Stats

Question 0

What happens to statistical significance when there is a large sample size?

Answer 0

Large sample size makes it more likely to find statistical significance. As size grows, significance can be found for even very small differences.

Question 1

How are the mean and standard deviation affected if a constant is subtracted from every score?

Answer 1

All operations (add, subtract, x, divide) affect mean but only multiplication and division affect standard deviation.

Question 2

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

Answer 2

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.

Question 3

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.

Answer 3

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.

Question 4

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.

Answer 4

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.

Question 5

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

Answer 5

1

ANOVA reduces type I error, which is additive.

More tests ya do more chance of error.

Question 6

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

Answer 6

3

Question 7

Single subject research design, which is the most significant problem?

1. Autocorrelation
2. Multicollinearity
3. Regression to the mean
4. Practice effects

Answer 7

1

Question 8

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

Answer 8

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.

Question 9

Cluster sampling involved what kind of clusters?

Answer 9

Naturally occuring groups and then randomly selecting from the clusters. Typically all the subjects within the clusters are sampled.

Question 10

Standard errors of mean, measurement, and estimate express error in terms of:

1. Standard deviation
2. Sampling error
3. Systematic error
4. Testing situation

Answer 10

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.

Question 11

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

Answer 11

C

Range of validity coefficient is -1 to 1

Reliability coefficient is 0 to 1.

D is range of std error of estimate.

Question 12

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.

Answer 12

2. Criterion related validity

Question 13

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

Answer 13

D…for any a, end up w all possible b. scatter plot

Knowing a tells u nothing of b

Question 14

Which will increase std error of mean?
Increase sd of population and decrease sample size

Decrease sd of population and increase sample siZe

Increase both
Decrease both

Answer 14

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.

Question 15

Increasing test length:
A.  Affects reliability only 
B.  reliability more than validity
C.  Equal effect
D.  Neither

Answer 15

B. effect on both but validity has a ceiling due to the reliability.

Question 16

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.

Answer 16

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.

Question 17

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

Answer 17

4

Question 18

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

Answer 18

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

Question 19

Shape of a z score distribution is:
Normal
Skewed
Flat
Can't be determined

Answer 19

Shape follows the raw score distribution which is not given.

Flat is for percentile ranks.

Question 20

Single subjects design involve an approach:
Ipsitive
Idiographic
Normative
Nomothetic

Answer 20

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.

Question 21

3 levels of an IV and a continuous dv should be analyzed using what stats?
One way ANOVA
Factorial ANOVA
Chi square
Manova

Answer 21

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

Question 22

Relationship between education Nd income for clinical psychologists is?
Correlation between education and income in general?

Answer 22

1. Zero. Restricted range

2. Broader .3 to .5

Question 23

Changes in the Variable causes changes in the
Variable.

Answer 23

Independent
Dependent

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).

Question 24

What is a factorial experimental design?

Adv of factorial?

Answer 24

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.

Question 25

What is internal validity?

What are the threats to internal validity?

How do you control for threats to internal validity?

Answer 25

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)

Control?

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

Question 26

What does a confound mean?

Answer 26

Experiment contaminated by an extraneous variable is confounded.

Question 27

What is a threat to internal validity for a pretest/post test design? This is a one group before and after design.

Answer 27

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

Stat regression

Question 28

Pygmalion in the classroom:

1. Experimental expectancy
2. Impact of maturation
3. Confounding variable
4. Unequal selection of students

Another name for it?

Answer 28

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.
2. No
3. It is a confounding variable! Yes
4. No!

Also called rosenthal effect. Behavior of subjects changes due to expectancies.

Overcome w double blind study

Question 29

What is the difference between random assignment and random selection?

Answer 29

Random selection or random sampling is selecting subjects into a study. All members of the population under study have equal chance to be selected to participate. (External validity)

Random assignment is after they have been selected. For subjects already selected the probability of being assigned to ea grp is the same. Great equalizer!

Question 30

When is matching used?

Answer 30

Controls for effects of a specific extraneous variable. Identify subjects thru a pretest who are similar on an extraneous variable, group and randomly assign.

Good when sample size is small and random assignment cannot be counted on to ensure equivalency.

Question 31

How is blocking done to control for confounding variables?

Answer 31

Involves studying the effects of an extraneous variable, usually a preexisting characteristic (gender, iq) to determine if and degree acct for scores on dv.

Make extraneous a IV
Ie. divide into blocks…hi and lo iq then randomly assign to IV. Now have iq and tx as 2 IV.

Different from matching bc
Matching ensures equivalence. Number of Ivs stay the same.
Blocking determines the effects of the extraneous variable. Also adding a IV.

