Unit 2: Ch. 9

Question 1

causality

Answer 1

many quantitative research questions are about cause and effect

Question 2

criteria for making causal inferences? (3)

Answer 2

1. the cause must precede the effect
2. there must be a demonstrated association between the cause and effect
3. the relationship between the presumed cause and the effect cannot be explained by a 3rd variable or confounder (“intervening variable”)
   - there can’t be an outside thing influencing the other 2 things

know what’s a cause and what’s an effect

cause comes before the effect (ex: smoking causes lung cancer)

Question 3

what are the 3 types of quantitative research?

Answer 3

1. experiment
2. quasi-experiment
3. non-experiment (observational studies in medicine)

Question 4

experiment

Answer 4

offer the strongest evidence on whether a cause (intervention) results in an effect (outcome)
-this is why experiments are high on the evidence hierarchy (strong, but not as strong as systematic reviews)

Question 5

3 characteristics of a true study?

Answer 5

1. intervention
   - researcher does something to one group but not the other
2. control group
   - has no intervention but may have a placebo
3. randomization
   - researcher assigns subjects to groups randomly
   - “random assignment/selection”
   - randomization is the “great equalizer”

Question 6

quasi-experimental

Answer 6

involves an intervention but lacks one of the other 2 characteristics (either RANDOMIZATION or CONTROL)

Question 7

non-experimental

Answer 7

a study w/ no intervention
-considered weaker studies than experiments or quasi-experiments; still valuable b/c some variables can’t be manipulated (ex: abuse)

Question 8

examples of non-experimental studies (9)?

Answer 8

1. non-correlational design
2. prospective correlational designs
3. retrospective studies
4. time related study
   - cross sectional design: data collected at only one point in time
   - longitudinal study: data collected over time
5. correlation is an association between 2 variables; one may not cause another but one may happen w/ another (correlation ≠ causation)
6. prospective design may also be called cohort study by medical researchers (go forward in time)
7. retrospective correlational design is when an outcome is present, such as depression
8. comparison studies (2 types): one is between subjects (ex: men and women in a study - compare men to women) and a within subjects design (men and women in a study; only compare women w/in the group)
9. descriptive: simply describing what’s there

Question 9

Purpose of control and randomization is to make the groups equal in regards to all factors except the?

Answer 9

intervention

Question 10

quasi-experiment involves an intervention but?

Answer 10

lacks one of the other 2 characteristics (either randomization or control)

Question 11

counterfactual

Answer 11

what would happen to people if they were exposed to a causal influence and were simultaneously not exposed to it

Question 12

most causes are not ____; they only increase the likelihood that an effect will occur

Answer 12

deterministic

Question 13

John Stuart Mill developed 3 criteria for establishing causal relationships. What are those 3 criteria?

Answer 13

1. temporal: a cause must precede an effect in time
2. relationship: must be an association between the presumed cause and the effect (ex: an association between smoking and lung cancer)
3. confounders: relationship can’t be explained as being caused by a 3rd variable

Question 14

what are the 3 techniques of research control?

Answer 14

1. context: conditions of the study; want the conditions of the study to be held constant (when data is collected, where data is collected, and how data is collected)
   - need control over the study context
   - communication w/ participants is standardized, using scripts as necessary
   - intervention must be delivered according to a plan “treatment fidelity”)
   - have to have control over the context (who, what, when, where, and why of your study)
2. participants: typically done through randomization; also done through homogeneity (want participants to be as much alike on characteristics as possible)
   - can be done through matching, statistics and ANCOVA (way of controlling characteristics statistically)
3. confounders: intervening/extraneous variables; confounding variables not part of the study but can influence findings
   - researcher must be aware of what the confounding variables are/might be and control them
   - ex: studying stress - there are other things that result from stress that look like the cause

Question 15

biologic plausibility

Answer 15

evidence from basic physiologic studies that a causal pathway is credible

Question 16

T/F: except for description questions, questions that call for a quantitative approach usually concern causal relationships

Answer 16

true

Question 17

other features of quantitative research designs (3)?

Answer 17

1. masking/blinding: when participants aren’t told anything about the study; done to eliminate/decrease bias
   - there is a debrief and review after
2. time frames: researcher has to decide when and how often data will be collected
3. location: decisions have to be made about where the data is collected
   * quantitative studies consider these every time!!

