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PSYC3042 > Revision Deck > Flashcards

Revision Deck Flashcards

1
Q

What is Popper’s theory of falsification?

A

Theories can never be confirmed by empirical tests, only falsified.
Falsifiability = criterion for distinguishing science from non-science.

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2
Q

What is the hypothetico-deductive spiral?

A

Observation -> theory -> empirically testable hypotheses -> theory modification.

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3
Q

What defines a true experiment?

A

Causation can be inferred most easily when the suspected causal factor is manipulated (i.e., a “true experiment”).
and the influences of all other “contaminating” factors or artifacts are excluded.

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4
Q

What is data linkage?

A

preparing data for statistical or qualitative analysis
e.g.
averaging response latencies over several trials
expressing raw scores as percentages or proportions (NB: Simpson’s Paradox).
standardising scores (e.g., expressing in standard deviation terms, i.e., converting to z scores).
log transformations.
collapsing scores into categories (e.g., median split into high vs. low scorers).
forming composites.

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5
Q

What is the difference between correlation does not equal causation and covariation does not equal causation?

A

A reliable statistical association is not enough to demonstrate causation.
This holds true:
whatever statistical analysis is used (e.g., correlation coefficient, regression, ANOVA).
even in well-controlled, ‘true’ experiments

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6
Q

What are the four alternative explanations to X causes A?

A

Coincidence
X ->A.
Confounding Variable.
Underlying Construct.

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7
Q

What are the three attributes of relationships?

A

Direction (pos/neg)
Strength
Form (linear, curvilinear/exponential, cyclical)

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8
Q

What is the difference between a factorial and non factorial research design

A

Factorial: All IVs crossed with each other over each level to form cells
Non-Factorial: one or more IVs have levels that are not crossed

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9
Q

How do you test interactions with continuous IVs?

A

Plotting groups at 1SD above and below mean, predictors

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10
Q

What defines an Ordinal Interaction?

A
  1. The lines do not cross, AND

2. The lines do not slope in opposite directions.

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11
Q

What defines a disordinal interaction?

A
  1. The lines cross, AND

2. The lines slope in opposite directions.

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12
Q

What is the requirement for an unclassified interaction?

A

Meeting only one of the criteria for a disordinal interaction

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13
Q

Why does it matter what kind of interaction there is?

A

A predicted disordinal interaction (without main effects) is a very strong result, because:
It’s more difficult to find an alternative explanation to fit the opposing simple effects.
A predicted ordinal interaction (2 main effects) is weakest.
Measurement artifacts or other alternative explanations may account for the results.
For a predicted “not classified” interaction, it depends.
More vulnerable to alternative explanations than disordinal interactions

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14
Q

What is Generality?

A

External validity

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15
Q

How do you determine generality limitations?

A

Revealed by the interaction of a critical IV with a person variable, a setting variable, some aspect of treatment, etc.

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16
Q

Under what circumstances are main effects and interactions not independent?

A

In categorical-IV studies (e.g., using ANOVA).
If the design is unbalanced

In measured-IV studies (e.g., using moderated regression):
An interaction term (e.g., a x b) may be highly correlated with the individual IVs

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17
Q

What is a hidden interaction?

A

Sometimes, the effect of a within-Ps IV on a DV (i.e., an IV-DV relation) is collapsed into a difference score and treated as a DV.
The effect of another IV on this DV may be reported as though it is a main effect, when really it is an interaction.

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18
Q

How do you prevent hidden interactions?

A

Always show all cell means before collapsing
Possible problems with a collapsed IV.
Check the full pattern of results.

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19
Q

What is internal validity?

A

“The degree to which a study establishes that a factor causes a difference in behaviour. If a study lacks internal validity, the researcher may falsely believe that a factor causes an effect when really it doesn’t.”

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20
Q

What are threats to internal validity?

A

Alternative explanations of an association (usually confounds)

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21
Q

What is the difference between design-related confounds and procedural confounds?

A

Design-related- occur from design decisions

Procedural confounds are introduced in the procedure and generally cannot be predicted.

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22
Q

What is a counter confound?

A

A counter-confound is a variable that covaries with the IV & the DV such that it could produce a pattern of results opposite to that predicted.

a counter-confound makes your experiment less sensitive because it works against a significant result for hypothesis
BUT if you do get significant results in the predicted direction, a counter-confound is not a threat to interpretation.

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23
Q

What is a person-variable confound?