Question 32

Discuss holding the extraneous variable constant as a way to manage internal validity and Ancova.

Answer 32

Holding the extraneous variable constant eliminates the effects of an extraneous variable.

Include only homogeneous subjects
Ie only the high iq peeps
Disadvantage..can’t generalize

Ancova is a post hoc matching after data are obtained. Dv scores adjusted so subjects are equalized
Disadv..like matching..can’t control for what has not been identified or measured.

Question 33

What is external validity?

What are some threats to external validity?

Some may overlap w internal validity. Understand concept not need to know which classification they go to.

Answer 33

Generalizability of the results to other settings, times, people…

Interaction (some variables have one effect under one set of conditions but a different effect under another set).

Intx between selection and tx:
Given tx not generalize to other members of population (ie use college kids may not go to rest of population).

Intx between hx and tx:
Effects of tx don’t generalize beyond setting or time pd expt done.

Intx between testing and tx:
Pretest sensitization..can’t generalize to sit where pretests not used. Pretest may sensitize to purpose or increase susceptibility to respond to the tx.

Demand characteristics
Cues in research setting that allow to guess research hypothesis. Due to cues, subjects act different than real world (try disprove..)

Hawthorne effect:
Respond different just due to mere fact being studied. Study..workers increased output following any change in the environment.

Order effects or carry over or multiple treatment interference
Problem in repeated measures design or same subjects exposed to more than one tx. Last tx may have greater effect bc it followed previous interventions.

Question 34

How do you increase external validity?

Answer 34

1. Random selection or random sampling. Often use experimentally accessible population and assumption is made that subjects similar in relevant ways to rest of target population.
   Stratified random sampling.. sample from several subgroups of total population. To ensure proportionate rep of defined pop
   Cluster sampling. Natural occurring group of individuals vs individual.
2. Naturalistic research
   Controls for hawthorn and demand characteristics but will lack internal validity (always a trade off).
3. Single and double blind
   Reduce demand and hawthorn effects
4. Counterbalancing
   Controls for order effects.
   Diff subjects or grps receive tx in diff order. Type is Latin square design…order administration of tx so ea appears once and only once in every position.

Question 35

Experiemental vs quasi experimental vs correlational research.

Answer 35

Exptal..random assignment; manipulate variables

Quasi ..NO random assignment
Pre existing grps. Naturally occurring. Manipulable variable studied (decide which grp gets which tx) so experimenter control. Use w preexisting intact grps like classroom, ward..

Correlational..grps measured only
Not manipulated. No internal validity. Only associations.
Used for prediction.
Variables like age, gender, ses, eye color…

Question 36

Discuss the differences between the developmental designs. Longitudinal, cross sectional, and cross sequential. Review drawbacks of each.

Answer 36

Longitudinal..study one grp of subjects over a long pd of time.
Disadv Time, money, dropout rate.

Cross sectional..study two or more grps of ppl at one time
Disadv…cohort effects..experience effect vs their age

Cross sequential..combo of the above..look at ppl of diff ages at 2 plus times
Controls for cohort. Cheaper, less expensive, decreases drop out.

Question 37

What is a time series design?

Interrupted time series design?

Advantage of multiple measurements?

Answer 37

Multiple measurements over time to assess IV
Usually multiple pre and post test measures

Interrupted time series design is series of measurements on dv that is interrupted by administration of tx

One grp interrupted time series design. Threat is an event that occurs at same time as tx. Ie. price of cigs went up when administered. Control history with two grp time series design w a control grp that also look at over time.

Adv? Rule out threats to internal validity like maturation, regression, testing.

Question 38

What is a single subject design?

Answer 38

Number of subjects is one or if two or more subjects treated as one group.
Dependent variable measured several times during both phases
Lots of variability poses threat to design.

Good for behavior modification study.

Usually baseline (no tx) and then tx phase.

Types: AB, reversal, multiple baseline

Question 39

Describe the the single subject designs.

Answer 39

AB single baseline, single tx phase
Disadv..history, other confound

Reversal or withdrawal design
Single subject design
Treatment is withdrawn and data collected to det if behavior goes back to original level upon withdrawal. If returns to original level during wdrawal then more sure due to tx vs extraneous factors
ABA
ABAB. Tx reapplied at end. Adv over ABA is additional confirmation tx causes changes. Also don’t leave them wo tx.

Multiple baseline used when reversal can’t be. So can’t reverse or withdraw tx due to ethics. May not be possible to demo tx effect in a reversal. So instead of a withdrawal, this applies the tx sequentially or across different baselines. In other words…single subject design in which IV is sequentially administered across 2 or more subjects, behaviors, or settings (baselines)

Question 40

What is qualitative research?

