PSYC 426 Final Actual Exam Questions and
Correct Answers | Latest Updated 2026/2027 |
Graded A+ | 100% Pass.
3 components of validity - CI: Construct of Interest
SE: Systematic Error of Measurement
RE: Random Error of Measurement
example of CI and SE influence on test - you have a test of math reasoning (CI)
scores can be influenced by ability to understand instructions, vocab (SE)
classical test theory of validity definition/formula defining validity - validity is the
proportion of the relevant construct over the total variance
𝜎𝐶𝐼2 /𝜎𝑋2
Classical Test Theory Validity - ox2 = ot2 + oe2
ot= oci2 + ose2
ox2 = oci2 + ose2 + ore2
validity splits true score variance into construct of interest variance and systematic error
variance (what you want to measure and what you don't want to measure)
Validity is about figuring out what you are really measuring
why do we need a reliable measure to have a valid measure - there will be lots of
measurement error if we dont
types of validity (5) - face validity
content validity
criterion related validity (many)
,experimental validity
construct validity
face validity - test seems to be measuring what it is supposed to
extent to which a test is subjectively viewed as covering the construct it is supposed to
assess
not real validity, a test doesn't really need this
discriminant validity - the criterion assess a construct that is either the opposite of yours
(negative correlation) OR unrelated to yours (no correlation)
discriminative validity - the criterion is categorical and you want to predict group
membership
2 ways to assess discriminative validity - Mean comparisons: Groups (serving as the
criterion) are compared based on their test scores treated as continuous variables
(norm-referenced or not).
For example, one could compare engineers and musicians scores on a test of "musical
abilities" using a t-test (or ANOVA, MANOVA, etc.).
Chi-Square: Groups (serving as the criterion) are compared based on their test scores
treated as categorical variables (criterion-referenced or not).
For example, one could compare the frequency of individuals receiving a diagnosis of
bipolar disorder on the basis of their test scores in a group of psychology students and a
group of psychiatric patients.
what are the limits of face validity - bias, social desirability
content validity (what is it and how is it assessed) - The test covers key aspects of the
construct it aims to assess (includes a representative sample of the target behaviors) -
important
This form of validity is typically assessed by experts. Ask to rate items, suggest some
items, not assessed by numers
following proper principles when developing a test
criterion related validity types -
,concurrent predictive congruent convergent discriminant
discriminative
What is the usefulness of face validity - cooperation, acceptation
criterion related validity - Established via the comparison of test scores with
"objective" criterion assumed to provide some "true" reflection of the underlying
construct.
concurrent validity - your test + criterion is administered simultaneously
assessed with correlation
predictive validity - the criterion test is administered after yours
assessed with regression
congruent validity - the criterion test assess the same construct as yours
convergent validity - the criterion assess a related construct to yours (depression and
anxiety)
proportion of variance related to the construct (in context of assessing convergent and
congruent validity) - rxy2= CI2/ox2
proportion of systematic error (in context of assessing convergent and congruent
validity) - 1-rxy2
oes2/ox2
why is random error not important (in context of assessing convergent and congruent
validity) - because it's unrelated to anything else, follows normal distribution
when predictive congruent/convergent validity is assessed using a regression, how is
validity interpreted - There is always a discrepancy between the observed score on the
criterion (Y) and the score that is predicted (Y') based on the test scores (X), unless
validity is perfect (which never happens).
the extent to which there is a difference = the index of validity
, prediction error - the difference between observed score on criterion (Y) ans predicted
score(Y') based on test scores (x)
Y= a+bx+error
in a selection procedure what are we trying to predict - whether someone will be
efficient or not at work (i.e. whether that person will present or not the desired
characteristic).
what kind of validity assessment can be done on selection process efficacy - criterion-
related, leading to a decision to select, or not, a specific person.
Why: Because people presenting the characteristic will be excluded. Everyone has the
characteristic and places are limited.
how does the low base rate effect specificity - The lower the base rate, the lower the
specificity.
Why: Because people not presenting the characteristic will need to be selected to meet
the selection quotas (to fill the positions).
selection rate - Proportion of cases that are selected: (A+B) / (A+B+C+D)
how many people you will select in the end (limited positions)
long way to calculate standard error of the estimate (prediction error) - The prediction
residuals are estimated: Y' - Y.
The standard deviation of these residuals represent the standard error of the estimate.
short calculation for standard error of the estimate - √(1- rxy2) * y (sd on the criterion)
what are confidence intervals used for in predictive validity tests - Confidence
Intervals are used for the the predicted score on the criterion Y' (e.g., success on the
job)
CI go around the predicted score Y' on the criterion measure
Y' = a + b (X) [+/- (1.96 or 2.58) * standard error of the estimate].