A

Person variable: A pre-existing characteristic of participants that they bring with them to the study.
Confound:
a person-variable (other than a PAV IV) that varies systematically over levels of an IV.

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24
Q

How do you prevent person variable confounds?

A

(a) Randomly assign persons to IV levels.
(b) Match participants on potential confound + randomly assign.
(c) Vary potential confound & IV(s) factorially.
(d) Match groups on potential confound without random assignment.

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25
Q

What is differential attrition?

A

When the rate of participant loss over conditions differs systematically and significantly across groups.

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26
Q

What is a testing confound

A

When the pre-test causes an effect rather than the manipulation/intervention

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27
Q

How do you prevent testing confounds?

A

a)Vary the treatment between-participants and randomly assign participants to levels of the treatment IV (treatment vs. no treatment).
With random assignment to groups, a pre-test is not necessary for control  can avoid the problem.
(b) Use a Solomon Four-Group Design. Half get pre-test, half don’t

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28
Q

What is a sequencing confound?

A

occurs when exposure to the IV or DV (or associated procedures) causes changes in responding that provide an alternative explanation for the results.

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29
Q

What types of research have a high risk of sequencing confounds?

A

Experiments on attention & performance, cognitive processes.
Ps complete multiple questionnaires on attitudes, beliefs, etc.

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30
Q

How to prevent sequencing confounds?

A

(a) Randomly sequence conditions.
(b) Counterbalance condition orders
(c) Vary IV between-participants.
(d) Used fixed sequence that works against the hypothesis.
(e) Use a yoked control.

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31
Q

What is counterbalancing?

A

Counterbalancing ensures that:
each level of the IV occurs equally often in each available slot, and
every condition precedes every other condition equally often.

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32
Q

What is a contrast effect and why is it problematic?

A

A participant’s response to a stimulus item may be influenced by the extent of the contrast between the present item and the previously-presented item.
A large change in the IV value from one item to the next may lead to a disproportionately large shift in responding.
Counterbalancing will not account for contrast effects as contrast is not equal

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33
Q

What is asymmetric carryover and why is it an issue?

A

if A2 is affected by A1 more than A1 is affected by A2

An issue as counterbalancing will not equally alter carryover effects

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34
Q

What are history confounds and what kinds of studies do they affect??

A

Time- and place-dependent changes that:
happen to occur during the course of the study,
and provide an alternative explanation for the results.

History confounds can affect:
within-participants IVs (mainly).
between-participants IVs.

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35
Q

What are maturation confounds?

A

Time-dependent changes that:
occur within participants during the course of the study,
and provide an alternative explanation for the results.

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36
Q

How do you prevent within-participants maturation and history confounds?

A

Control group that receives placebo or no treatment

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37
Q

What is Instrumentation change?

A

Instrumentation change is:
a shift in measurement over time in the direction predicted by hypothesis,
resulting in apparent change in the DV.
Preventable by random assignment, concurrent score collection and comparison group

38
Q

What are operations confounds?

A

An operations confound is an alternative explanation (specifically, a competing construct) that is introduced in the procedures used by the researcher to operationalise a construct.

39
Q

What are materials confounds?

A

A materials confound is an alternative explanation (specifically, a competing construct) that is introduced in the materials used by the researcher to operationalise a construct.

40
Q

How do you prevent materials confounds? (3 strategies)

A

Replicate over stimuli.
Random assignment of items to levels of the IV
Hold extraneous item variables

41
Q

What are data linkage confounds?

A

when spurious results are produced due to:
inappropriate analysis of data; or
inappropriate transformation of scores (i.e., preparing data for analysis).

42
Q

What is Simpson’s paradox?

A

Simpson’s Paradox
Can occur when two tests differ in both:
(a) difficulty; and
(b) the proportion of Ps undertaking each test.
e.g., if the less able group of participants is over-represented in the easier test, it can look as though they are performing better overall if the scores are inappropriately proportionalised
Drawing conclusions about % passing is misleading if raw frequencies are pooled over the tests

43
Q

What are observer expectancy effects and how do you prevent them?

A

Observer expectancy issues occur when an observer is biased to perceive participants’ behaviour as consistent with the hypotheses.

Where possible, use observers who are blind to the hypotheses and conditions (i.e., which participant is in which condition).
Videotape participants’ behaviour for independent observers to code.
Where possible, use objective/unambiguous measures of behaviour (e.g., a physiological index).