Talk about the types, especially surveys and case studies.

Answer 40

Descriptive research
Theory is developed from the data vs being derived beforehand.

Often pilot to help define ho

Types:
Participant observation 
Nonparticipating observation
Interviews 
Surveys..personal, phone, mail 
Biased selection or sampling is problem
Case studies ...case is example of more general class.  Can't draw conclusions between variables and may not be generalize able.  
Protocol analysis ...verbatim reports
No traditional quantitative techniques but based on interpretation.

Question 41

What are the differences between stratified random sampling and clustering?

Answer 41

Stratified random sampling is a population divided into sub populations and all members of ea sub population have an equal probability of being chosen.

Clustering involved grouping subjects who are similar in terms of status on an extraneous variable and then assign to each grp. Naturally occuring grps (vs individual)

Question 42

Greatest threat to validity for a mail survey is?
Selection
Maturation
Randomization
Instrumentation

Answer 42

Selection

Question 43

Defining feature of a true experimental design is
Random selection of subjects from the population
Random assignment of subjects into experimental grps
Use of manipulated variables
Use of non manipulated variables

Answer 43

B

Not c because other designs use manipulated variables

Question 44

All are true of multiple baseline except?
Tx sequentially applied
May serve as a substitute when the ABAB design is unethical
May involve studying the same tx for different behaviors in diff settings, or w different subjects
Involve the administration and then withdrawal of tx

Answer 44

D. No withdraw of tx

Question 45

Major threat to internal validity of a one grp time series design is
Maturation 
Regression to the mean
History
Testing

Answer 45

C. Administer multiple pretests and post tests to one grp of subjects before and after tx. Design controls for many threats such as maturation, testing, stat regression .
An external threat is the threat.

Question 46

Major advantage and then a major disadvantage of case studies:
Can identify variables for future research
Involve study of one individual
Do not permit conclusions about causal relationships to be made
Permit generalization of results

Answer 46

Adv. A

Disadv. C

Question 47

Name the scales of measurement.

Answer 47

Nominal…unordered categories
Dx, color, sex. Can be labeled w numbers but not ordered.

Ordinal….ordered categories
Ranks. Attitude scales.
Don’t know how much more or less
Tell how ordered but not amount between the categories

Interval….numbers arranged in order and intervals in between are equal
No absolute zero pt
Can’t multiply or divide; can add and subtract (say 50 points higher but can’t say twice as smart)
Iq, standardized test scores, temps

Ratio….numbers arranged in order and intervals between are equal.
Absolute zero pt
Can add, subtract, multiply, divide
Dollar amounts, time, distance, height, wt…

Question 48

A normal distribution is:
Called the bell shaped curve
Symmetrical
Greatest number of cases fall close to the mean
How most variables are distributed in the population

Answer 48

All

Question 49

Define negative distribution:

Define positive distribution

Answer 49

Tale tells the tale…location of the tail determines labeling

Negatively skewed
Easy test
Most scores at high end
Tail at lo end (few lo scores) so neg
Mode greater than median greater than mean.

Positively skewed
Hard test
Most scores low end (left side)
Tail at hi end bc very few hi scores… .so positive
Mean is greater than median greater than mode

Mean pulled to the tail.

Question 50

Describe the measures of central tendency

Answer 50

Mean is average.
Add all and divide by N.
Sensitive to extreme values and misleading when highly skewed.

Median..middle value
When ordered lowest to highest.
Less sensitive to extreme scores

Mode…most frequent value
Can be bimodal or multimodal.

Question 51

Measures of variability.
Range
Variance
Standard deviation

Answer 51

Range
Difference between the highest and lowest score
Affected by extreme scores
Tells nothing of distribution
Only a general descriptor.

Variance
Average of the squared differences from the mean of each score
Measure of variability of scores
Basically how the scores disperse around the mean.
Sample variance is sum (x-M) squared divided by N-1.
Probably don’t need to know formula. Just that it is variance is the square of the std deviation.

Standard deviation
Square root of variance
Thought of as expected deviation from the mean if a score chosen at random.
Higher the std deviation the more that scores in a distribution are likely to deviate from the mean.

Question 52

Define z score and how it is calculated.

Answer 52

A score is a measure of how many std deviations a given raw score is from the mean.

When all scores in the distribution are converted to a scores there will be a mean of zero and std deviation of 1. All scores below the mean will have negative scores, all scores above the mean are positive, all scores at mean are 0.

Allows comparisons

When raw scores are transformed to a scores the shape of the distribution does not change. This is called a linear transformation.

Z score is x minus M divided by standard deviation.

Question 53

Define t scores
Stanine
Percentile rank

Answer 53

T score is a standard score w mean of 50 and std deviation of 10.

