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final Exam survey

Total questions: 138

Worksheet time: 1hrs 14mins

Name
Class
Date
1.

____ is the number by which we multiply (i.e., weight) each X to make a composite X with all the information from the separate Xs in it.

a)

b-weight

b)

beta-weight

c)

zero-partials

d)

semi-partials

e)

partials

2.

what is the blue representative in this image?

a)

'Unique' variance

b)

'Shared' variance

c)

semi-partial

d)

R2R^2

e)

Partials

3.

what is the Red representative in this image?

a)

'Unique' variance

b)

'Shared' variance

c)

semi-partial

d)

R2R^2

e)

Partials

4.

what is it when all the unique and shared variances are totalled

a)

'Unique' variance

b)

'Shared' variance

c)

semi-partial

d)

R2R^2

e)

Partials

5.

what will you find if all the unique varices are taken from R-Squared

a)

'Unique' variance

b)

'Shared' variance

c)

semi-partial

d)

R2R^2

e)

Partials

6.

That is this finding out?

a)

Unique varience

b)

shared varience

c)

percent of semi-partials

d)

percent of the partials

7.

That is this finding out?

a)

0.433 (43%)

b)

0.535 (53%)

c)

0.1875 (18%)

d)

0.2862 (28%)

8.

That is this finding out?

a)

Unique varience

b)

shared varience

c)

percent of semi-partials

d)

percent of the partials

9.

That is this finding out?

a)

0.433 (43%)

b)

0.535 (53%)

c)

0.1875 (18%)

d)

0.2862 (29%)

10.

If R-squired is 0.608

a)

0.433 (43%)

b)

0.135 (13.5%)

c)

0.1875 (18%)

d)

0.2862 (28%)

11.

what is 783.952 representative of?

a)

SSreg

b)

SSres

c)

SSy

d)

R-squred

12.

what is 2430.55 representative of?

a)

SSreg

b)

SSres

c)

SSy

d)

R-squred

13.

what is the formular for R-squared

a)

SSreg / residual

b)

SSres / SSy

c)

SSreg / total

d)

Total / SSres

14.

Using the formula for R-squared, what is the value of R-squared?

a)

0.323

b)

0.187

c)

0.286

d)

0.341

15.

When working out Y that is the intercept

a)

b0

b)

b1

c)

b2

d)

constant

e)

Gre_Q

16.

When working out the slops, what is used

a)

b0

b)

b1

c)

b2

d)

attending

e)

Gre_Q

17.

what is b1 here, there's 2 answers

a)

0.51X10.51X_1

b)

12.157X212.157X_2

c)

0.444X10.444X_1

d)

attendance

e)

Gre_Q

18.

what is b2 here, there's 2 answers

a)

0.51X10.51X_1

b)

12.157X212.157X_2

c)

0.444X10.444X_1

d)

attendance

e)

Gre_Q

19.

What is the residuals within the data?

a)

The Error

b)

The leftover

c)

The important info

d)

ordinal

20.

what assumption is this? Constant variance of residuals (across predicted scores)

a)

normality

b)

homoscedasticity

c)

independence of errors

d)

linearity of the relationship

21.

Wihich of the following is a regression assumption or a used for it? pick more then one

a)

Normality

b)

Homoscedasticity

c)

independence

d)

predicted

e)

residual

22.

Wihich of the following is a regression assumption or a used for it? pick more then one

a)

Normality

b)

Homoscedasticity

c)

independence

d)

predicted

e)

residual

23.

what assumption is this? The residuals are uncorrelated with Y

a)

normality

b)

homoscedasticity

c)

independence of errors

d)

linearity of the relationship

24.

What association does this describe?

Residuals (error) are normally distributed with mean = 0

Often said, cantered around 0

a)

normality

b)

homoscedasticity

c)

independence of errors

d)

linearity of the relationship

25.

Data is observed to not neatly distributed around zero, what does this mean? more then one correct answer

a)

something systematic and amiss

b)

potetial problem within the data

c)

creates a fan

d)

data has a corrilation

26.

Involve the estimation of at least one population parameter

a)

parametric test

b)

sample variance

c)

population testing

d)

non-paramotor test

27.

What is used to measure how much variance shared between the criterion and the pridictors?

a)

R squared

b)

R

c)

semi-partials

d)

unique variance

28.