44
Q

What are experimenter expectancy effects?

A

Experimenter expectancy issues occur when the experimenter communicates to the participant what to do to support the hypotheses.
Experimenters may do this:
through unintentional nuances in: tone of voice, expression, wording of instructions, etc..; through the “demand characteristics” of the settings, stimuli, tasks, questions, etc., used in the study

45
Q

How do you prevent experimenter expectancy effects?

A

Where possible, use experimenters who are blind to the hypotheses and conditions.
Include procedures that disguise the purpose of the study

46
Q

What are participant expectancy effects (4 types)?

A

Participant expectancy effects may take several forms:
Demand Compliance (the participant responds in accordance with demand characteristics).
Participant Resistance (the participant tries to resist what they think the experimenter wants them to do).
The Hawthorne Effect (When the treatment group changes their behaviour not because of the treatment itself, but because they know they are getting special treatment).
Positive Self-Presentation (participants respond in a way that portrays them in a good light)

47
Q

How do you prevent participant expectancy effects?

A

Prevent experimenter expectancy/demand characteristics
Use a placebo-control
Blind participants to the hypotheses and conditions.

48
Q

What is floor or ceiling compression?

A

Floor Compression:
occurs when a test is too difficult or inadequate time is given – scores are compressed near the minimum.

Ceiling Compression:
occurs when a test is too easy – scores are compressed near the maximum.

usually works against a significant effect (i.e., a sensitivity issue).
is a confound if null results were expected.

49
Q

What are inappropriate comopsite dependent variables?

A

Correlated DVs are sometimes combined in a weighted linear combination or (unweighted) average, to produce a composite dependent variable.

Correlated DV measures do not necessarily measure the same construct (i.e., “the same thing”).
Consequently, individual components of a composite dependent variable may behave differently over IV levels.

50
Q

What is the difference between confounding and generality?

A

systematic variation (potential confound).

vs. variable held constant or restricted in range (potential generality limitation).

51
Q

What are the conditions for an extraneous variable to limit generality?

A

it is held constant (or restricted in range) in the study,

and, if varied systematically, it would interact with IV(s) in the study

52
Q

What are the four possible interaction effects of settings? (Spatial Generalisation)

A

(a) The Obtrusiveness of Scientific Trappings.
(b) The Physical Setting.
(c) Researcher Attributes.
(d) Researcher Expectancies.

53
Q

What are the four possible interaction effects of tasks and materials? (Spatial Generalisation)

A

(a) The Artificiality of Laboratory Experiments. (Variables held constant in the lab may exert large effects in the field. Conversely, variables manipulated in the lab may have little practical influence in the field.)
(b) Pre-Testing Effects.
(c) Carryover Effects.
(d) Demand Characteristics.

54
Q

What are the four possible interaction effects of participant attributes? (Spatial Generalisation)

A

(a) Convenience Sampling. (For generality, a representative sample is required)
(b) Psychology Student Samples.
(c) Volunteers. (Their results may not generalise, because volunteers may exceed the norm in)
(d) Attrition.

55
Q

What are the three kinds of temporal generalisation issues?

A

(a) Changes in Social Norms, etc..
(b) Changes Within Psychology. (Past results may not be replicated by experimenters with different assumptions and approaches.)
(c) Type I Errors & Artifacts; Short-Term Circumstances. (May produce results that will not be replicated)

56
Q

What are the major strategies to improve generality?

A

Replicate lab findings in the field, use a variety of designs and approaches

To establish/improve spatial generality, conceptual replication is the best approach.
Conceptual replication = replication with variation

For temporal generality, replication is essential

57
Q

What is the difference between confounding and sensitivity?

A 
systematic variation (potential confound).
vs. random variation (may affect sensitivity).
58
Q

What is the issue with null results?

A

Null results are ambiguous. Possibilities:
The hypothesis is incorrect.
The study was not sufficiently sensitive.

59
Q

How do you maximise the IV-DV relationship?

A

Ensure adequate variation of the IV. Methods include:
(a) Use extreme levels of the IV.
(b) Use parametric variation. (Stepping systematically through the estimated effective range of the IV.)
(c) Check the potency of the manipulation.
Tailor the procedures for maximal IV effect. (Optimise the level of a non-focal interactive variable (i.e., a factor which is not an IV in the study, but which has the potential to interact with one of the IVs)

60
Q

What is the adequacy/sensitivity of a measure?