Stanine divide distribution into 9 intervals. One is lowest ninth…
Mean of 5, std dev of about 2.

Percentile ranks
Percent scoring below attained raw score.
Flat or rectangular distribution. So within a given range always same number of scores
Changes shape of distribution.
Nonlinear transformation.
70 percentile is 70 percent scored below you
(Percentage is items answered correctly on a test; percentile is referenced to other scores in the distribution).

Question 54

See graph on yellow sheets on how to convert and equivalents on the normal distribution.

True or false

A change in raw score in the middle of the normal distribution results in a much greater change in percentile rank then same raw score change at distributions extreme.

Answer 54

True
In middle if your score is increased you jump over a lot more people. At hi end of distribution there are only a few scores and will jump over many fewer people.

Question 55

Converting raw scores to z scored u would be:
Linear transformation bc shape of distribution changes
Linear transformation bc shape doesn’t change
Nonlinear bc shape changes
Nonlinear bc shape doesn’t change

Answer 55

B

Percentile is nonlinear

Question 56

1000 ppl take job test with a mean of 60 and std dev of 5.  They want top 150.  Normal distribution the cut off is
55
65
70
75

Answer 56

B. around 16 percent which is z score of 1.

Question 57

Judy scores in 48 percentile.  John at 93 percentile.  Normal distribution but error.  Each gets 3 pts Added to score but no one else's.  what is true?
Both percentile ranks increase the same
Judy's will increase more
Johnny's will increase more
Neither will change.

Answer 57

B. more scores in middle of distribution and she will jump over more people.

Question 58

Inferential stats means

What is an invariable result of using samples to study populations?
Sampling error 
Standard error of the mean
Statistical regression
Type I error

Answer 58

Inferential stats allow us to make inferences about what is happening in an entire population on the basis of what we observe in the sample.
Sample stats only provide estimates of corresponding populations (sample value is called statistic and population value is called parameter).

A. Sampling error is the inaccuracy of a sample value (stat) and the population value (parameter). When use a stat some error is inevitable.
One type of sampling error is standard error of the mean.

Question 59

Define standard error of the mean

Answer 59

Type of sampling error
Difference between a sample mean and a population mean or extent to which a sample mean can be expected to deviate from its corresponding population mean.

Also called error of the mean

Must know formula!!!
SE mean equals std dev divided by square root of N (sample size).

N is 25 and sd is 10. Error is 2.
So sample obtained can be expected to deviate 2 points either way higher or lower from the population mean.
Error is smaller as sample size larger bc get closer to size of the population.
INVERSE relationship…as sample size increase, std error of mean decreases!

Question 60

Define null and alternative hypothesis

Define one and two tailed tests.

Answer 60

Null hypothesis..no difference in tx conditions. So in population the IV has no effect on dv.

Alternative hypothesis…states the opposite of the null.
Usually predicts a relationship between variables

One tailed test..stat test used when the alternative ho is directional (one mean is greater than another). Greater than or less than another mean.

Two tailed…means are different but we don’t know the direction.

Question 61

What is type I error? Type II error?
Power?
1. Erroneously accept the null hypothesis. No difference.
2. Probability of rejecting a false null hypothesis. Correctly detect tx effect.
3. Nondirectional alternative hypothesis
4. Erroneously reject the null hypothesis. Say difference exists but in reality no difference exists. Finding something when it is not there.

Answer 61

1. Type II
2. Power. Probability of not making a type II error.
3. Two tailed test
4. Type I error

Question 62

Discuss the retention region and the rejection region.

Answer 62

Retention region is the white area of the graph. If the value falls there the null ho is retained. Meaning it is kept.

If the value falls in the rejection region (defined by the alpha level), the null hypothesis is rejected. This is because the stat test has indicated that the null has only 5 % or less chance of being true or 95% chance that it is not true so we have rejected it. When reject it say reached at the significance level.

Say reject the null at the .05 level
The significance level is the probability at which we reject the null as being true.

Question 63

What factors affect power? T or F?
A. As alpha increases, power increases.
B. as alpha decreases, power increases.
C. Two tailed tests are more powerful than one.
D. Larger the sample size, greater the power.
E. smaller the difference between the population means, more likely to detect these differences (more power).

Answer 63

A. True. Higher the alpha easier to reject null
B. wrong
C. False. One tailed tests more powerful.
D. True
E. false. Greater diff between pop means more power. Can make the difference between the IV bigger. Drink glass of wine vs bottle of whiskey..

Question 64

Increasing alpha, increases power. Also means:
A. Probability of making type I error increases
B. probability of making a type II error increases
C. They both increase
D. They both decrease

Answer 64

Probability of type I goes up and type II goes down.

