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Why has psychology been criticized for not being a legitimate scientific enterprise? Mention Meehl's criticisms,

Meehl’s Criticisms
-With a large enough sample size anything can be rejected, there will always be a difference between 2 means
- findings are context dependent
-null is technically always wrong
-hard to refute theories
*Theories are never refuted nor corroborated, conflicting and just creates more “rubble”
*Prone to bandwagons and sinking ships


Why has psychology been criticized for not being a legitimate scientific enterprise? Mention measurement precision, and hypothesis

Measurement Precision and hypothesis testing
-In psychology more precision leads to a better chance at finding a significant difference, whereas in the physical sciences more precision makes it harder to find a difference.
- As our procedures get better, more powerful, more likely to find null to be false
- More precise = more likely to reject the null
-Physics makes point predictions whereas psychology tests for zero’s.
-In psychology null statistical hypotheses are not derived from substantive theories thus its rejection would not increase the plausibility of the substantive theory.


Why is cumulative progress in psychology slow?

-We rarely refute theories
-Due to money and resources, large sample sizes are hard to get. This in combination with significance testing produces a large amount of conflicting results.
-Humans are harder to study
-In psychology there may be many multiway interactions needed to explain/predict a certain behavior
-Theories themselves are rarely put to the test, auxiliary theories are.


What is the problem regarding core and auxiliary theories in psychology vs. other sciences?

*There is a large gap between core and auxiliary theories in psychology vs the hard sciences. Auxiliary theories are loosely derived from substantive theories.
*Independent testing of auxiliary theories is harder to do in psychology because so many variables are at play.


Describe Lakatos' view of scientific progress and the historical perspective

-Scientists tenaciously cling to their theories
-Theories are never abandoned after refuting evidence due to a protective belt of auxiliary theories
-Theories that propose novel content but are not corroborated with core theories are called ad hoc theories
Theories must explain previous findings and build upon those findings in order for progress to be made


Describe positive and negative heuristics

*Defensive reaction to refuting evidence by attributing the problems to less important features, blaming auxiliary theories not the core.
*Ex. Blame participants, too small of sample size, sensitivity of measure (usually blame auxiliary factors)
*How we can modify the core to account for our findings while still preserving the core, adjusting or expanding core.
*Ex. Theory true for men not women, or parents are not the only reason children become psychopaths (need to look at more factors


Describe Progressive and degenerating research

*Theories that expand
*A theory remains progressive as long as its positive heuristic is still capable of anticipating novel facts
*Theories that are no longer moving forward and have stalled
*However it is considered okay to cling to a degenerating research program if no rival program exists that satisfies all the findings.
When a competing theory supports and explains all the other stuff
Sign of a degenerating research program (one that isn’t progressing) → not able to account for new emerging evidence, use negative heuristics (always blame auxiliaries, always on the defense), not expanding


Describe Crucial tests

-Crucial tests pit one theory against another to see which is correct
*Ex. Einstein’s theory of relativity and the solar eclipse
-The need for a crucial test is usually seen in hindsight; history sometimes dictates if a test was a crucial one of not


How is Lakatos' view relevant to the criticisms of psychological science vs. other sciences?

-hard sciences cling to their theories just as much as psychology
-The notion that physical sciences are consistent or cumulative is not supported and the claim that psychological sciences are inconsistent or non-cumulative is not supported either.
-Critics hold an idealized version of research practices in physics as a standard by which psychology should follow however research in physics as shown similar deficits in methodology proportional to those found in psychological research.


What is construct validity and how is it related to other forms of validity?

a broad concept encompassing all forms of validity, essentially depicting the accuracy of the measure. Is the measure measuring what it is supposed to be measuring?
-As our understanding of the construct becomes clearer, measures shift to make them more accurate.
-It is a process rather than an endpoint
- It should be consulted at all points of developing a measure, not just at the end
- It is best understood as an over arching concept of all types of validity
three aspects of construct validity; substantive validity- ( lit reviews) structural validity, and external validity


Why is construct validity not a static quality of a test that can be established with a single study?

-Construct validity is dynamic.
-When scales are constructed some aspect of the theory will be valid but not all will support the theory
- researcher must decide whether the fault lies with the test or the theory
-one cannot just throw away years of work because a single study didn’t support the theory
-construct validity is acquired by rigorous testing of alternative hypotheses to further our understanding of the construct.


When is construct validity typically assessed and when should it be?

