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Research > Midterm > Flashcards

Midterm Flashcards

1
Q

Maturation

A

Internal Validity Threat

changes that result from processes internal within the participants

Only a problem when the effects of maturation cannot be separated from the effects of the intervention

Often go together with threats due to history

Control group can help mitigate

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2
Q

Single Operations and Narrow Stimulus Sampling

A

Construct Validity

Sometimes a single set of stimuli, investigator or other facet of the study that the investigator considers irrelevant may contribute to the impact of the experimental manipulation

Also a type of external validity but in construct it’s not about generalizability but rather not being able to separate “active ingredient” from other variables

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3
Q

Threats to External Validity

A 
Multiple-Treatment Interference 
Sample Characteristics
Narrow Stimulus Sampling
Reactivity of Experimental Arrangements 
Reactivity of Assessment
Test Sensitization 
Novetly Effects
Generality across Measues, Setting and Time

Mneumonic: M. SNRRTNG

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4
Q

Types of Data-Evaluation Validity

A

Low Statistical Power
Unreliability of the Measures
Multiple Comparisons and Error Rates
Participant Heterogeneity

Variability in Procedures
Errors in Data Recording, Analysis, and Reporting
Restricted Range of the Measures
Misreading or Misinterpreting the Data Analyses

Menumonic: LUMP VERM

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5
Q

Experimenter Expectancies

A

Construct Validity

Unintentional effects the experimenter may have that influence the subject’s responses in the experiment

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6
Q

Statistical Regression

A

Internal Validity Threat

extreme scores tend to change towards the mean over time

no treatment control group helps mediate

protect against:

randomly assign participants to groups
use measures with high reliability and validity
-test participants twice before the intervention and select those who are high on both testings (rarely done)

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7
Q

Purposive Samples

A

Pick cases that are judged to be typical of the pop of interest

Used to forecast elections

Ex: Picking a number of small election districts whose election returns in previous years have approximated the overall state returns.

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8
Q

Interrelations among Validities

A

Trade-off between controlling a situation in a study and being able to generalize it

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9
Q

Reactivity of Experimental Arrangements

A

External Validity Threat

Issue of how partcipants’ knowledge that they are being studied (or in a special program or that a relationship is being examined between specific variables) changes their behavior

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10
Q

History

A

Internal Validity Threat

Any event inside (except the IV/intervention) or outside of the experiment that may account for the results but it has to be a plausible explanation of the results

Control group can help mitigate

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11
Q

Testing

A

Internal Validity Threat

Effects of repeated assessment

“practice effect”

group that receives pre and post without intervention can help rule this out

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12
Q

Test Sensitzation

A

External Validity Threat

Partcipants may respond differently to an intervention because the pretest shows them what the focus of assessment is

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13
Q

Sample Characteristics

A

External Validity Threat

The extent to which the results can be extended to subjects or clients whose characteristics may differ from those included in the investigation

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14
Q

Variablity in the Procedures

A

Data-Evaluation Validity

Same as particpant heterogeneity but has to do with procedures, instructions, etc.

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15
Q

Attention and Contact Accorded the Client

A

Construct Validity

The extent to which increased attention or contact to the client that is associated with the intervention could plausibly explain the effects attributed to the intervention

Placebo group can help mediate this

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16
Q

Special Treatment or Reactions of Control

A

Internal Validity Threat

control group gets special attention which can be an alternative explanation of the results

Ex: control group is given something so they won’t feel snubbed

Other examples:

  • Participants try harder because they know they’re in the treatment group
  • Control group tries harder to match Part.
  • Control group performs worse because they are let down that they are in control group
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17
Q

Restricted Range of the Measures

A

Data-Evaluation Validity

A measure may have a very limited range (total score from high to low) and that may interfere with showing group differences.