When set alpha consider real world circumstances. If I is more serious than II error then set alpha low. Do if research counterintuitive and contradict previous research.

Question 65

Difference between a parametric and nonparametric test.

Answer 65

Parametric. 
Test interval or ratio data.  
3 assumptions:  
Normal distribution of dv
Homogeneity of variance
Independence of observations 
Ie.  t test, ANOVA 
Most tests robust re the first 2
Last is most important

Nonparametric 
Nominal or ordinal data
Not based on the assumptions 
Distribution free tests
Generally less powerful that parametric tests
Chi squared, Mann Whitney u

Both assume samples are representative of population under study. Random selection of subjects

Question 66

Study w 400 subjects, std deviation on dependent variable is 20.  Std error of the mean is
.50
1
2
4

Answer 66

1

Question 67

Std error of the mean
Directly proportional to std dev and inversely proportional to N
Directly to std dev, directly N
Inversely to std dev, directly N
Inversely std dev, inversely N

Answer 67

A.

Question 68

Which of the following assumptions is shared by both parametric and nonparametric tests?
Normal distribution of data
Homogeneity of variance
Random assignment to groups
Random selection from population

Answer 68

D

Question 69

When stat test lacks power, this means that
Probability of type I hi
Probability of type II lo
Statistical significance will be low
Results published low

Answer 69

C. Lacks power this means the probability of type II error is hi
Or that a false null will be kept
Test won’t detect true effect
Won’t yield stat significance

Question 70

Alpha can be defined as
Probability of rejecting null when it is true
Retain null when it is true
Reject null when null is false
Retain null when null is false

Answer 70

A. Alpha is the probability of making a type I error

Question 71

Which has least meaning?
Keep null when power is low
Reject the null when power is low
Retain null when power is hi
Reject null when power is hi

Answer 71

A. Power is low unlikely to detect an effect of IV when one is present
Likely keep null
When keep null doesn’t mean u did correctly. Just means test lacked power to correctly reject

Question 72

How decide to reject or accept null ho?

Answer 72

Compare stay value to critical value table. Critical value depends on alpha and degrees of freedom.

If obtained value exceeds critical value, null rejected.

Obtained value is lower than critical, keep null or retain null.

Question 73

T test:
A.  Nonparametric test
B.  result is t ratio
C.  Three types
D.  Used if more than 2 means

Answer 73

False parametric test
Correct. T ratio
3 types:
One sample t test…compares one sample mean w known population mean. Is. Sample 35 women lawyers and compare to national lawyer ave. df=N-1.
T test for independent samples
Compare two means from unrelated samples. Ie random assign subjects into drug and placebo grp and then compare means
Df = N-2
T test for correlated samples
Related in some way (matched, pre/post). Compare pre and post means.
Df = N-1. N equals number of pairs of scores.
D. No!! Only use t test w 2 means!

Question 74

ANOVA
A. Stat test assesses difference between two means of two grps; one IV
B. 1 IV; more than 2 groups
C. 2 or more IV and all possible combo of IV administered
D. Use post hoc tests

Answer 74

A. True. One way ANOVA
one way ANOVA. Usually use t test bc easier. Yields F stat. Tells you there is a difference but not which direction.
B. ANOVA
C. Factorial ANOVA
Main and intx effects
D. ANOVA just tells difference in means. Use post hoc tests to identify exactly where the significant difference is

Question 75

What are the degrees of freedom for:
1.  One way ANOVA
Ss between
Ss within
2.  T test
One sample
Independent sample
Correlated

Answer 75

1.  K-1 (k is number if groups)
W in N -k
2.  T test 
One sample, correlated sample
N-1
Independent sample
N-2

Question 76

What is the mean square used to derive the f ratio on the ANOVA?

Answer 76

Index of variability used to derived f ratio. Equal to sum of squares divided by degrees of freedom.

F = MSB/MSW

MSB= sum of squares between/ k-1

MSW=sum of sq w in/N-k

Then compare to critical value. Higher than stat significance.
Doesn’t tell which means differ significantly just that they do.
Post hoc done if significant

Question 77

Post hoc comparisons are done when significance is found for the F stay in the ANOVA.
Pair wise comparison
Complex comparison

Usually more than one comparison is of interest. More comparisons, greater chance of type I error.
What post hoc test provides the greatest protection against inflation of type I error?

What provides enough type I protection when doing pair wise comparisons?

Answer 77

Pairwise comparison
Between 2 means

Complex comparisons
Between combined means

Protection? Scheffe is most conservative. Best protection against type I error when multiple comparisons are made. However this may increase type II error (and miss it if there is an effect)

If doing pair wise comparisons use what? Tukey.