What is used to measure how much variance shared between the criterion and the pridictors, measure in this data set?

a)

0.028

b)

0.143

c)

0.054

d)

0.022

29.

What is the dependent variable

a)

Psychological adjustment

b)

age

c)

personal growth scale

d)

interpersonal growth

e)

accepentance scale

30.

If you wanted to improve someone’s psychological adjustment, what would you do?

a)

Psychological adjustment

b)

age

c)

personal growth scale

d)

interpersonal growth

e)

accepentance scale

31.

what is the unique variance accounted for by interpersonal growth?

a)

0.0246%

b)

0.054

c)

-0.016

d)

0.292%

e)

2.045%

32.

what is the unique variance accounted for by age?

a)

0.0246%

b)

0.054

c)

-0.016

d)

0.292%

e)

2.045%

33.

what is the unique variance accounted for by personal growth scale?

a)

0.0246%

b)

0.054

c)

-0.016

d)

0.292%

e)

2.045%

34.

The amount of variance in the outcome variable explained by the linear composite (aka model) is refered to as:

a)

R square

b)

  • the slope coefficient

c)

  • the intercept coefficient

d)

  • the residual term

35.

The above formula provides us with:

a)

SSreg

b)

  • R2

c)

SSy

d)

SSres

36.

The above formula provides us with:

a)

SSreg

b)

  • R2

c)

SSy

d)

SSres

37.

The above formula provides us with:

a)

SSreg

b)

  • R2

c)

SSy

d)

SSres

38.

The above formula provides us with:

a)

SSreg

b)

  • R2

c)

SSy

d)

SSres

39.

which is the slope coefficient, multi part answer

a)

b0

b)

b1

c)

B2

d)

Gre_Q

e)

attendence

40.

which is the intercept coefficient, two part answer

a)

b0

b)

b1

c)

36.13

d)

Gre_Q

e)

attendence

41.

We extrapolate sample coefficients to the population to make___________

a)

  • judgements

b)

inferences

c)

  • hay

d)

  • research questions

42.

In the above diagram, the section labelled B represents

a)

  • The correlation between predictor 1 and the criterion

b)

Shared variance of predictor 1 and predictor 2 on the criterion

c)

  • Unique variance of predictor 1 with the Y variable

d)

  • Unique variance of predictor 2 with the Y variable

43.

In the above diagram, the section labelled c represents

a)

  • The correlation between predictor 1 and the criterion

b)

Shared variance of predictor 1 and predictor 2 on the criterion

c)

  • Unique variance of predictor 1 with the Y variable

d)

  • Unique variance of predictor 2 with the Y variable

44.

In the above diagram, the section labelled A represents

a)

  • The correlation between predictor 1 and the criterion

b)

Shared variance of predictor 1 and predictor 2 on the criterion

c)

  • Unique variance of predictor 1 with the Y variable

d)

  • Unique variance of predictor 2 with the Y variable

45.

Semipartial correlations are found in which part of the SPSS output?

a)

  • The partial column of the Coefficients table

b)

The part column of the Coefficients table

c)

  • The Model Summary Table

d)

  • The ANOVA table

46.

the models significance is found in the...

a)

  • The partial column of the Coefficients table

b)

The part column of the Coefficients table

c)

  • The Model Summary Table

d)

  • The ANOVA table

47.

the total variance of all the predictors is found in what tableP

a)

  • Model summary

b)

The part column of the Coefficients table

c)

  • The Model Summary Table

d)

  • The ANOVA table

48.

Semipartial correlations are used as an index of:

a)

  • shared variance

b)

  • total variance

c)

unique variance

d)

  • common variance

49.

What is the regression assumption that is tested by examining the plot of residuals against predicted values?:

a)

  • Mahalanobis’ assumption

b)

  • linearity

c)

  • independence of errors

d)

homoscedasticity

e)

  • residuals centred around 0 and normal

50.

What is the regression assumption that is tested by examining If the residuals (error) are centred around 0

a)

  • Mahalanobis’ assumption

b)

  • linearity

c)

  • independence of errors

d)

homoscedasticity

e)

  • normality

51.

What is the regression assumption that is tested by examining If the residuals (error) are uncorreltation with y

a)

  • Mahalanobis’ assumption

b)

  • linearity

c)

  • independence of errors

d)

homoscedasticity

e)

  • normality

52.