A

Adequacy/Sensitivity of a Measure = The extent to which the measure is capable of tapping into (or registering) variation in behaviour.

61
Q

How do you improve the adequacy/sensitivity of a measure?

A

(a) Check score distribution, range, skew, etc..
Check whether the range of scores is adequate, etc..
Ideally, do so before the study (i.e., in a pilot study).

(b) Conduct a power analysis.
Assesses ability to show a significant difference (e.g., between groups) if such a difference truly exists.
Can use to decide how many participants to recruit.

(c) Pilot test for possible floor/ceiling effects.
Caused by insensitive tests, e.g., ability test: Too difficult (floor effect) or too easy (ceiling effect).

(d) Make measurement more fine-grained.
(e) Consider whether participants are likely to use the entire range.

(f) Conduct a “Benchmark” Check.
i. e., Replicate an established phenomenon, using new IV tasks, procedures, etc., to demonstrate their adequacy.

62
Q

What do you need in addition to a predicted significant result to conclude that your hypothesis was supported?

A

Construct Validity and Eliminate any Alternative Explanations

63
Q

What is construct validity?

A

Do the IV(s) and DV(s) capture the construct(s) they are intended to tap into?

64
Q

What are the four types of construct validity?

A

Face Validity.
Criterion Validity.
Convergent Validity.
Discriminant Validity

65
Q

What is criterion validity?

A

The degree to which scores on the measure are correlated with scores on a measure of a theoretically-related construct.

66
Q

What is convergent validity?

A

Whether the measure correlates well with other measures of the same construct.

67
Q

What is common method variance?

A

When the same measurement method is used for two measures, part of the covariation between them may be attributable to the common method itself.

68
Q

What is discriminant validity?

A

Whether a measure is:
uncorrelated with measures of unrelated constructs, or
separable/distinguishable from (i.e., not highly correlated with) measures of related or similar constructs.

Discriminant validity is important; convergent validity alone is insufficient to establish construct validity.

69
Q

What are the five controls to validate focal constructs?

A 
Face Validity.  
Parametric IV Variation.  
Parallel DVs.
Manipulation Check.
Benchmark Check.
70
Q

What is parametric IV variation

A

As for sensitivity, Varying an IV Parametrically = Stepping systematically through the estimated effective range of the IV.
But, for construct validity, Q is whether the effect varies as expected if the IV has captured the intended construct.

71
Q

What is a benchmark check?

A

Replicate an established phenomenon, using new IV tasks, procedures, etc., to demonstrate their adequacy.

Does the IV task produce recognised effects?
Is the DV responsive to variables previously established as influential?

72
Q

What are the conditions for a confound to be preventable?

A

The confound can be varied separately from the IV; or

It is possible to make the influence of the confound comparable across conditions.

73
Q

What are converging operations? Detection strategy

A

Add conditions, IVs, DVs, measures, etc., to a study (or a series of studies) to produce different patterns of results for competing explanations

74
Q

What is discriminant validation?

A

It involves the same strategy used for validating constructs, but with an emphasis on eliminating a particular variable as an explanation.

75
Q

What is variation and replication in relation to converging operations?

A

Variation and Replication basically involves investigating the same causal relationship between constructs using multiple empirical approaches.

Every approach (every study) has weaknesses, so exact replication is not useful for eliminating alternative explanations
Different methods also have different strengths.
76
Q

What are opposing predictions in relation to converging operations?

A

There are several approaches that all involve augmenting the design to generate opposing predictions to the effect that:
If the hypothesis is correct, pattern A will be obtained, or
If a nuisance confound or rival hypothesis is responsible for the phenomenon, pattern B will be observed.

77
Q

Converging Operations: Opposing Predictions

What is the purpose of adding extra IV conditions?

A

Add a condition that will produce an effect according to the alternative interpretation, but not according to the hypothesis.

78
Q

Converging Operations: Opposing Predictions

What is the purpose of adding extra IV interactions?

A

Add an IV that will interact with an existing IV according to the theory
but will only show a main effect according to the alternative explanation.

79
Q

Converging Operations: Opposing Predictions

What is the purpose of adding extra DVs? DV pattern control

A

Add DV(s) which will be affected differently by the IV(s) according to alternative explanations.

80
Q

What are the techniques involved in Checking the Results for a Confound?

