Research Methods Final Exam Part 2

Front 1

Theory- Data

Back 1

-The collection of data to test, change, or update theories
- Ex: the contact comfort theory

Front 2

Peer Review

Back 2

-Editor receives a manuscript and sends it to 3-4 experts on the subject and is told its virtue and flaws and is decided if to come out in journal
-View: rejection, revision, acceptance

Front 3

Basic-Applied

Back 3

- Basic- goal is to simply enhance the general body of knowledge
- Applied- done with a practical problem in mind

Front 4

Journal-to-Journalism

Back 4

- How it goes from scientific to general public on advice columns and such
- Note: accuracy changes throughout cycle

Front 5

What makes a good theory?

Back 5

 Supported by data
 Falsifiable
 Parsimonious

Front 6

Empiricism

Back 6

The collection of data and using it to develop, support or challenge a theory
Weight of the evidence

Front 7

Comparison groups

Back 7

- Enables you to compare what would happen both with and without the thing you are interested in
- Dr. Rush’s blood letting
- Note: Personal experience has no comparison group, only with a systematic comparison

Front 8

Confounds

Back 8

Occurs when you think one thing caused an outcome but in fact other changes occurred too and don’t know the cause

Front 9

Biases of Intuition

Back 9

 Thinking the easy way
 Present/ Present bias
 The pop-up principle
 Thinking what we want
 Conformity hypothesis testing
 Cherry picking the evidence

Front 10

Thinking the easy way

Back 10

Easier to believe a “good story” than a complicated or unfamiliar; makes sense

Front 11

Present/ Present bias

Back 11

Related to need to compare we focus on the positive instances more than the negative ones

Front 12

The pop-up principle

Back 12

Things that come to mind readily tend to guide our thinking

Front 13

Thinking what we want

Back 13

- We don’t want to challenge preconceived notions and seeing what they want
- Also by asking biased questions

Front 14

Conformity hypothesis testing

Back 14

Asking questions that confirm the hypothesis

Front 15

Cherry picking the evidence

Back 15

We seek and accept only the evidence that supports what we think

Front 16

Measured Variable

Back 16

- A variable in a study whose levels are observed and recorded
- Dependent Variable

Front 17

Manipulated Variable

Back 17

- A variable in an experiment that researchers control by assigning participants to its different levels
- Independent Variable

Front 18

Three Claims

Back 18

 Frequency Claim
 Association Claims
 Casual Claims

Front 19

Frequency Claim

Back 19

- Describe a particular rate or level of something
- Ex. More than 2 million US teens are depressed
- They focus on one variable
- Always measured, not manipulated

Front 20

Association Claims

Back 20

- Argues that one level of variable is likely to be associated with a particular level of another
- Ex. Belly fat linked to dementia, study shows
- Correlation
- Measured, not manipulated

Front 21

Casual Claims

Back 21

- Argues that one of these variables is responsible for changing the other
- Ex. Music lessons enhance IQ
- Can be positive, negative, or curvilinear
- Use causal language: cause, enhance, curb.
- Relationship cannot be zero must have a correlation
- Must show A first than B
- Must establish no other explanation

Front 22

The Four Validities

Back 22

 Construct Validity
 External Validity
 Statistical Validity
 Internal Validity

Front 23

Construct Validity

Back 23

- How well the variable in the study are measured or manipulated
- Are the operational variable s used in the study a good approximation of the constructs of interest?

Front 24

External Validity

Back 24

- The degree to which the results of the study generalize to some larger population (do the results from this sample of children apply to all US school children), as well as to other situations (do their results based on this type of music apply to other types of music?)

Front 25

Statistical Validity

Back 25

o How well study minimizes the probability of 2 errors:
 Type I:
• Concluding that there is an effect when in fact there is none, “false alarm”
 Type II:
• Concluding that there is no effect whereas there is one, “miss”
o Also addresses the strength of an association and its statistical significance (the possibility of chance)

Front 26

Internal Validity

Back 26

- In a relationship between one variable (A) and another (B), the degree to which we can say that (A), rater than some other variable (such as C), is responsible for the effect of (B).

Front 27

The three rules for causation

Back 27

 Covariance:
o As A changes, B changes; for example, as (A) increases, B increases, and as A decreases, B decreases.
 Temporal precedence:
o A comes first in time, before B
 Internal validity:
o There are no possible alternative explanations for the change in B; A is the only thing that changed

Front 28

Reliability: Do you get consistent scores every time?

Back 28

 Test-retest:
o People get consistent scores every time they take the test
 Interrater:
o Two coders’ ratings of a set of targets are consistent with each other
 Internal:
o People give consistent scores on every item of the questionnaire

Front 29

Bivariate Correlation

Back 29

- Associations between two measured variables
- Correlational studies can include both quantitative and categorical variables (but they are graphed differently)

Front 30

Cohen's Benchmarks

Back 30

An r of: would have an effect size of:
.10 (or -.10) Small or weak
.30 (or -.30) Medium or moderate
.50 (or -.50) Large or strong

Front 31

moderator

Back 31

a third variable that, depending on its level, changes the relationship between two other variables

Front 32

third variable problem

Back 32

a situation in which plausible alternative explanations exist for the association between two variables

Front 33

Bivariate correlations show covariance

Back 33

- But not temporal precedence—not sure which variable came first
Solution: Cross-lag panel designs (longitudinal designs)
- And not internal validity—no control for third variables
Solution :Multiple regression

Front 34

design confound

Back 34

refers to a second variable that happens to vary systematically along with the intended independent variable and therefore the alternative explanation for the results

Front 35

selection effects

Back 35

occurs in an experiment when the kinds of participants at one level of the independent variable are systematically different from the kinds of participants at the other level of the independent variable