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Quantitative Research Methods Quiz
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Questions in this quiz (42)
1Match the term to the definition:
[blank_start]Dependent variable[blank_end] - Aspect of the investigation which we are trying to measure.
[blank_start]Independent variable[blank_end] - An element within the investigation that influences the dependent variable, which is manipulated by the investigator.
[blank_start]Control Variable[blank_end] - An element within the investigation that influences the dependent variable, which is removed or minimised by the investigator.
[blank_start]Extraneous variable[blank_end] - An element within the investigation that influences the dependent variable, which interferes with another relationship without being manipulated.- ADependent Variable
- BIndependent Variable
- CControl Variable
- DExtraneous variable
2Outliers are tested by looking at [blank_start]z-scores[blank_end]. In SPSS, the [blank_start]z-scores[blank_end] have to be outside the range of [blank_start]-3[blank_end] and [blank_start]3[blank_end], in order for it to be considered as an outlier.
Skewness and kurtosis are tested by looking at the distribution of the data. In SPSS, this can be done through looking at a [blank_start]histogram[blank_end]. The value has to be between [blank_start]-1[blank_end] and [blank_start]1[blank_end], in order for the data to be considered to have [blank_start]normal distribution[blank_end].- A-3
- B3
- Cz-scores
- Dhistogram
- E-1
- F1
- Gnormal distribution
- Hz-scores
3Match the term to the definition:
[blank_start]Interval data[blank_end] - Level of measurement where the values between each element is equal.
[blank_start]Ratio data[blank_end] - Level of measurement where the values between each element is equal, with the scale containing an absolute 0.
[blank_start]Ordinal data[blank_end] - Level of measurement where the ranking of the values is important to the investigation.
[blank_start]Nominal data[blank_end] - Level of measurement where the data is placed into categories or groups.- AInterval data
- BRatio data
- COrdinal
- DNominal
- ERatio data
- FInterval data
- GOrdinal data
- HNominal data
- IOrdinal data
- JInterval data
- KRatio data
- LNominal data
- MNominal data
- NInterval data
- ORatio data
- POrdinal data
4Which of these assumptions have to be met in order to do a between-groups parametric t-test?
- AHomogeneity of variance
- BNormal distribution
- CInterval/ratio data
- DData independently sampled
- ENominal data
- FOrdinal data
- GSphericity
- HHomoscedasticity
5Which of these assumptions does not have to be met in order to do a within-groups t-test?
- AHomogeneity of variance
- BNormal distribution
- CInterval/ratio data
- DData independently sampled
6Welch's t test is used if within-groups t-test lacks homogeneity of variance.
- ATrue
- BFalse
7Fill in the blanks- Statistical justification for Welch's t-test:
[blank_start]t[blank_end]( [blank_start]value of t[blank_end] ) = [blank_start]degree of freedom value[blank_end] , p = [blank_start]p value[blank_end], [blank_start]one/two[blank_end] -tailed test- Adegree of freedom value
- Bt
- Cvalue of t
- Dvalue of p
- Eone/two
- Fwt
- Gg
- Ha
- Ivalue of wt
- Jvalue of a
- Kvalue of g
8Fill in the blanks- Statistical justification for Mann-Whitney U:
[blank_start]U[blank_end] = [blank_start]value of U[blank_end] , p = [blank_start]value of p[blank_end], [blank_start]one/two[blank_end] tailed test- Avalue of U
- BU
- Cvalue of p
- Done/two
- ER
- FQ
- Gvalue of R
- Hvalue of Q
9Fill in the blanks- Statistical justification for Wilcoxon:
[blank_start]Z[blank_end] = [blank_start]value of Z[blank_end], p = [blank_start]value of p[blank_end] , [blank_start]one/two[blank_end] tailed test- AZ
- BWC
- CWil
- DF
- Evalue of Z
- Fvalue of WC
- Gvalue of Wil
- Hvalue of F
- Ivalue of p
- Jone/two
10Fill in the blanks- Statistical justification for between-groups t-test:
[blank_start]t[blank_end]( [blank_start]degree of freedom value[blank_end] ) = [blank_start]value of t[blank_end] , p = [blank_start]p value[blank_end], [blank_start]one/two[blank_end] -tailed test- At
- Bdegree of freedom value
- Cvalue of t
- Dp value
- Eone/two
- Ftwo/three
- Gw
- Hg
- Ivalue of g
- Jvalue of w
11Correlation does not mean causation
- ATrue
- BFalse
12We cannot make predictions with correlations and linear regressions.
- ATrue
- BFalse
13Tick the methods which has an IV and a DV.