Question 18

pretest-posttest design

Answer 18

involves the observation of the outcome (mood) at 2 points in time: before and after the intervention

involves collecting pretest data on the outcome before the intervention and posttest data after it

Question 19

control group

Answer 19

refers to a group of participants whose performance on an outcome variable is used to evaluate the performance of the experimental group on the same outcome

a control group is used for comparative purposes represents a proxy for the ideal counterfactual

Question 20

experimental group

Answer 20

the group getting the intervention

Question 21

randomization

Answer 21

every participant has an equal chance of being included in the group

aka random assignment

randomly assigned groups are to be expected to be comparable, on average, with respect to an infinite number of biologic, psychological, and social traits at the outset of the study

group differences on outcomes observed after randomization can therefore be inferred as being caused by the treatment

Question 22

posttest-only design

Answer 22

the most basic experimental design

involves randomizing people to different groups and then measuring the outcome

Question 23

baseline data

Answer 23

pretest data

Question 24

posttest data

Answer 24

outcome data

Question 25

placebo

Answer 25

pseudointervention presumed to have no therapeutic value, which is also called an attention control condition (the control group gets attention but not the intervention's active ingredients)

Question 26

carryover effects

Answer 26

when subjects are exposed to 2 different treatments, they may be influenced in the second condition by their experience in the first

Question 27

protocols

Answer 27

stipulate exactly what the treatment is for those in the experimental group

Question 28

delayed treatment

Answer 28

i.e., control group members are wait-listed and exposed to the experimental treatment at a later point

Question 29

validity

Answer 29

accuracy. Whether or not the questionnaire is measuring what it reports to measure the degree to which inferences made in a study are accurate; in instrumentation, it is when an instrument measures what it purports to measure; accuracy of measurement

Question 30

Hawthorne effect

Answer 30

A term derived from experiments at the Hawthorne plant, in which various environmental conditions (e.g. light, working hours) were varied to determine their effects on worker productivity regardless of what change was made, productivity increased thus, knowledge of being in a study may cause people to change their behavior, thereby obscuring the effect of the research variables

Question 31

time series design

Answer 31

involves collecting data over an extended time period, and introducing the treatment during that period

Question 32

correlation

Answer 32

an interrelationship or association between 2 variables can be detected through statistical analyses

Question 33

T/F: correlation doesn't prove causation

Answer 33

true

Question 34

cohort design (prospective design)

Answer 34

observational studies w/ a cohort design start w/ a presumed cause and then go forward to the presumed effect

Question 35

retrospective correlational study

Answer 35

an effect (outcome) observed in the present is linked to a potential cause occurring in the past

Question 36

case-control-design

Answer 36

cases w/ a certain condition, such as lung cancer, are compared to controls w/o it

Question 37

descriptive correlational

Answer 37

researchers seek to describe relationships among variables, w/o attempting to infer causal connections

Question 38

self-selection

Answer 38

selection bias

Question 39

attrition

Answer 39

loss of participants over time

Question 40

homogeneity

Answer 40

only people who are similar w/ respect to confounding variables are included in the study confounding variables in this care are not allowed to vary

Question 41

matching

Answer 41

involves consciously forming comparable groups

Question 42

statistical conclusion validity

Answer 42

the ability to detect true relationships

Question 43

internal validity

Answer 43

extent to which the IV, tx, or cause influenced the DV, outcome, or had an effect "Internal validity = how much the Independent variable is Influencing the outcome"

Question 44

external validity

Answer 44

generalizability (whether nor not you can apply the findings to other settings/people)

Question 45

construct validity

Answer 45

degree to which key concepts are captured in the study; whether or not everything should be measured are; are constructs accurate?

Question 46

stastical power

Answer 46

refers to the capacity to detect true relationships can be achieved in various ways, the most straightforward of which is to use a large enough sample

Question 47

threats to statistical conclusion validity (3)?

Answer 47

1. low statistical power (e.g. sample size too small) - power analysis 2. weakly defined "cause" - independent variable not powerful; tx not intense enough to have an effect on the outcome 3. unreliable implementation of a tx - low intervention fidelity

Question 48

power analysis

Answer 48

researchers can estimate how large their samples should be for testing their research hypotheses through power analyses

Question 49

threats to internal validity (5)?

Answer 49

1. temporal ambiguity: too much or too little time between tx and outcome 2. selection threat: bias from preexisting differences - ex: studying postpartum moms - including first time moms and moms w/ 2 or 3 kids (selection bias b/c first time moms and moms w/ previous kids are different) 3. history threat: co-occurring events that influence outcome 4. maturation threat: developmental changes that influence outcome - comes up a lot in pediatric and adolescent studies (gero studies too) - take age into consideration - ex: outcome in a 4yo may be totally different than in an 8 or 10yo 5. mortality/attrition threat: loss of participants so the group make up changes

Question 50

threats to external validity (1)?

Answer 50

inadequate sampling of study participants: may be the wrong type of people -ex: clinical sample and non-clinical sample; must compare apples to apples so that it's generalizable

Question 51

threats to construct validity (3)?

Answer 51

1. is the intervention (IV, tx) a good representation of the underlying construct? 2. is the intervention or awareness of the intervention that resulted in benefits? 3. does the dependent variable (DV, outcome, effect) really measure the intended constructs?