Question 78

What are all the ANOVAs?
One way ANOVA
Factorial ANOVA 
     For repeated measures
Manova
Forms of one way ANOVA
     For repeated measures
Ancova

Answer 78

One way…1 IV and more than 2 independent grps

Factorial ANOVA 2 plus IV
For repeated measures all levels of all IV applied to single grp (or matched grps)
Mixed or split plot ANOVA…
2 plus IV; mixed…at least one between subject IV and at least one repeated measures or w in subj variable (Not variance!)

Manova. 2 or more Dv;1 or more IV
Adv (vs many one way ANOVAs) is decreasing Expter wise error rate or type I error

ANOVA repeated measures
All subjects get all levels of IV
Ancova
Stat control over one or more dv to control for effects of extraneous variables.

Two way ANOVA is 2 IV factorial

Question 79

Advantage of using factorial ANOVA over many one way ANOVAs is?
A.  Less work
B.  allows main effect and intx
C.  Less statistics
D.  Hell who knows

Answer 79

B

Main effect…effect of one IV by itself. Find these differences, if any, in the marginal means column (pg 91).

Intx effect. Effects of IV at different levels of other Ivs. Look inside boxes or cell means to find . Numbers move in same direction for both then no intx when reading across or down. Go in opposite directions. Also can draw a graph. If intx then lines cross.

When interaction effect must interpret main effects w caution. Can’t interpret them wout looking at intx

Question 80

What is the nonparametric test used for categorical data?
A.  Chi squared
B.  Mann Whitney
C.  Wilcoxon matched pairs
D.  Kruskal wallis

Answer 80

A. Used when given frequency or number of subjects w in ea category (not mean scores).
Compares observed frequencies of observations within nominal categories to frequencies expected under null.

B. compare two independent grps on dv measured w rank ordered data. Alternative to t test for independent samples.

C. Compare two correlated grps on dv w rank ordered data.
Alt to t test for correlated samples

D. Compare 2 plus independent grps on dv w rank ordered data.
Alt to one way ANOVA.

Question 81

Describe chi squared.
Single sample
Multiple sample

What cautions are needed when using it?

Answer 81

Nominal data. Frequencies of observations w in a category. Test ho that observed frequencies equal those expected of null true.

Single sample…from one grp of ppl
Df = C - 1. (Categories)
Multiple sample…adding another variable in addition to one that gives rise to classification categories
Df = (C-1) (R-1). R is rows

Caution:

1. All observations must be independent of ea other
2. Ea observation only in one category or cell
3. Percentages of observations w in categories can not be compared.

Question 82

May be exam question.

Determine the expected frequencies under the null ho for chi squared.

Single sample

Multiple sample

Answer 82

Single sample
Total number of subjects/ number of cells.

Coke study had 100 subj and 2 cells
100/2= 50

Multiple sample
Fe = (column total)(row total)/N
See page 97.
Most do for ea cell. Need look example.

Question 83

Nonparametric alternative to a t test for independent samples 
Kruskal wallis 
Wicoxon matched pairs
Mann Whitney u
T test for correlated samples

Answer 83

C.

See ? Pg 100

Question 84

Use if which post hoc test results in greatest probability of making a type II error?
Tukey
Scheffe
Fischers LSD
Neuman-keuls

Answer 84

Scheffe is most conservative
Greatest protection vs type I but then increases chance of type II.

Tukey. Use for pair wise comparisons. Gives enough protection against type I

Question 85

Correlation coefficient: t or f

A. Usually -1 to 1
B. Magnitude is number; higher number more related.
C. -1 is a perfect correlation
D. Zero is no relationship
E. positive coefficient..two variables move in same direction
F. Negative correlation..as one goes up, the other goes down or they vary inversely.
G. High correlation infers a causal relationship.

Answer 85

All true but g

Could mean causally correlated

If two variables are causally related, there will be a correlation between them.
Correlation is a necessary but sufficient condition of causality. So correlation does not guarantee causality but if there is a causal relationship they also must be correlated.

Question 86

Pearson r correlation:
A.  Most used
B.  interval or ratio scale data
C.  Assumes linear relationship
D.  Heteroscedasticity
E.   smaller range of sampled behavior, more accurate estimate of correlation.

Answer 86

All except d, e

Homoscedasticity …dispersion of scores is equal thruout scattergram and is an assumption of Pearson r.

(Heteroscedacity is more dispersion at some parts of scattergram or not uniform). This lowers the coefficient.

Wider range of scores the more accurate the correlation. Increase a correlation by increasing the range of scores.