-Construct Validity is typically considered after the test has been constructed, a post-hoc fashion. However, it is more appropriately considered a process than an endpoint.
-Construct validity should be considered at all stages of the scale construction process


Why is a lit review an important first step in scale construction?

- reveals whether psychologists already have a reliable scale for measuring a specific construct, thus if they do, development of a new scale may not be necessary.
-However, a new scale may be important if previous scales define the construct differently or are measuring ranges that are too narrow or too broad
-The literature may also reveal if new scales are needed to advance a theory or cross-validation with other measures of the same construct
-It can develop a clear conceptualization of the target constructs.
-although one may already have a general sense of the concept, literature reviews may be helpful in considering alternative explanations.


Why is it useful to write a formal definition of a construct in the very early stages of test development?

-A formal definition helps finalize the construct and define its breadth and scope.
-The researcher can define lower order components of the construct such as subcomponents, allowing the conceptualization of the construct to expand to include all overlapping subcomponents.


Why should initial item pools be over-inclusive?

-so every possible aspect of that construct is covered and the boundaries of that construct defined
- As analyses are run, weaker and unrelated items can then be dropped from the final scale
- You can always take items away but you cant add new ones throughout.


What is content validity?

the degree to which a measure is representative to all the possible facets of a target construct.
-Related to relevancy, the appropriateness of a measure’s items when measuring the target construct. All items in the measure should fall within the target construct.
-Also relate to representativeness, the degree to which the item pool adequately samples content from all aspects of the target construct. Often used in the form of subscales.


Why should items with extreme endorsement probabilities not be automatically dumped?

- Often removed because researchers believe they don’t offer much information
- may be beneficial to know who are the five percent who responded oppositely, ex. To a question on suicidal thoughts
- many measures are tested across a wide variety of individuals (college student to psychiatric patients) and these groups may differ on their average trait levels, thus excluding that item may dispose of important information regarding a certain group of people
- It is important to look at people on extreme ends too


What are the pros and cons of dichotomous response formats?

-Easier scoring and analyses (computers have made this obsolete)
-Less time consuming
-Less reliable (Must make the scales longer in order to achieve same reliability as polytomous scales)


Describe the rational-theoretical method of item selection

a method in which the scale developer simply writes items that appear to be consistent with the target construct.
-Good convergent validity
-Poor discriminant validity


Describe the Criterion-keying method of item selection

items on selected for a scale based solely on their ability to discriminate between individuals in a “normal” group and those from the group containing the construct being measured
-Item content is irrelevant
-Good discriminant validity
-Good convergent validity
-Measures are atheorectical and don’t advance psychological theory in a meaningful way
-Scales are highly heterogeneous, lack internal consistency, making proper interpretation of scores difficult


What is factor analysis and why is it a useful tool in scale construction?

A statistical procedure used to identify clusters or groups of related items on a test. It is extremely useful for producing homogenous scales with good discriminant validity.


Describe the features of good and bad candidate items

-Good Candidate Items
-items that load moderately on a primary factor and only minimally on other factors
-Bad Candidate Items
-items that load weakly with the hypothesized factor or cross-loads on other


Describe the kind of factor analysis results that are required for measures of broad constructs that have subscales.

Groupings must be homogenous and correlated for a broad construct to be broken into subscales.


Differences between internal consistency and homogeneity?

-Internal Consistency
-measured by Cronbach’s alpha
-indicates the overall degree of interrelation between a set of items
-examines the average inter-item correlation and the number of items on the scale
-uses internal consistency to establish homogeneity
-indicates the extent to which all of the items on a given scale tap a single facet of a target construct.
-examines the mean of the distribution of the inter-item correlations.


How will the inter item correlation matrix differ for uni- and multi-dimensional pools of items?

-there may be significant variability in inter-item correlations in a multi-dimensional pool of items whereas a uni-dimensional pool of items would cluster around the average.


What is examined in the external validity phase and how is the task different from the task in the structural validity phase?

-examines the relationship between the new measure and important test and non-test criteria are congruent with one’s theoretical understanding of the target construct and its position with respect to other similar and dissimilar constructs called the nomological net.
-structural validity phase involves analyses of items WITHIN the new measure.


Convergent validity

the extent to which a measure correlates with other measures of the same construct


Discriminant Validity

the extent that a measure does not correlate with measures of other constructs.


Predictive validity

The extent to which a measure can predict a criterion occurring in the future.


Concurrent validity

Relating a measure to criterion evidence collected at the same time as the measure itself.