A

(a) Check Covariation of Confound with IV or DV.
(b) Assess Participants’ Awareness of Aims.
(c) Check for Observer Agreement & Validity.

81
Q

What is the purpose of the three strategies for checking results for confounds?

(a) Check Covariation of Confound with IV or DV.
(b) Assess Participants’ Awareness of Aims.
(c) Check for Observer Agreement & Validity.

A

Check Covariation of Confound with IV or DV.
If the suspected confound is not correlated with both the IV and the DV, it cannot confound the results.

Assess Participants’ Awareness of Aims.
Use a post-experimental questionnaire to check whether the participants worked out the hypothesis (e.g., Petty et al.).

Check for Observer Agreement & Validity- obvious

82
Q

What do data correction techniques involve?

A

Data correction techniques involve adjusting the raw data before conducting the focal analyses, to remove the influence of a potential confound.

83
Q

What does removing data affected by confound involve?

What does proportionalising scores achieve?

A

Removing the affected data, like lost participants to attrition, participants who figured out the hypothesis and data affected by regression simply removes these confounds

Avoids data linkage issues.

84
Q

How does proportionalising scores work?

A

Two strategies: Express Raw Frequencies as a proportion of opportunities

Comparing frequencies (e.g., the number of participants who pass a test) between groups of participants:    
Raw frequencies can be misleading if there was a different number of participants in each group.

And Expressing Individual Ps’ Scores as a proportion of opportunities

In some research studies, the number of opportunities an individual participant has to contribute to their own DV score can vary from participant-to-participant.
There is a potential confound (or counter-confound) if the average number of opportunities per participant varies systematically from one condition to another.
Hence, individual DV scores are sometimes expressed as a proportion of some other performance measure (i.e., to quantify the number of opportunities that the participant had to contribute to their own DV score).

85
Q

What are the three statistical correction techniques for correcting confounds?

A

ANCOVA (Analysis of Covariance).
Partial Correlation Techniques.
Statistical Modelling Techniques

86
Q

What are the four cautions of ANCOVA?

A

(a) Under-Correction.
The effect of the covariate may not be fully corrected.
Because of the unreliability of measures, the confound may still be contributing to group differences on the DV

(b) Assumptions of ANCOVA.
The assumptions of the test must be met, or else the results may be spurious.
Most importantly, the regression slopes (predicting the DV from the covariate) must be the same for each group (strong assumption).

(c) Additional Confounds.
ANCOVA only controls for the covariate.
Another confound of the IV-DV relationship may remain.
Therefore, ANCOVA cannot compensate entirely for non-random assignment.

(d) Efficiency Issues.

87
Q

What are partial correlation techniques?

A

Partial Correlation Techniques can be used to remove the effect of the confound from the IV and/or DV when assessing the IV-DV correlation.
Involves essentially the same ‘residualising’ technique used in ANCOVA

88
Q

What are statistical modelling techniques?

A

Statistical Modelling Techniques can be used to:
Control for the effects of confound(s).
Assess the relative importance of theoretically relevant competing constructs.
Examples:
(a) Multiple Regression.
(b) Path Analysis.
(c) Structural Equation Modelling

89
Q

What is path analysis?

A 
Path analysis is a regression-based technique used to assess mediated and direct effects among a set of variables.
The observed pattern  of correlations is compared against the pattern predicted by a causal model (based on theory).
Assess sign (+ vs. –ve) & significance of path weights. 
Test for ‘goodness-of-fit’ of data to model.
90
Q

What is Structural Equation Modelling?

A

Structural Equation Modelling (SEM) is similar to Path Analysis, but more sophisticated.
Main difference:
Paths are drawn between latent variables rather than observed variables (i.e., IVs & DVs).
Latent variables capture the common variance among multiple measures of a construct.

91
Q

What are the cautions about statistical modelling?

a) has four sub points

A

(a) Factors Affecting Strength of Relationships
i. Unreliability of Measures.
Stronger relationships may indicate nothing more than higher reliability.
ii. Common Method Variance.
e.g., If all measures are self-report.
May inflate some associations.
iii. Chance Results.
Statistical modelling is not useful with small samples.
iv. Our Ability to Think of Possible Models.
Need theoretically-motivated models – not post-hoc!

(b) Making Causal Inferences
Although Path Analysis/SEM may be referred to as ‘causal modelling’, they do not demonstrate causation.
Causation cannot be established by statistics alone

PSYC3042 (2 decks)

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