Question 87

What is the coefficient of determination?
A. Percentage of variability in one measure accounted for variability in other measure.
B. squared correlation coefficient
C. Includes all but reliability coefficient which is never squared.
D. Example. Coefficient is .70 so 49 % of variation of x explained by y.

Answer 87

All

Question 88

Regression equation
A. Allows prediction of unknown value of one variable from known value of another
B. one is independent or predictor variable; one is dependent or criteria variables
C. Criterion is that which is being predicted
D. Unless correlation is perfect (1 or -1) there will always be some error. Considered estimates.
E. higher correlations are, more accurate the predictions made will be by regression equation.
F. Linear relationship noted by a straight line in scattergram. If as close to as many dots as possible called line of best fit or regression line.

Answer 88

All correct.
Regression line is pic of overall relationship between 2 variables. Higher correlation, closer dots and more accurate at predicting y.

Error is diff between predicted and actual criterion scores. Error scores assumed normally distributed w a mean of zero. Assume homoscrdastic.
Regression line is least amount of error in predicting y scores from x.
Regression can be used as sub for ANOVA.

Question 89

Multiple R:
A. Another name for multiple correlation coefficient.
B. use of scores on more than one predictor to estimate scores on criterion
C. Multiple R is highest when predictor variables each have hi correlations w criterion but low w ea other.
D. Lo correlations between predictors are desired bc better when ea predictor provides new info about the criterion.
E. multiple correlation coefficient is never lower than highest simple correlation
F.cant be negative

Answer 89

A. True
B. multiple regression
C. True
D. True. If predictors overlap/hi correlations w ea other combining them yields no significantly new info
Significant predictor overlap is multicollinearity. One of predictors past 3 or 4 bound to have hi correlation w one of others.
E true
F true
G true coefficient of multiple determination. Gives proportion of variance in criterion variable accounted for by combo of predictor variables.

Question 90

What is stepwise regression?
Advantages

Discuss forward stepwise and backward stepwise

Answer 90

Large number of potential predictors but use smaller set.
Goal to get smallest set that maximizes predictive power. Those w hi multiple correlation w criterion.

Adv
Time, money
Multicollinearity..adding gives no more power

Forward…start w one predictor and add predictors one at a time. W ea you do analysis to determine predictive power of multiple regression is substantially increased. First kept has highest correlation w criterion. Add til no more predictive increase

Backward. Start w all potential predictors and remove one at a time. When starts to significantly decrease R stop removing.

Question 91

Match the technique w the correlational technique;
A. Goal to classify individuals into grps on basis of scores on multiple predictors. Differential validity..ea predictor has different correlation w ea criterion variable. Continuous variables.
B. examines trend of change in dependent variable (vs if dv changes at all)
C. Assess relationship between 2 variables with the effects of another variable partialled out (stat removed). Remove suppressor variable may contribute to misleadingly hi or lo correlation.
D. Calculate relationship between 2 plus predictors and two plus criterion variables
E. set minimum cut off on a series of predictors. If not met on even one, not selected
F. Set of techniques calculating the pair wise correlations between multiple variables. Causal modeling, path analysis, liSeral
G. Classify into grps; no assumptions met; can be nominal data ; value between 0 and 1.

Answer 91

A.  Discriminate function analysis
B.  trend analysis
C.  Partial correlation 
D.  Canonical correlation
E.  multiple cutoff
F. Structural equation modeling 
G.  Logistical regression

(Vs multiple correlation coefficient which is relationship between two or more predictors and one criterion)

Question 92

What is the difference between logistical regression and discriminate analysis?
A. Logistical regression uses interval and ratio data.
B. discriminate analysis assumes a normal distribution and homogeneity of variance.
C. Logistics regression assumes only homogeneity of variance.
D. Both have to do w grps.
E. logistic is used mostly w dichotomous dv or where classify into one of two criterion grps; predicted value between 0 and 1, probability belongs to one of grps

Answer 92

A. so does discriminate analysis however…logistical also used nominal or continuous
B. correct. Logistical doesn’t have these assumptions
C.nope none
D. True
E. true

Question 93

What is structural equation modeling?
Describe the types.
(Path and liSeral)

Different from trend analysis …examine trend of change in quantitative(interval, ratio) dv (vs if change at all).
Types..linear, quadratic, cubic, quaric. Tells if trends (break points or change in direction) are stat significant.

Answer 93

General term for variety of techniques that are based on correlations between multiple variables.
Assumption…linear relationship
Application…testing causal models

Steps:
Specify model w different variables usually w path diagram
Stat analysis between all pairs
Interpret results..degree data consistent w model

Types:
Path analysis. Simpler models w one way causal flows.
Observed variables only

LiSReL. One or two way
Latent and observed variables

Question 94

The difference between multiple regression and canonical correlation is?