Multivariate outliers are assessed by:

a)

  • looking at residuals

b)

  • looking at box plots

c)

looking at Malahnobis distance

d)

  • looking at Z Scores

53.

To address the potential influence of multivariate outliers you should:

a)

  • transform the outcome variable

b)

  • transform the predictor variables

c)

remove the outliers from the dataset

d)

  • do nothing

54.

to address skew and kurtosis you should?

a)

  • transform the outcome variable

b)

  • transform the predictor variables

c)

remove the outliers from the dataset

d)

  • do nothing

55.

One approach to correct moderately skewed data is to apply a ...... transformation to the data

a)

  • summation

b)

  • average

c)

square-root

d)

  • moderately skewed data should not be corrected

56.

The assumption that the association between the predictors and the outcome falls along a straight line is the assumption of:

a)

  • homoscedasticity

b)

linearity

c)

  • multicollinearity

d)

  • normality

57.

The assumption that residuals have consistent variance across the predicted values is the assumption of:

a)

homoscedasticity

b)

  • linearity

c)

  • multicollinearity

d)

  • normality

58.

What will you need to do with data with a negative skew prior to applying a transformation? multi answer

a)

reflect the scores

b)

  • nothing

c)

  • check with your tutor

d)

  • check for linearity

e)

use log, must be all psitive

59.

Multivariate outliers...

a)

  • are data with extreme values on multiple variables

b)

are data with an unusual combination of scores on multiple variables

c)

  • are always also univariate outliers

d)

  • are not problematic and can be safely ignored

60.

univariate outliers...

a)

  • are data with extreme values on a variables

b)

are data with an unusual combination of scores on multiple variables

c)

  • the result with outliers left in is significant

d)

  • are not problematic and can be safely ignored

e)

  • the result with outliers left in is non-significant

61.

Remove univariate outliers if their removal ......

a)

  • does not change the significance of the results

b)

changes the significance of the results

c)

  • the result with outliers left in is significant

d)

  • the result with outliers left in is non-significant

62.

Ancombe’s............demonstrated that we need to look at the scatter plot (rather than just looking at our descriptive statistics) to check for non-linearity and outliers.

a)

quartet

b)

  • duet

c)

  • law

d)

  • test of residuals

63.

Phi is:

a)

  • the correlation in the sample

b)

  • the correlation in the population

c)

  • an index of effect size in a multiple regression analysis

d)

an index of effect size in a chi-square analysis

64.

what do these measurements tell us?

a)

  • the correlation in the sample

b)

  • the correlation in the population

c)

  • an index of effect size in a multiple regression analysis

d)

an index of effect size in a chi-square analysis

65.

This kind of correlation can be used to account for extreme scores in your data

a)

  • point-biserial

b)

Spearman

c)

  • Pearson

d)

  • monotonic

66.

This kind of correlation can be used when one of your variables is dichotomous

a)

  • point-biserial

b)

Spearman

c)

  • Pearson

d)

  • monotonic

67.

Spearman’s Rho is based on the use of:

a)

  • continuous data

b)

ranked data

c)

  • frequency data

d)

  • ordinal data

68.

_______tests involve estimating population parameters, making assumptions about the shape of the data and assumptions about the scale of the variables

a)

Parametric

b)

non-parametric

c)

spearman's rho

d)

chi-squared

69.

what is used to calculate the correlation between variables htat has natural ranks, there are extreme scores in the sample

a)

Parametric

b)

non-parametric

c)

spearman's rho

d)

chi-squared

e)

Pearson's r

70.

what is used to calculate the correlation between variables that has a monotonic relationship between the variables

a)

Parametric

b)

non-parametric

c)

spearman's rho

d)

chi-squared

e)

Pearson's r

71.

what is appropriate for describing the linear relationship between two continuous variables

a)

Parametric

b)

non-parametric

c)

spearman's rho

d)

chi-squared

e)

Pearson's r

72.

Frank wants know whether there is a relationship between sample frequencies he has obtained on promotion to manager and on gender. He should run…

a)

  • the one-way chi square with equal expected frequencies

b)

the two-way chi square test

c)

  • the one-way chi square with specified expected frequencies

d)

  • Fisher’s exact test

73.