Not enough variablity in scores to show difference in group

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18
Q

Low Statistical Power

A

Data-Evaluation Validity

Major threat to DEV

Power is likelihood of demonstrating an effect or group difference when in fact there is a true effect in the world

Can happen when samples are too small (not representative) or too large (too much variability)

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19
Q

Threats to Internal Validity

A 
Statistical Regression
Maturation
Instrumentation 
Testing 
History
Selection Biases

Attrition
Diffusion or Imitation Treatment
Special Treatment or Reactions of Controls

Mneumonic: SMITHS ADS

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20
Q

Accidental (Available) Samples

A

take cases that are available until reach a specifed N (ex: first 100 people on the street)

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21
Q

Demand Characteristics

A

Construct Validity

Cues of the experimental situation that influence the results

aspects of the instructions, procedures, etc. that are part of the study but not the “active ingredient”

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22
Q

Reactivity of Assessment

A

External Validity Threat

Participants’ awareness that they are being assessed can alter their responses

Focuses on the measures used and other measurement procedures

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23
Q

When and How Threats to Internal Valdity Occur

A
  1. A study is poorly designed and many threats are possible
  2. A study is designed well but conducted sloppily
  3. A study is designed well but attrition occurs
  4. A study is designed well but the results do not allow the conclusion that treatment led to change
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24
Q

Instrumentation

A

Internal Validity Threat

Changes in how the DV is measured over time

Can occur when any of the following is not constant:

measuring instruments
observers, raters, or interviewers
remarks or directions form the experimenter
test conditions

Most common occurrence is where raters change the criteria they are using over time

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25
Q

Construct Validity

A

what specific aspects of an intervention are responsible for observed change or an observed effect

Distinguish from contruct validity of a test

considered after threats to internal validity have been ruled out

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26
Q

Cluster Sampling

A

first sample groupings or clusters, then sample individuals from these

Can be random

used because it is difficult and expensive to get random or stratified random samples

Large-scale surveys often use this method

Aka multistage sampling because of the different stages involved

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27
Q

Diffusion or Imitation of Treatment

A

some or all of the participants in the control group may inadvertently receive some or all of the treatment (e.g. kids in two classes talk about the treatment during recess)

Also can happen if some people in the intervention group do no receive the intervention

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28
Q

Types of sampling

A

Probability Sampling
Accidental Samples
Systematic Sampling
Stratified Sampling

Quota Samples
Purposive Samples
Cluster Sampling

Mneumonic: PASS QPC

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29
Q

Types of Validity (4)

A

Internal
External
Construct
Data-Evaluation

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30
Q

Null hypothesis

A

specifies there is no differences between groups

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31
Q

Effect size (ES)

A

magnitude of the difference between two (or more) conditions or groups

M1 - M2/SD

The smaller the variablity (the more we minimize error) the larger the effect size because SD is the denominator

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32
Q

Confounds

A

factors that varied with the intervention and therefore could explain (be responsible for) the results

Confounds are threats to construct validity

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33
Q

Novelty Effects

A

External Validity Threat

It is possible that the effect of an experimental manipulation or intervention might in part be due to its novelty

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34
Q

Reactive measures

A

If awareness of assessment leads persons to respond differently from ow they would usually respond

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35
Q

Obtrusive measures

A

When subjects are aware their performance is being assessed

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36
Q

Stratified Sampling

A

members of groups in the pop are selection in proportion to their representation in the pop

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37
Q

Types of contruct validities

A

Single Operations and Narrow Stimulus Sampling
Experimenter Expectancies
Demand Characteristics
Attention and Contact Accorded the Client

Mneumonic: Single Experimenters Demand Attention
or SADE

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38
Q

Data-Evaluation Validity

A

Facets of the evaluation that influence the conclusions we reach about the experimental condition and its effects

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39
Q

Generality across Measures, Settings and Time

A

External Validity Threat

The extent to which the results extend to other measures, settings, or assesment occasions than those included in the study

40
Q

Quota Sample

A

Accidental Sample where you are trying to meet certain groups proprotionate to the pop

Ex: interviewing x number of men and women

41
Q

Multiple Comparisons and Error Rates

A

Data-Evaluation Validity

When multiple statistical tests are completed the likelihood of chance finding is increased

i.e. .05 alpha is only for one test. alpha goes above .05 when you do multiple tests a.k.a “experiment-wise error rate”