Answer 94

Cannonical correlation coefficient is used with multiple criterion and multiple predictors.

Multiple correlation is 2 or more predictors and one criterion.

Question 95

Test a has .6 correlation w test b and correlation of .3 with test c.
Test a accounts for —- as much variability in test b as it does in c.
Twice
Three times
Four times
Eight times

Answer 95

Four time.

Square correlation coefficient

So .6 is .36 and .3 is .09.

Question 96

Which describes a correlation between x and y?
Variability of y at each x is lower than total variability of y.
Variability of y is different at different levels of x
Variability of y scores at each x is equal to the total variability of y scores.
Variability of y at each x is approximately the same.

Answer 96

C. Says…range of y at every individual x will be equal to the entire range of y. In other words, any one score on x doesn’t provide any info about y. Means correlation is 0.

B is heteroscedascity.
D is homoscedasticity

Question 97

Define sampling distribution:
A. Whole set if cases researcher is interested in.
B. set of scores obtained from the sample.
C. Distribution of values of the statistic with each value computed from same sized samples drawn w replacement from the population.
D. Theoretical distribution of the means various samples one could draw, all of equal size, from one population.

Answer 97

A. Population and can be represented in frequency distribution. Every single score in population. Population can be anything..height or cornstalk etc
B. set of scores obtained from a sample is sample distribution. Most cases it will have less score variability than pop distribution. Make a frequency distribution. Doesn’t include the full range of scores.
C. Sampling distribution. Can be done with the mean, t, F.
D. Sampling distribution of the means. Ea sample drawn will have a mean close to but not exactly at population mean. Plotting a large series of the means of these samples will yield a distribution of sample means that will approach a normal shape, be very tightly clustered around population mean, and have a mean that is equal to population mean. Use sampling with replacement and all sample sizes are the same.

Question 98

Central limit theorem:

1. As sample increases, shape of the sampling distribution of means approaches a normal shape.
2. Above is true even if the population isn’t a normal shape.
3. Mean of the sampling distribution of means is equal to the mean of the population.
4. Less variability than the population distribution.
5. Std deviation of sampling distribution of means is equal to population std deviation divided by square root of the size of the samples from which the means were taken. This is the formula for the standard error of the mean or standard deviation of the sampling distribution means.

Answer 98

All true!

Basis of statistical inference!

A test is robust or the rate of false rejections of the null/type I error rate is not substantially increased when normal distribution and homogeneity of variance are violated. Most parametric tests are robust w. as long as the sample size is adequate, still working w a normal distribution.
As N decreases, parametric tests are less robust..and important that the normality of assumption is met.
Re: homogeneity of variance…ok if equal number of subjects.
Unequal then inflated type I error.

Question 99

In the time series design the assumption for independence of observations is not met.  This leads to?
A.  Multicollinearity 
B.  heteroscedascity 
C.  Autocorrelation
D.  Homoscadscity

Answer 99

C. Means across measurements will be related and there will be a different magnitude of relationship among the means, depending on lag or how close together in time they were obtained. Correlation a between observations at given lags is autocorrelation.

Question 100

Bayes theorem should be associated with:
A.  Conditional properties
B.  base rates 
C.  Common conceptual basis
D.  Combines studies with a common conceptual basis as a larger part of a study.

Answer 100

A, b correct! Formula for conditional probabilities. Revise probabilities based on additional info.

C, d is meta analysis. analysis uses the results of each study as if they were separate scores, sums, averages. Ea study used is one subject in meta analysis
Stat yielded is effect size..gives magnitude if independent variables effect. Computed for ea dv. Then get average effect size.

Adv..allows consider size of effect
Disadv..subject to bias by analyzer, such as which studies to include

See of 137 for ?, of 140 ?

Question 101

Which is Criterion based score?
Percentile
Percentage 
Standard score
Iq score

Answer 101

B. how person does in reference to external criterion. Percentage tells is how did on a test or how much was mastered.

Others are norm referenced scores on how did compared to others. Don’t say anything about how much of the criterion is mastered.

Question 102

Defining feature of. True experiment is:
Random selection from population 
Random assignment into grps
Use of manipulated variables
Use of non manipulated variables

Answer 102

B

Also c but that applies to others as well…like quasi experiments

Question 103

Which of the following assumptions is shared by both parametric and nonparametric tests?
Normal distribution of data 
Homogeneity of variance
Random assignment to grps
Random selection from population

Answer 103

D. Both inferential stats do use to draw conclusions about a population on basis of info from the sample. Must be representative from population it is drawn. Best way is to randomly select.