Expected frequencies in two-way chi-square tests are calculated by

a)

he product of the relevant marginal totals weighted by the grand total

b)

  • random assignment with a sampling frame

c)

  • starting with the values from the appropriate oneway test

d)

  • using ABS data

74.

If the expected frequencies equalled the observed frequencies exactly in a one-way chi-square:

a)

  • the chi-square test statistic would be undefined

b)

the calculated chi-square would equal zero

c)

  • the null hypothesis would probably be rejected

d)

  • the degrees of freedom would be zero

75.

According to the table, women are more likely to be over 65?

a)

true

b)

false

76.

What data does chi square allow us to test?

a)

continuous data

b)

ranked data

c)

ordinal data

d)

frequency data

e)

categorical data

77.

what test do we use when people/things distribute evenly across the categories of the variable.

a)

chi-squared

b)

point-biserial

c)

fundamental of congitudinal

d)

fisher's exect test

78.

what is also referred to as the test of independence

a)

two-way chi-squared

b)

point-biserial

c)

fundamental of congitudinal

d)

fisher's exect test

79.

We _____ the null hypothesis of independence and conclude that our sample represents a population in which gender and being over 65 are associated.

a)

reject

b)

accept

80.

under measurement levels,

Nominal data is....

a)

categorical

b)

continuous

c)

A frequency

d)

vaiable

81.

under measurement levels,

ordinal data is....

a)

categorical

b)

continuous

c)

A frequency

d)

vaiable

82.

Under measures levels.

what refers to: measures are just names for things e.g. gender

a)

nominal

b)

ordinal

c)

interval

d)

ratio

83.

Under measures levels.

measure the rank ordering of something e.g. 1st/2nd place

a)

nominal

b)

ordinal

c)

interval

d)

ratio

84.

Under measures levels.

Equal difference between scores can be treated as equal units e.g. degrees C

a)

nominal

b)

ordinal

c)

interval

d)

ratio

85.

Under measures levels.

interval scale with true zero point e.g. length

a)

nominal

b)

ordinal

c)

interval

d)

ratio

86.

TO test the significance of chi squared.

a)

The obtained value is compared to the critical value in the chi square distribution

b)

Compares obtained frequencies to expected frequencies as stated under the null hypothesis

c)

Depend on the number of categories

87.

In a two way chi squared can participance be in more then one?

a)

No they can not

b)

yes they can

88.

What is it when a two way chi square, when looking at gender and salary, share the same shape.

i.e salary levels are not dependent on gender

a)

independent

b)

dependent

c)

one way chi

d)

expected frequence

89.

What is it when a two way chi square, when looking at gender and salary, dont share the same shape.

i.e salary levels depends on gender

a)

independent

b)

dependent

c)

one way chi

d)

expected frequence

90.

With in chi square, which model asks; generally, does the observed data fit the model

a)

one-way

b)

Two-way

91.

With in chi square, which model asks; are the two variables independent?

a)

one-way

b)

Two-way

92.

What refers to:

The degree of CONSISTENCY in scores, means, or rank orders from one time point to another.

a)

stability

b)

change

c)

Uni-directional relational relationship

d)

Bi-directional relationship

93.

What refers to:

The degree of FLUCTUATION in scores, means, or rank orders from one time point to another.

a)

stability

b)

change

c)

Uni-directional relational relationship

d)

Bi-directional relationship

94.

What refers to:

  • - there is a clear direction in the relationship between the predictor and criterion variable.

  • - A uni-directional relationship in a well-design longitudinal study provides support for temporal precedence

a)

stability

b)

change

c)

Uni-directional relational relationship

d)

Bi-directional relationship

95.

What refers to:

  • - Occurs when the predictor variable is related to the criterion variable, and the criterion variable is related to the predictor variable.

  • - In this instance, it is not possible to conclude that one variable occurred prior to the other, so temporal precedence cannot be determined. rather both variables 'cause' one another

a)

stability

b)

change

c)

Uni-directional relational relationship

d)

Bi-directional relationship

96.

What is also refered to as autoregressive design

a)

simplex models

b)

longitudinal correlation

c)

Residualised longitudinal regression

d)

Cross-lagged models

97.

What measurement of a variable at time 1 should predict the time 2 (i.e. stability)

a)

simplex models

b)

longitudinal correlation

c)

Residualised longitudinal regression

d)

Cross-lagged models

98.

