-reports questionnaire, checking records, teacher’s observations, etc

Frequency claims (examples)

• describes a particular rate or degree of a single variable; only 1 measured variable; can NOT say “X caused Y”

  • Ex. 2 out 5 Americans say they worry everyday (Americans are the only measured variable); 72% of the world smiled yesterday (smiling is the only measured variable)

Association claims (correlational)

argues that one level of a variable is likely to be associated with a particular level of another variable; involves at least 2 measured variables; variables that are associated are said to correlate; can predict behavior

name and define the types of associations

• Positive association - as one variable increases, the other variable increases; 0 < r <= 1; positive linear association

  • Ex. romantic partners who express gratitude are 3x more likely to stay together

• Negative association - as one variable increases, the other one decreases; -1 <= r < 0

  • Ex. people who multitask the most are the worst at it

• Zero association - one variable does NOT predict another; r = 0 (about)

  • Ex. a late night dinner is NOT related to childhood obesity

causal claims

2 variables, 1 of which causes the other; 1 manipulated variable (X) and 1 measured variable (Y)

name and define the 4 types of validity

• Construct - measuring what you say you are measuring; must use operational definitions

• External (generalizability) - the ability to generalize/apply findings to a larger population; your sample needs to match your target population (having random samples increases the chances of your sample matching your population)

• Statistical - having an adequate sample size that is representative of your population; the extent to which a study’s statistical conclusions are precise, reasonable and replicable; how well do the numbers support the claim?

• Internal - to make a cause-effect conclusion, a study needs this type of validity; needs random assignment; everyone needs a fair chance to be in the group; only one systematic variable (no confounds)

interrogating frequency claims

must examine construct & external validity (generalizability)

interrogating association claims

must examine construct, external, & statistical validity

• Ex. people who multitask are worst at it

  • construct validity - define how you’re measuring multitasking (ie self-reports and surveys) and how you define they’re “good” at it

  • external validity - sample needs to represent “people”; would need to be a big sample

  • statistical validity - compare the 2 groups and do a statistical test

null vs alternative hypotheses

• Null hypothesis - a statement that is assumed to be true unless the evidence is strong enough to reject it; what we are trying to disprove; H₀

• Alternative hypothesis - a statement that claims there is a statistically significant difference between 2 or more groups; what we are trying to show evidence to support; H₁

Type 1 vs Type 2 error

• Type 1 error - occurs when a researcher rejects a true null hypothesis

• Type 2 error - occurs when a researcher fails to reject a false null hypothesis

interrogating causal claims

most difficult claim to make; needs covariation, temporal sequence, and elimination of 3rd variables (confounds); should have all 4 validities, but internal validity is the most important

What are the 3 steps to supporting a causal claim?

• Covariation - you have to show your 2 variables covary before you can say “X causes Y”; least difficult criteria to meet; 1st step in causal claim; can be shown with a survey

• Temporal sequence - directionality; have to show that 1 variable comes before the other one; “X comes before Y”; 2nd step in causal claim

• 3rd variables (confounds) - get rid of other variables that may have caused “Y”; 3rd step in causal claim; needs an experiment