- ALinear regression
- BExperiments
- CCorrelation
14Cohen (1988) says with simple linear regression:
R² = 0.02 = [blank_start]Small effect[blank_end]
R² = 0.13 = [blank_start]Medium effect[blank_end]
R² = 0.26 = [blank_start]Large effect[blank_end]
Cohen (1988) also says with t-tests:
d = [blank_start]0.2[blank_end] = small effect size
d = [blank_start]0.5[blank_end] = medium effect size
d = [blank_start]0.8[blank_end] = large effect size- ASmall effect
- BMedium effect
- CLarge effect
- D0.2
- E0.5
- F0.8
- GLow effect
- HMid effect
- IHigh effect
- J0.1
- K0.6
- L0.9
15Which of these would you need to calculate power?
- ANumber of participants
- BThe d value
- CMean
- DStandard deviation
16Match the parametric test to the non-parametric alternative:
Pearson's correlation = [blank_start]Spearman's correlation[blank_end]
[blank_start]No equivalent test[blank_end] = Chi squared test
[blank_start]Between groups t-test[blank_end] = Mann-Whitney U test
Within groups t-test = [blank_start]Wilcoxon[blank_end]
One-way Independent Measures Analysis of Variance (ANOVA) = [blank_start]Kruskal-Wallis test[blank_end]
One-way Repeated-Measures Analysis of Variance (ANOVA) = [blank_start]Friedman's test[blank_end]- AKruskal-Wallis test
- BFriedman's test
- CBetween groups t-test
- DNo equivalent test
- ESpearman's correlation
- FWilcoxon
- GLinear regression
17Which one of these is a reason why a researcher would not use a one way ANOVA?
- AThere are two levels of the IV
- BReduce the risk of false positives
- CModifying the alpha level reduces power
- DTest multiple levels at the same time
18Fill in the blanks-
Statistical justification for between-group ANOVA is:
[blank_start]F[blank_end]( [blank_start]value of df for IV[blank_end], [blank_start]value of df for error[blank_end]) = [blank_start]value of F for IV[blank_end], p = [blank_start]value of p[blank_end], [blank_start]2[blank_end]-tailed- AF
- Bvalue of df for IV
- Cvalue of df for error
- Dvalue of F for IV
- Evalue of p
- F2
- GA
- HV
- Ivalue of df for DV
- Jvalue of df for intercept
- Kvalue of df for corrected model
- Lvalue of F for corrected model
- Mvalue of F for DV
- Nvalue of F for error
- Ovalue of F for intercept
- P1
- Q1/2
19Which of these assumptions have to be met in order to do a between-groups between-groups ANOVA?
- ANormal distribution
- BHomogeneity of varience
- CSphericity
- DInterval/Ratio data
- EOrdinal data
- FNominal data
- GHomoscedasticity
20Which of these assumptions have to be met in order to do a within-groups ANOVA?
- ASphericity
- BNormal distribution
- CInterval/ratio data
- DNominal data
- EOrdinal data
- FHomogeneity of variance
21Fill in the blanks-
Statistical justification for within-group ANOVA is:
[blank_start]F[blank_end]( [blank_start]value of df for IV[blank_end], [blank_start]value of df for error[blank_end]) = [blank_start]value of F for IV[blank_end], p = [blank_start]value of p[blank_end], [blank_start]2[blank_end]-tailed- AF
- Bvalue of df for IV
- Cvalue of df for DV
- Dvalue of df for error
- Evalue of F for IV
- Fvalue of F for error
- Gvalue of F for DV
- Hvalue of p
- IT
- Jvalue of T for IV
- Kvalue of T for DV
- Lvalue of T for error
- M2
- N1
- O1/2
22When does a researcher use the Greenhouse-Geisser value within an ANOVA?
- AWhen Maunchly's test is significant
- BWhen Maunchly's test is not significant
- CWhen Bonferroni correction is significant
- DWhen Bonferroni correction is not significant
23Which of these is the main criticism of using an ANOVA?
- ACan only tell the researcher that there is a difference
- BHard to replicate the data
- CRequires low levels of power
- DTime consuming
24Which of these assumptions have to be met in order to do a Pearson's r?