I.e. there is a high degree of stability from T1 to t2.

In a simplex design what does this mean?

a)

perfect association

b)

small or zero association

c)

bi-directional relationship

d)

significant

99.

I.e. there is a small or zero association

In a simplex design what does this mean?

a)

perfect association

b)

small or zero association

c)

bi-directional relationship

d)

significant

100.

is this weak or strong

a)

weak

b)

strong

101.

is this a weak or strong relationship, temporal, stability

a)

weak

b)

strong

102.

If the relationship between IV at time 1 and DV at time 2 is significant AND the relationship between the DV at time 1 with the IV at time 2 is not significant, it can be concluded that what has been found?

a)

temporal precedence

b)

bi-directional relationship

c)

uni-directional relationship

d)

nothing at all.

103.

If the relationship between IV at time 1 and DV at time 2 is significant AND the relationship between the DV at time 1 with the IV at time 2 is significant, it can be concluded that what has been found?

a)

temporal precedence

b)

bi-directional relationship

c)

uni-directional relationship

d)

nothing at all.

104.

If the relationship between IV at time 1 and DV at time 2 is not significant AND the relationship between the DV at time 1 with the IV at time 2 is not significant, it can be concluded that what has been found?

a)

temporal precedence

b)

bi-directional relationship

c)

uni-directional relationship

d)

nothing at all.

105.

which model has the weakness of;

  • - This analysis does not account for correlations between variables at time points.

  • - Does not account for the stability in a construct

a)

longitudinal correlation

b)

simples design

c)

residualised longitudinal regression

d)

cross-lagged models

106.

what model does this refer to, remember to think of why that is.

a)

simple design

b)

Longitudinal Correlation

c)

residualised longitudinal regression

d)

cross-lagged

107.

what model does this refer to, remember to think of why that is.

a)

simple design

b)

Longitudinal Correlation

c)

residualised longitudinal regression

d)

cross-lagged

108.

what model does this refer to, remember to think of why that is.

a)

simple design

b)

Longitudinal Correlation

c)

residualised longitudinal regression

d)

cross-lagged

109.

what model can test for bi-directional relationships

a)

simple design

b)

Longitudinal Correlation

c)

residualised longitudinal regression

d)

cross-lagged

110.

what is a cross-lagged model especially like?

a)

two simple design

b)

two Longitudinal Correlation

c)

two residualised longitudinal regression

d)

a one-way repeated anova

111.

what does a cross-lagged model investigate?

a)

Correlation between variables at time 1

b)

correlation between variables at time 2

c)

Stability of variable 1

d)

stability of variable 2

e)

literally every comparison between variables

112.

what is the most expensive to run and require additional resources outside of SPSS

a)

simplex

b)

Longitudinal correlation

c)

Residualised regression

d)

Cross-lagged model

113.

what only estimates stability and change

a)

simplex

b)

Longitudinal correlation

c)

Residualised regression

d)

Cross-lagged model

114.

what only explores uni-directional relationships

a)

simplex

b)

Longitudinal correlation

c)

Residualised regression

d)

Cross-lagged model

115.

what estimates stability, estimates change, predicts change, accounts for cross-sectional relationships, explores unidirectional relationships, but des NOT explore bi-directional reationships?

a)

simplex

b)

Longitudinal correlation

c)

Residualised regression

d)

Cross-lagged model

116.

what estimates stability, estimates change, predicts change, accounts for cross-sectional relationships, explores unidirectional relationships, and explores bi-directional reationships?

a)

simplex

b)

Longitudinal correlation

c)

Residualised regression

d)

Cross-lagged model

117.

in a longituditudinal analysis, which you should be well aware of right now. what refers to this assumption?

  • - These techniques examine stability and change in the sample over time.

  • - Thus, it is assumed that there are no systematic differences in the stability and change between the participants

  • - This assumption is violated when one group of participants changed faster or slower relative to the other participant.

a)

inter-individual stability

b)

consistent measurement

c)

synchronicity

d)

timeframe

e)

other variables (third variable effects)

118.

in a longituditudinal analysis, which you should be well aware of right now. what refers to this assumption?