42
Q

Oversample

A

take more participants of one group to be able to compare (ex: oversampling native americans to be able to compare them to other groups)

43
Q

Systematic Sampling

A

take every Kth person from your list

problem can occur if there is a cyclical pattern in the list that coincides with the sampling interval

44
Q

Multiple-Treatment Interference

A

External Validity Threat

Do not know whether a particular treatment would have been effective by itself if it is administered along with another treatment or after another treatment

Also, when one task is given and then preceded by other tasks

Can refer to therapy or to different conditions in an experiment

45
Q

Participant Heterogeneity

A

Data-Evaluation Validity

when characteristics related to how particpants respond to the treatment are too varied which varies the results

Can be addressed with factorial design or by using a more homogenous sample

46
Q

Misreading or Misinterpreting the Data

A

Data-Evaluation Validity

Wrong stastical test was used to analyze the data or author went beyond what the results were showing in their interpretation

47
Q

Selection Biases

A

Internal Validity Threat

differences between groups before the intervention or experimental manipulation because of selection or assignment of groups

One form is using different selection methods for two groups

Often a problem when using groups that already exist (i.e. classes or hospital wards)

48
Q

Narrow Stimulus Sampling

A

External Validity Threat

The extent to which the results might be restricted to a restricted range of sampling materials (stimuli) or other features the experimenters used in the experiment

Stimulus characteristics include the experimenters, setting, interviewers or other factors

Most commonly occurs when there is one experimenter, one therapist, one setting, one taped story, etc.

49
Q

Probability Sampling

A

Aka random sampling

Each person in the population has equal chance of being choosen for the sample

50
Q

Risk Factor

A

A predictor of some later outcome

Correlation where we know that one variable comes before the other

Risk factor is not a cause

Increases the likelihood of some outcome

51
Q

Cause

A

One variable influences either diretly or through other variables the apprearance of the outcome

52
Q

Criteria for Inferring a Causal Relationship between Variables

A
  1. A strong relationship between the independent variable and the dependent variable.
  2. Consistency or replication, although some inconsistency can occur when there is a moderator variable.
  3. The cause comes before the effect.
  4. A “does-response relation”: more of the IV is associated with greater change in the DV.
  5. A reasonable process that explains how the IV leads to the DV.
  6. Experiment: when the IV is altered, a change in the DV occurs.
  7. Existence of similar findings in other areas.
53
Q

4 conditions for mediator

A
  1. “The intervention (e.g. exercise) leads to change on [an] outcome measure (e.g. depression).
  2. The intervention alters the proposed mediator (e.g. …stress level).
  3. The mediator is related to [the] outcome (stress level is related to symptoms).
  4. Outcome effects (changes in depression) are not evident or substantially less evident if the proposed mediator (stress in this example) did not change.”
54
Q

Protective Factor

A

A characteristic or variable that prevents or reduces the likelihood of a deleterious outcome

negatively correlated with the onset of some later problem

protective factor decreases the likelihood of the outcome

55
Q

Correlate

A

two variables are related but one variable does not precede the other

56
Q

Moderated mediation

A

occurs when strength (or direction) of the relation of the mediator to outcome depends on the level of some other variable

mediator that doesn’t work for everyone

57
Q

Mechanism

A

the steps or processes through which the intervention (or some IV) actually unfolds and produces the change

58
Q

Translational research

A

applies finding from basic research (e.g. laboratory research) to people in real life (applied research)

59
Q

Efficacy research

A

conducting treatment in highly controlled conditions

60
Q

Effectiveness research

A

evaluates treatments in clinical settings with “real” patients and under conditions more routinely seen in clinical practice

61
Q

Theory

A

conceptualization of the phenomenon of interest

provides a tentative explanation of how variables are related

To be a scientific theory it must generate testable hypotheses

organizes existing research in a way that guides further studies

Can explain the basis for change and give us an idea of which moderators to investigate

62
Q

Research Design

A

refers to the arrangement or ways to arrange conditions to evaluate the hypotheses