- AHomoscedasticity
- BLinear relationship
- CNormal distribution
- DInterval/ratio
- ESphericity
- FOrdinal
25Fill in the blanks-
Statistical justification for Pearson's r:
[blank_start]r[blank_end]([blank_start]number of participants[blank_end]) = [blank_start]Pearson's correlation value-2 variables[blank_end], p = [blank_start]p value- 2 variables[blank_end]- APearson's correlation value-2 variables
- BPearson's correlation value- top value
- CPearson's correlation- bottom value
- Dnumber of participants
- Enumber of variables
- Fhighest value of data
- Gr
- Hy
- Ib
- Jp value- 2 variables
- Kp value- participant variable
26Fill in the blanks-
Statistical justification for linear regression:
[blank_start]F[blank_end]( [blank_start]value of df for regression[blank_end], [blank_start]value of df for residual[blank_end] ) = [blank_start]value of F for regression[blank_end], p = [blank_start]value of p[blank_end]- Avalue of df for regression
- Bvalue of df for residual
- Cvalue of F for regression
- Dvalue of p
- EF
- FLR
- GLin
- Hvalue of LR for regression
- Ivalue of Lin for regression
- Jvalue of Lin for residual
- Kvalue of LR for residual
- Lvalue of F for residual
27A researcher wants to use a between-groups t test. When testing for homogeneity of variance, how many times would the the variance of the two groups, with equal sample size, have to differ by?
- AMore than 4
- BMore than 2
- CMore than 3
- DLess than 8
- ELess than 7
28A researcher wants to use a between-groups t test. When testing for homogeneity of variance, how many times would the the variance of the two groups, with unequal sample size, have to differ by?
- AMore than 2
- BMore than 4
- CLess than 6
- DMore than 5
- ELess than 8
29Maunchly's test is an ANOVA test for sphericity.
- ATrue
- BFalse
30Levene's test is a between-groups t test for variance.
- ATrue
- BFalse
31Which of these are diagrams that does not represent monotonicity?
- A
- B
- C
- D
- E
- F
- A
32Fill in the blanks- Statistical justification for spearman's rho:
[blank_start]rho[blank_end]([blank_start]number of participants[blank_end]) = [blank_start]correlation coefficient value[blank_end], p = [blank_start]p value[blank_end], [blank_start]1/2[blank_end] tailed test.- Anumber of participants
- Brho
- Ccorrelation coefficient value
- Dp value
- E1/2
- F1
- G2
- Hp
- ISR
- Jnumber of variables
- Knumber of provided data
- LSR value
- Mrho value
33Which of these are the BPS' general ethical principals for research?
- ARespect for the autonomy, privacy, and dignity of individuals and communities
- BMaximising benefit and minimising harm
- CScientific integrity
- DSocial responsibility
34Which of these is not an example of sampling error?
- AMoral bias
- BSampling bias
- CChance error
35Which of these sampling methods are more likely to be considered representative?
- ARandom
- BQuota
- COpportunity
- DVolunteer
36Which of these sampling methods are more likely to be considered easy to obtain participants?
- AVolunteer
- BQuota
- COpportunity
- DRandom
37Which of these statistical analysis methods is used to analyse interval/ratio data?
- AMean
- BStandard Deviation
- CMedian
- DProportion
- EExpected frequency
- FMode
38Which of these statistical analysis methods is used to analyse ordinal data?
- AMean
- BStandard deviation
- CMedian
- DProportion
- EExpected frequency
- FMode
39Which of these statistical analysis methods is used to analyse nominal data?
- AProportion
- BExpected frequency
- CMean
- DStandard deviation
- EMedian
40Fill in the blanks-
Statistical justification for chi-squared goodness of fit:
[blank_start]χ[blank_end]² ( [blank_start]df value[blank_end] , N = [blank_start]number of participants[blank_end] ) = [blank_start]χ² significance value[blank_end], p = [blank_start]p value[blank_end]- Aχ
- BI
- CW
- Ddf value
- Enumber of participants
- Fnumber of trials
- Gerror value
- Hχ² significance value
- IW² significance value
- JI² significance value
- Kp value
- LP
41Fill in the blanks-
Statistical justification for chi-squared test of association:
[blank_start]χ[blank_end]² ( [blank_start]chi-squared df value[blank_end] , N = [blank_start]number of participants[blank_end] ) = [blank_start]chi-squared value[blank_end] , p = [blank_start]p value[blank_end]- Aχ
- BI
- CW
- Dchi-squared df value
- Echi-squared error value
- Fnumber of participants
- Gnumber of trials
- Hchi-squared value
- Ierror value
- JW value
- KI value
- Lp value
- MP
42The main difference between chi-squared test-of-association and chi-squared goodness-of-fit is that chi-squared goodness-of-fit only uses 1 variable.
- ATrue
- BFalse
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- Undergraduate Psychology with Counselling (Introduction to Research Methods) Quiz on Quantitative Research Methods Quiz, created by Taz P on 04/04/2020.
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- Taz P
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