  • - it is assumed that the measurement is the same when using repeated measures

  • - In practical terms, this means that the items administered to participants need to be exactly the same across time periods

  • - in conceptual terms, participants need to read and interpret the questions in exactly the same way across time

a)

inter-individual stability

b)

consistent measurement

c)

synchronicity

d)

timeframe

e)

other variables (third variable effects)

119.

in a longituditudinal analysis, which you should be well aware of right now. what refers to this assumption?

  • It is assumed that the administration of questionnaire occurs with the same interval between time periods for all participants

a)

inter-individual stability

b)

consistent measurement

c)

synchronicity

d)

timeframe

e)

other variables (third variable effects)

120.

in a longituditudinal analysis, which you should be well aware of right now. what refers to this assumption?

  • - in order to find a longitudinal effect, the length of time between measurements needs to be considered

  • - the timeframe may be too short for a variable to impact anther, and so you won't find any effects

  • - The timeframe may be too long and the impact of one variable on the other may have dissipated

a)

inter-individual stability

b)

consistent measurement

c)

synchronicity

d)

timeframe

e)

other variables (third variable effects)

121.

in a longituditudinal analysis, which you should be well aware of right now. what refers to this assumption?

  • - it is important to ensure that important variables are not omitted

  • - this idea is related to a causal process or pathway

  • - for this reason, it is easier to say that a variable at time 1 influences or impacts something, rather than causing it.

a)

inter-individual stability

b)

consistent measurement

c)

synchronicity

d)

timeframe

e)

other variables (third variable effects)

122.

Which of the following is NOT a longitudinal design:

a)

  • A simplex model

b)

A repeated measures chi-square

c)

  • A residualised longitudinal regression

d)

  • A cross-lag model

123.

A simplex model:

a)

  • predicts change over time

b)

  • accounts for cross-sectional relationships

c)

estimates change over time

d)

  • explores bi-directional relationships

124.

The degree of consistency in scores, means, or rank order across time is referred to as:

a)

  • linearity

b)

stability

c)

  • rankability

d)

  • continuity

125.

The simplex, residualised longitudinal regression, and cross-lagged models can be used to test:

a)

change across time

b)

  • change across situations

c)

  • change across location

d)

  • differences between groups

126.

Another name for a simplex model is a:

a)

autoregressive model

b)

residualised longitudinal regression model

c)

  • cross-lagged model

d)

  • temporal model

127.

A ....... model can be used to test bi-direction relationships:

a)

  • simplex model

b)

  • residualised longitudinal regression model

c)

cross-lagged model

d)

  • autoregressive model

128.

The assumption of equal time intervals of measurement is referred to as

a)

  • linearity

b)

  • homoscedasticity

c)

synchronicity

d)

  • multicollinearity

129.

The approach that accounts for the change in the DV over time by entering the DV score at time 1 is:

a)

  • simplex model

b)

residualised longitudinal regression model

c)

  • cross-lagged model

d)

  • autoregressive model

130.

The approach that can be used to estimate change but not predict change is the:

a)

simplex model

b)

  • residualised longitudinal regression model

c)

  • cross-lagged model

d)

  • autoregressive model

131.

According to Funder a good indicator of the stability of research results is:

a)

  • content validity

b)

  • siginificance 

c)

  • face validity

d)

replication

132.

Problems that have been found with previous research findings include:

a)

  • publication bias

b)

  • publication fluctuation

c)

  • publication limitation

d)

  • online publications

133.

Questionnable Research Practices are also referred to as:

a)

  • q-hacking

b)

p-hacking

c)

  • p-cheating

d)

  • q-cheating

134.

Diederik Stapel became known for:

a)

  • developing the regression equation

b)

fabricating data 

c)

  • writing a paper with the title "Why most published research findings are false"

d)

  • establishing the Open Science Framework

135.

According to Brian Nosek, in order to change a research culture in part we need to make it

a)

  • difficult

b)

normative

c)

  • optional

d)

  • punishing

136.

Advocates for Open Science argue that we need:

a)

  • greater use of qualitative approaches

b)

  • Closed data

c)

preregistered studies

d)

  • closed materials

137.

Reproducing results when re-analysing the original data is referred to as:

a)

reproductibility

b)

  • reliability

c)

  • replication

d)

  • repeatability

138.

The Reproducibility Project found 36% of replications:

a)

  • were unsuccessful

b)

  • had no hypotheses

c)

  • had limited generalisibility

d)

were successful

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