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Research 3900 > Unit 2: Ch. 9 > Flashcards

Unit 2: Ch. 9 Flashcards

1
Q

causality

A

many quantitative research questions are about cause and effect

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2
Q

criteria for making causal inferences? (3)

A
  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)

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3
Q

what are the 3 types of quantitative research?

A
  1. experiment
  2. quasi-experiment
  3. non-experiment (observational studies in medicine)
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4
Q

experiment

A

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)

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5
Q

3 characteristics of a true study?

A
  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”
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6
Q

quasi-experimental

A

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

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7
Q

non-experimental

A

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)

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8
Q

examples of non-experimental studies (9)?

A
  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
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9
Q

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

A

intervention

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10
Q

quasi-experiment involves an intervention but?

A

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

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11
Q

counterfactual

A

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

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12
Q

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

A

deterministic

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13
Q

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

A
  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
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14
Q

what are the 3 techniques of research control?

A
  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
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15
Q

biologic plausibility

A

evidence from basic physiologic studies that a causal pathway is credible

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16
Q

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

A

true

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17
Q

other features of quantitative research designs (3)?

A
  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!!
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18
Q

pretest-posttest design

A

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

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19
Q

control group

A

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

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20
Q

experimental group

A

the group getting the intervention

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21
Q

randomization

A

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

22
Q

posttest-only design

A

the most basic experimental design

involves randomizing people to different groups and then measuring the outcome

23
Q

baseline data

A

pretest data

24
Q

posttest data

A

outcome data

25
Q

placebo

A

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)

26
Q

carryover effects

A

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

27
Q

protocols

A

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

28
Q

delayed treatment

A

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

29
Q

validity

A

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

30
Q

Hawthorne effect

A

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

31
Q

time series design

A

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

32
Q

correlation

A

an interrelationship or association between 2 variables

can be detected through statistical analyses

33
Q

T/F: correlation doesn’t prove causation

A

true

34
Q

cohort design (prospective design)

A

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

35
Q

retrospective correlational study

A

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

36
Q

case-control-design

A

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

37
Q

descriptive correlational

A

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

38
Q

self-selection

A

selection bias

39
Q

attrition

A

loss of participants over time

40
Q

homogeneity

A

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

41
Q

matching

A

involves consciously forming comparable groups

42
Q

statistical conclusion validity

A

the ability to detect true relationships

43
Q

internal validity

A

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”

44
Q

external validity

A

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

45
Q

construct validity

A

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

46
Q

stastical power

A

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

47
Q

threats to statistical conclusion validity (3)?

A
  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
48
Q

power analysis

A

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

49
Q

threats to internal validity (5)?

A
  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
50
Q

threats to external validity (1)?

A

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

51
Q

threats to construct validity (3)?

A
  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?

Research 3900 (15 decks)

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