Correct Answers | Latest Updated 2026/2027 |
Graded A+ | 100% Pass.
3 components of validity - CI: Construct of Interest
SE: Systematic Error of Measurement
RE: Random Error of Measurement
example of CI and SE influence on test - you have a test of math reasoning (CI)
scores can be influenced by ability to understand instructions, vocab (SE)
classical test theory of validity definition/formula defining validity - validity is the
proportion of the relevant construct over the total variance
𝜎𝐶𝐼2 /𝜎𝑋2
Classical Test Theory Validity - ox2 = ot2 + oe2
ot= oci2 + ose2
ox2 = oci2 + ose2 + ore2
validity splits true score variance into construct of interest variance and systematic error
variance (what you want to measure and what you don't want to measure)
Validity is about figuring out what you are really measuring
why do we need a reliable measure to have a valid measure - there will be lots of
measurement error if we dont
types of validity (5) - face validity
content validity
criterion related validity (many)
,experimental validity
construct validity
face validity - test seems to be measuring what it is supposed to
extent to which a test is subjectively viewed as covering the construct it is supposed to
assess
not real validity, a test doesn't really need this
discriminant validity - the criterion assess a construct that is either the opposite of yours
(negative correlation) OR unrelated to yours (no correlation)
discriminative validity - the criterion is categorical and you want to predict group
membership
2 ways to assess discriminative validity - Mean comparisons: Groups (serving as the
criterion) are compared based on their test scores treated as continuous variables
(norm-referenced or not).
For example, one could compare engineers and musicians scores on a test of "musical
abilities" using a t-test (or ANOVA, MANOVA, etc.).
Chi-Square: Groups (serving as the criterion) are compared based on their test scores
treated as categorical variables (criterion-referenced or not).
For example, one could compare the frequency of individuals receiving a diagnosis of
bipolar disorder on the basis of their test scores in a group of psychology students and a
group of psychiatric patients.
what are the limits of face validity - bias, social desirability
content validity (what is it and how is it assessed) - The test covers key aspects of the
construct it aims to assess (includes a representative sample of the target behaviors) -
important
This form of validity is typically assessed by experts. Ask to rate items, suggest some
items, not assessed by numers
following proper principles when developing a test
criterion related validity types -
,concurrent predictive congruent convergent discriminant
discriminative
What is the usefulness of face validity - cooperation, acceptation
criterion related validity - Established via the comparison of test scores with
"objective" criterion assumed to provide some "true" reflection of the underlying
construct.
concurrent validity - your test + criterion is administered simultaneously
assessed with correlation
predictive validity - the criterion test is administered after yours
assessed with regression
congruent validity - the criterion test assess the same construct as yours
convergent validity - the criterion assess a related construct to yours (depression and
anxiety)
proportion of variance related to the construct (in context of assessing convergent and
congruent validity) - rxy2= CI2/ox2
proportion of systematic error (in context of assessing convergent and congruent
validity) - 1-rxy2
oes2/ox2
why is random error not important (in context of assessing convergent and congruent
validity) - because it's unrelated to anything else, follows normal distribution
when predictive congruent/convergent validity is assessed using a regression, how is
validity interpreted - There is always a discrepancy between the observed score on the
criterion (Y) and the score that is predicted (Y') based on the test scores (X), unless
validity is perfect (which never happens).
the extent to which there is a difference = the index of validity
, prediction error - the difference between observed score on criterion (Y) ans predicted
score(Y') based on test scores (x)
Y= a+bx+error
in a selection procedure what are we trying to predict - whether someone will be
efficient or not at work (i.e. whether that person will present or not the desired
characteristic).
what kind of validity assessment can be done on selection process efficacy - criterion-
related, leading to a decision to select, or not, a specific person.
Why: Because people presenting the characteristic will be excluded. Everyone has the
characteristic and places are limited.
how does the low base rate effect specificity - The lower the base rate, the lower the
specificity.
Why: Because people not presenting the characteristic will need to be selected to meet
the selection quotas (to fill the positions).
selection rate - Proportion of cases that are selected: (A+B) / (A+B+C+D)
how many people you will select in the end (limited positions)
long way to calculate standard error of the estimate (prediction error) - The prediction
residuals are estimated: Y' - Y.
The standard deviation of these residuals represent the standard error of the estimate.
short calculation for standard error of the estimate - √(1- rxy2) * y (sd on the criterion)
what are confidence intervals used for in predictive validity tests - Confidence
Intervals are used for the the predicted score on the criterion Y' (e.g., success on the
job)
CI go around the predicted score Y' on the criterion measure
Y' = a + b (X) [+/- (1.96 or 2.58) * standard error of the estimate].
