Flashcards in Stats Deck (104)
How are the mean and standard deviation affected if a constant is subtracted from every score?
All operations (add, subtract, x, divide) affect mean but only multiplication and division affect standard deviation.
What happens to statistical significance when there is a large sample size?
Large sample size makes it more likely to find statistical significance. As size grows, significance can be found for even very small differences.
Criterion contamination. Refers to:
1. Hi validity coef bc ratings contaminated by knoweledge of predictor.
2. Underestimate of validity coef bc criterion rating contaminated by knowledge of predictor
3. Carryover effects
4. Due to low reliability
1. It occurs when the rating on criterion is affected by knowledge of score on predictor.
Ie may inflate grades of hi IQ kids causing hi correlation between iq and grades.
Using an ANOVA a pooled error term is justified when:
1. Sample size is unequal
2. Variance is equal
3. All cells have the same number of subjects
4. Homiscedasticity is violated.
2. Pooled error term is used when there is homogeneity of variance (equal). Homoscedacity is also equal variance.
When things Re equal they can be pooled. When unequal, treat them separately.
Match definitions below with std deviation, std error of measurement, std error of estimate, std error of the mean
1. Ave amt of error in predicting each persons score
2. Ave amt of error in calculating each score
3. Ave amt of error in grp means iq score
4. Ave amt of spread in grp scores.
1. Predicting is std error of estimate
2. Std error of measurement...ave amt of error in each persons score.
3. Std error of the mean...in relation to the population mean
4. Std deviation.
What is the most significant problem in using a series of t tests to analyze a data set?
1. Experimenter wise error Type I
2. Difficulty distinguishing intx and main effects
3. Low power beta - 1
4. Violating parametric assumptions
ANOVA reduces type I error, which is additive.
More tests ya do more chance of error.
How are the mean and std deviation effected if a constant is subtracted from every score?
1. Both decrease
2. Both increase
3. Mean decreases, std dev same
4. Mean same, std dev decreases
Single subject research design, which is the most significant problem?
3. Regression to the mean
4. Practice effects
The association between two variables, when ea variables association w another variable has been removed is known as:
A.analysis of covariance
B. partial correlation
C. Semi-partial correlation
D. Coefficient of determination
B. this is correlation between 2 variables when the association between a third variable and ea of the two original variables has been partialed out.
Cluster sampling involved what kind of clusters?
Naturally occuring groups and then randomly selecting from the clusters. Typically all the subjects within the clusters are sampled.
Standard errors of mean, measurement, and estimate express error in terms of:
1. Standard deviation
2. Sampling error
3. Systematic error
4. Testing situation
1. All std error express in terms of std deviation.
Sampling error applies to std error of mean only
Systematic...applies to none
Testing situation...source of error for std error of measurement only.
Std error of measurement is significantly influenced by reliability coefficient, the range of std error of measurement is:
A. -1 to 1
B. 0 to 1
C. 0 to sdx
D. 0 to sdy
Range of validity coefficient is -1 to 1
Reliability coefficient is 0 to 1.
D is range of std error of estimate.
Taylor Russell tables evaluate incremental validity: base rate, selection ratio, criterion related validity
A. Construct validity
B. criterion related validity
C. Test retest
D. Internal consistency reliability
Selection ratio...ratio of number of openings to number of apps
Low optimizes incremental validity
Higher criterion validity, better incremental.
2. Criterion related validity
For each a score, variability of b scores is equal to total variability of b scores. Conclude:
A. Error; unlikely
B. moderate positive correlation
C. Mod negative correlation
D. No correlation
D...for any a, end up w all possible b. scatter plot
Knowing a tells u nothing of b
Which will increase std error of mean?
Increase sd of population and decrease sample size
Decrease sd of population and increase sample siZe
1. Std error of mean has direct relationship w sd of population Nd indirect relationship w sample size
Std error of mean increases when sd of pop is increased and n is reduced.