63
Q

Three major types of studies done in psychology

A
  1. True Experiments
  2. Quasi-experiments
  3. Observational designs
64
Q

True Experiment

A

allow the most control over the IV

randomly assign each person to a condition

65
Q

Randomized controlled trails (RCTs)

A

When the IV involves an intervention, a true experiment becomes a RCT

66
Q

Quasi-experiment

A

when researcher cannot control who is in each group

often occurs when doing research in schools or hospitals (because classes or wards already exist)

Sometimes researcher is able to randomly assign P in some groups but not all groups

67
Q

Observational Designs

A

Researcher does not create the IV

variable of interest is studed by selecting subjects who vary in the characteristic or experience of interest

people come to the study with their level of IV (e.g. gender, ethnicity, etc.)

many variables that we study can only be researched in observational designs because it is not psosible and/or not ethical to assign people to levels of the IV (e.g. studying the effects of parental divorce on children)

68
Q

Cross-sectional studies

A

make comparisons between groups at a given point

69
Q

Longitudinal studies

A

make comparisons over an extended period of time often involving several years

a study with pre, post and follow up is longitudinal

70
Q

No-Treatment Control Group

A

receives all of the assessments that the treatment group does but no intervention

71
Q

Wait-List Control Group

A

Control group receives treatment after the final assessment has been made

time between pre and post assessments must be the same for the treatment group and for the wait-list control group

72
Q

No-Contact Control Group

A

no contact with the research project and do not know they are in a treatment

Assessment appear to them as a routine part of some other activity

difficult because violates informed consent

pretty rare

73
Q

Nonspecific Treatment or Attention-Placebo Control Group

A

Get everything except actual treatment (including attention)

Difficult to have expectations for improvement without using an intervention

Issues with APCG can be avoided by using Treatment as Usual (TAU)

74
Q

Treatment as Usual (TAU)

A

routine or standard treatment that is usually provided at a clinic

Advantages:

  1. People seeking treatment get it, so avoid an ethical issue.
  2. Likely to be less attrition than in a group not receiving treatment.
  3. Generally controls for common factors.
  4. More acceptable to therapists.
75
Q

Yoked Control Group

A

used to make the groups equal on some variable, like number of sessions, so that variable will not be a confounding variable

76
Q

Nonrandomly Assigned or Nonequivalent Control Group

A

—“help rule out specific rival hypotheses and decrease the plausibility of specific threats to internal validity,” like history, maturation, or testing. “Such a group may be used when a no-treatment control group cannot be formed through random assignment.”

77
Q

Case-control design

A

Type of observational design

investigate a variable (characteristic) by comparing those who have the characteristic with those who do not have the characteristic. These groups are compared on other variables in the present or in the past.

78
Q

Cross-Sectional Case-Control Design

A

compares groups that differ on the particular characteristic being studied on other variables that exist currently.

79
Q

Retrospective Case-Control Design

A

compares groups that differ on the particular characteristic being studied on other variables that occurred in the past

80
Q

Strengths of Case-Control Design

A
  1. “The designs are well suited to study conditions that are relatively infrequent,” for example, individuals diagnosed with DID.
  2. Less costly than following people over time, e.g., to see who develops a particular problem. Instead, people with and without the problem are compared.
  3. Since people are assessed at one point in time, attrition is not a problem. It is a problem when people are followed over time.
  4. These designs can go beyond showing that two variables are related and identify moderator variables.
  5. Can study variables that could not be studied experimentally (i.e. by assigning people to levels of the IV).
  6. Can match subjects on some variable(s).
  7. Can generate hypotheses about which variable caused which other variable.
81
Q

Weaknesses of Case-Control Design

A

Weaknesses of case-control designs:

  1. When two variables are related, it may be unclear which one came first.
  2. Causation cannot be demonstrated.
  3. There may be sampling bias in how participants are selected. For example, when studying women who have been abused by their spouses, if one found their participants at domestic violence shelters, they would be leaving out the majority of women victims of domestic violence who do not go to shelters. Clearly, how participants are found is very important in this kind of study.
82
Q

Cohort Designs

A

Type of observational design

here the researcher follows and studies a group or groups of people over time. So, this is a prospective, longitudinal design. A cohort “is a group of people who share a particular characteristic such as being born” in a particular year.

