Chapter 3

Question 1

A variable is …

Answer 1

something that varies. Thus, it must have more than one level (i.e., exist in more than one state).

Question 2

Any given variable may be either

Answer 2

measured or manipulated.

Question 3

T/F: some variables may be measured OR
manipulated, depending on the
nature of the study.

Answer 3

T

Question 4

Any variable can be considered in two different ways:

Answer 4

1. As a conceptual variable, or construct: the variable defined in terms of its definition, i.e., as an abstract concept.
   Stress: “an adaptive response to a perceived danger or threat that involves physiological, cognitive, affective, and behavioral
2. As an operational variable: the variable define in a more specific way that allows for manipulation / measurement.
   So, to operationalize a variable is to decide exactly how that variable will be manipulated or measured.
   E.g., How should we operationalize stress?

components.”

Question 5

One method with animals to manipulate stress

Answer 5

restraint stress

Question 6

One way with humans to manipulate stress

Answer 6

The Trier social stress test (TSST)

Question 7

The Trier social stress test (TSST)

Answer 7

mainly consists of an anticipation period (10 min) and a test period (10 min) in which the subjects have to deliver a free speech and perform mental arithmetic in front of an audience

Question 8

Measuring stress

Answer 8

Questionnaire, interview or diary, or physiological measures (Skin conductance, heart rate, blood pressure)

Question 9

What’s a claim?

Answer 9

Any sort of argument being made

Question 10

Three types of claims

Answer 10

• Frequency
• Association
• Causal

Question 11

Frequency claims

Answer 11

the claim regards the prevalence (or frequency) of a particular behaviour or phenomenon. Note that there is only one variable, and it is being measured.

Question 12

Association claims

Answer 12

In such cases there are two variables, both measured.

The claim involves the association between them.

Question 13

association claims involve

Answer 13

correlations between two variables, often plotted on scatterplots (one variable on each axis)

Question 14

Causal claims

Answer 14

again involve two variables, with the key difference that
one variable is manipulated, while the other is measured.
This enables the researcher to test a causal hypothesis.

Question 15

Association claim verbs

Answer 15

linked to, associated with, correlated with, prefers, may predict, tied to, goes with

Question 16

Causal verbs

Answer 16

Causes, affects, promotes, reduces, increases, decreases, makes, changes, leads to, adds, etc.

Question 17

T/F: Interrogating a claim involves evaluating it based on its validity: the appropriateness of its conclusion or decision. That is, whether the claim is reasonable, accurate and justifiable.

Answer 17

True

Question 18

four types of validity

Answer 18

1. Construct validity: How well is/are the variable(s) operationalized?
2. External validity: How generalizable are the findings (the claim)? To what degree do the findings generalize to or represent people and contexts beyond the study?
3. Statistical validity: Are the statistical conclusions accurate and reasonable? That is, do the statistical results support the claim?
4. Internal validity … is part of a larger question related to the criteria for causation and will be discussed a little later.

Question 19

Interrogating frequency claims, stat. validity

Answer 19

For frequency claims, it usually comes down to the
margin of error of the estimate , which indicates how close your
results are to the overall population.

Question 20

Sample size’s effect on margin of error

Answer 20

The bigger the sample size, the smaller (narrower) the margin of error of the estimate

Question 21

Construct validity info will almost always be in the

Answer 21

methods section

Question 22

statistical validity will be in the

Answer 22

results section

Question 23

To visualize an association

Answer 23

scatterplot data

Question 24

To statistically analyze an association

Answer 24

we typically calculate an r value

Question 25

The r value , or coefficient of correlation , describes the | association in two ways:

Answer 25

1. Its direction : A positive r value represents a positive association (as one thing increases, so does the other); negative represents negative. 2. Its magnitude : How strongly are the variables How large is the number (ranges from 0 to 1)

Question 26

To infer whether that association is | statistically significant, we can calculate a

Answer 26

p value and r-value (correlation)

Question 27

p-value

Answer 27

represents the likelihood that the relationship seen is due to random chance

Question 28

The threshold for statistical significance is

Answer 28

0.05. p < 0.05 means that there is less than a 5% chance that what we see is just due to random chance. In this case, we call the relationship statistically significant.

Question 29

The p value is affected by sample size. For a given r value , the resulting p value will go down as sample size goes up.

Answer 29

value will go down as sample size goes up.

Question 30

External validity of association claims has

Answer 30

Lower expectations than frequency claims.

Question 31

As with all causal claims, there are two variables:

Answer 31

1) An independent variable that is manipulated The manipulated variable is often easy to operationalize. 2)dependent variable that is measured:

Question 32

Interrogating causal claims: statistical validity

Answer 32

p-value

Question 33

establishing causation between two variables.

Answer 33

That is, not just that A and B are related, but that A causes B.

Question 34

There are three criteria for causation:

Answer 34

1. Covariance do the two variables change/vary together? As one changes, does the other change in a predictable manner? 2. Temporal precedence does the “causative” variable (and its manipulation) come before the other variable? 3. Internal validity Are there any alternative explanations for the observed relationship? Confounds? Maybe a “third variable” influencing both variables being examined? is less cut and dry. If we think long enough, we can probably come up with a possible confound or two for any study, but did the researchers do a reasonable job?

Question 35

Ways to assess internal validity

Answer 35

Having inclusion criteria and using random assignment