Increasing test length:
A. Affects reliability only
B. reliability more than validity
C. Equal effect
B. effect on both but validity has a ceiling due to the reliability.
Two way ANOVA find differences. U conclude:
A. Main effects and may:not have intx effects
B. intx effects and may/not have main effects
C. Can be any combo of main effects and interactions
D. Neither main effects or interaction effects because may be due to chance.
C. 3 F ratios so 4 possibilities of significance. Two possible main effects and a possible intx effect. Can be any combo. Can have intx but no main effects etc..if one way ANOVA can't detect intx effect.
Abab design the concern is:
A. History and maturation
B. regression and diffusion
C. Failure of IV to return to baseline
D. Failure of dv to return to baseline
Which circumstance would it be problematic to use chi square?
A. When looking for differences between groups
B. ordinal data
C. Repeater observations made
D. More than one iv
C. One of the main assumptions is independence of observations. Can't use when repeated observations are made, like a pre and post test
Chi square is non parametric test of differences used for nominal or categorical data. Can use with ordinal. Use multiple chi when more than one IV
Shape of a z score distribution is:
Can't be determined
Shape follows the raw score distribution which is not given.
Flat is for percentile ranks.
Single subjects design involve an approach:
Idiographic describes single subject approaches
Nomothetic group approach
Normative...data compared with in and between subjects
Ipsitive forced choice format. Only gives strengths and interests within a subject and can't be used for comparisons.
3 levels of an IV and a continuous dv should be analyzed using what stats?
One way ANOVA
1. One IV w 3 levels; 1 DV continuous or scored numerically
One way ANOVA used w 1IV and 1DV
Chi is nominal data or categorical
Manova had more than one dv
Two way ANOVA is 2 IV and one dv
Factorial ANOVA more 1 IV and 1 dv
Relationship between education Nd income for clinical psychologists is?
Correlation between education and income in general?
1. Zero. Restricted range
2. Broader .3 to .5
Changes in the Variable causes changes in the
IV is input and causes changes in
Dv is output
IV is manipulated
Dv is measured
Correlational research variables are not manipulated. Input variable is IV . Called predictor variables.
Outcome variables are dv or criterion variables.
Regardless...what effect does (IV) have on (Dv).
What is a factorial experimental design?
Adv of factorial?
More than one IV where every level of one IV is combo w every other level of IV .
What are the adv ? Statistical in nature.
What is internal validity?
What are the threats to internal validity?
How do you control for threats to internal validity?
Allows the conclusion that there is a causal relationship between the IV and dv.
Or if can conclude that no effect.
Threats: (hims teds)
Factors other than IV are responsible for changes in dv:
History (external event)
Maturation (internal event..fatigue, bored, hunger)
Testing (experience w pretest)
Instrumentation (change nature of it)
Statistical regression (less extreme scores when retested)
Selection (preexisting subject characteristics)
Differential mortality (diff of drop outs and non drop outs)
Experimenter bias (expectation or other bias)
1. Random assignment (equivalent on extraneous factors)
2. Matching or grp similar subjects on extraneous and randomly assign
3. Blocking or study as if extraneous is another IV
4. Hold extraneous variable constant or use only homogeneous subjects
5. Ancova...like post hoc matching
What does a confound mean?
Experiment contaminated by an extraneous variable is confounded.
What is a threat to internal validity for a pretest/post test design? This is a one group before and after design.
Testing...when pre and post are similar may show improvement due to experience w the test. Test wise
Instrumentation...raters may have improved by post test
Pygmalion in the classroom:
1. Experimental expectancy
2. Impact of maturation
3. Confounding variable
4. Unequal selection of students
Another name for it?
1. Correct! Teachers preconceived ideas of a students abilities resulted in the graded and even iq scores moving in the expected direction even though the students hadnt changed.
3. It is a confounding variable! Yes
Also called rosenthal effect. Behavior of subjects changes due to expectancies.
Overcome w double blind study