83
Q

Single-Group Cohort Design

A

identifies subjects who meet a particular criterion (e.g. …individuals released from prison…) and follows them over time.” A number of variables are assessed at the beginning of the study and often at other times, i.e. more than once, during the study. At the end of the study, the researcher examines which of the variables assessed predict(s) the outcome

84
Q

Multigroup Cohort Design

A

follows two or more groups over time “to examine outcomes of interest.” “One group is identified because they have an experience, condition, or characteristic of interest; the other group is identified who does not

85
Q

Accelerated, Multi-Cohort Longitudinal Design

A

include “cohorts who vary in age when they enter the study.” “The design is ‘accelerated’ because a longer period of development is covered by selecting cohorts at different periods and following them.”

For example, one could study the age range 5-14 years by selecting children at the ages of 5, 8, and 11, and following them for 4 years. It would take much longer to study a cohort of 5-year-olds until they were 14.

Advantage of this design is that it doesn’t take as long and can explore cohort effects

86
Q

Strengths of Cohort Designs

A
  1. Show that antecedent comes before outcome.
  2. Can be sure that the outcome did not bias assessments of the antecedents (because
    it had not occurred yet).
  3. Assessments can be made at different times to show the progression from
    antecedent to outcome.
  4. “Cohort designs are good for testing theories about risk, protective, and causal
    factors.”
87
Q

Weaknesses of Cohort Designs

A
  1. Take a long time.
  2. Expensive.
  3. Attrition can bias the sample.
  4. Possibility of cohort effects.
  5. If the outcome of interest is infrequent, sample size may end up being so small that statistical power is low.
88
Q

Pretest-Posttest Control Group Design

A

Type of true experiment design

R = random assignment A = Assessment or Observation X = Intervention

Group 1: R A1 X A2
Group 2: R A1 A2

89
Q

Matching

A

grouping participants on a variable (or variables). Then participants at each level of the variable are assigned to each group, so that the groups end up equivalent.

90
Q

Posttest-Only Control Group Design

A

Type of True Experimental Design

R = random assignment A = Assessment or Observation X = Intervention

 Group 1: 	R	X	A1
 Group 2:	R		A1
91
Q

Solomon Four-Group Design

A

Type of true experimental design

Group 1: R A1 X A2 Design 1
Group 2: R A3 A4
Group 3: R X A5
Group 4: R A6 Design 2

Pros:

  1. Controls for the usual threats to internal validity.
  2. Can measure the effect of pre-testing (A4 vs. A6) because they differ only in that Group 2 got the pretest and Group 4 did not.
  3. Can measure the interaction effect of pre-testing and the intervention (A2 vs. A5).
92
Q

Factorial Designs

A
  1. Factorial designs allow investigation of two or more independent variables at the
    same time, i.e. in one study. Each variable has two or more levels (or conditions).

Four box table

93
Q

Pretest-Posttest Design (aka nonequivalent control group design)

A

Type of quasi

Group 1: nonR A1 X A2
Group 2: nonR A1 A2

94
Q

Posttest-Only Design

A

Type of quasi

Group 1: nonR X A1
Group 2: nonR A2

95
Q

Crossover Design

A

Type of Multiple-Treatment Designs

Group 1: R A1 X1 A2 X2 A3
Group 2: R A4 X2 A5 X1 A6

Pretest
Participants are randomly assigned to groups (i.e. orders) and the two groups
receive the two treatments (X1 and X2) in different order. The pretest is sometimes
omitted.

96
Q

Multiple-Treatment Counterbalanced Design

A

Type of Multiple-Treatment Designs

	Order
	               1st	2nd	3rd	4th
Group (or
Sequence)	1	A	B	C	D
	               2	B	A	D	C
	               3	C	D	A	B
	               4	D	C	B	A

This design is called a Latin Square

97
Q

Initial Equivalence

A

Making sure the groups are as similar as possible so we can establish IV as cause of DV

We try to accomplish through random selection and random assignment

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