This summary contains all the lectures of the course Management Research Methods 2. It includes all the practice quizes per subject of the course and other assignments with notes from of the official lecture slides.
Q1: We usually reject the null hypothesis, if the p-value is below .05
Ø True
Q2: Consider the following playful analogy to look at the meaning of the p value. You want to
test whether your pen was hidden (H0: it was hidden – HA: It was not hidden). If your test for
this has a p-value of .8 (80%), it is likely that your pen was hidden.
Ø True
Q3: Consider the following playful analogy to look at the meaning of the p value. You want to
test whether your pen was hidden (H0: it was hidden). If your test for this has a p-value of 1%
(.01), what do you conclude?
Ø Your conclusion is that most likely your pen was NOT hidden. In other words, you reject
the null hypothesis.
Slide 7 – 12
Q4: What is a conceptual model
Ø A visual representations of relations between variables
Q5: Categorical variables are used when
Ø We categorize our observations (respondents) into different groups, based on certain
criteria, for example education level
Q6: Quantitative variables are used when
Ø We measure constructs via scales on which, in theory, you could score any value, for
example height or time
Q7: Which is correct?
Ø A positive effect of a PV on an OV, means that when the PV increases (or decreases),
the OV does the same (PV; predictive variable & independent) (OV; Outcome variable
& dependent)
Q8: Which is correct?
Ø A negative effect of a PV on an OV, means that when the PV increases (or decreases),
the OV does the opposite
,Slide 13 – 24
Q9: The idea behind an ANOVA is to statistically investigate
Ø Whether different groups score the same on a quantitative outcome
Q10: If scores on a quantitative outcome vary more WITHIN groups than BETWEEN groups,
for example exam scores for three groups of students who each received a different teaching
method. It then is unlikely that it matters which group you are in, regarding your score on the
outcome
Ø True
Q11: To perform an ANOVA the PV and OV need to be measured in the following ways:
Ø The OV is quantitative (continuous) and PV is categorical, with more than two
categories (levels)
Q12: The sum of squares (SS) calculations are a
Ø Quantification of variability of scores of a quantitative outcome
Q13: The total sum of squares represents
Ø The total variability in scores on an outcome variable, on an outcome variable
Q14: The sum of squares of the model PLUS the sum of squares of the residual, together add
up to
Ø The total sum of squares
Q15: The R squared is a measure that quantifies Met opmerkingen [HS2]: how well the regression model
Ø The proportion of our total variability in the OV that can be explained by the model explains observed data.
Q16: The F statistic is a ratio of
Ø The EXPLAINED variance to the UNEXPLAINED variance. It tells us how much more
explained variance we have compared to unexplained variance
Q17: The null hypotheses of the F test is:
Ø All the mean scores of groups on the outcome variable are the same
Slide 26 – 34
Q18: The Levene's test for statistical significance test whether
Ø The variance in the groups are equal
Q19: Obtaining a p-value smaller than 5% (.05) for the F test in an ANOVA tells us that
Ø We can confidently reject the null hypothesis, meaning that AT LEAST one of the group
mean differs from the others
Q20: Obtaining an F value of 11 tells us that
Ø We have 11 times more EXPLAINED variance than UNEXPLAINED variance in our
dataset
,Q21: Obtaining an R squared of 0.16 tells us that
Ø We can explain 16% of the differences in scores on our OV, by our model
Q22: When conducting a follow up test to the F test, and examining graphical information
through a means plot, we are able to obtain
Ø The different average scores of the groups on the outcome variable and see which
groups score higher/lower than which other groups
Q23: When conducting multiple comparisons, we
Ø Test whether the mean scores of individual groups statistically differ from one another
Q24: The column "mean difference" in the multiple comparisons output gives us information
on
Ø The difference in mean scores of the two groups that are compared, in terms of the
size of the difference and which group scores higher/lower than which other group
Q25: The "95% confidence interval" of a comparison of two means, in the multiple
comparison table, tells us that
Ø We can be 95% confident that we will find a mean difference that falls within this
interval (lower to upper limit) in the population
Week 2 – FACTORIAL ANOVA Met opmerkingen [HS3]: https://www.scribbr.nl/statistiek/
anova/
Slide 1 – 8
Q1: When we speak of an 'effect' or 'impact' of a categorical PV on a quantitative (continuous)
OV, we then look for
Ø Differences in averages scores on the outcome variable between categories of the
predictor variable
Q2: In a multiple comparisons post hoc test, the 95% confidence interval can be interpreted
as a 'bandwidth of certainty' of the single value estimate for the mean difference
Ø True
Q3: When we do a post hoc test for a one-way ANOVA with a PV that has three categories,
how many unique comparisons can we make?
Ø 3
Slide 9 – 15
Q4: Moderation is statistically and mathematically identical to interaction Met opmerkingen [HS4]: Interaction = moderation
Ø True (mathematically they are identical, however conceptually we
may distinguish between the two) • The effect of one PV on
the OV is moderated by another PV. • The effect of one PV
Q5: The idea behind moderation is that the effect of one PV depends on the value of another on the OV depends on the level of another PV. • PV’s
PV interact in their effect on the OV.
Ø True
, Q6: Reflecting on the knowledge you gained, it would NOT make sense to frame moderation
as a so called "conditional effect" Met opmerkingen [HS5]: Another term for interaction
Ø False effect.
Q7: When our model contains interaction, an INTERACTION effect occurs when the direction
AND/OR the size of the effect of one predictor on an outcome changes, depending on the
value of the other predictor
Ø True
Slide 16 – 25
Q8: The 'Factorial' in Factorial ANOVA refers to something we call 'factors', based on your
current knowledge, what would you say this stands for
Ø It is a synonym for predictor variables or independent variables
Q9: To perform a Factorial ANOVA the PV and OV need to be measured in the following ways
Ø The OV is quantitative (continuous) and PV is categorical, with more than two
categories (levels)
Q10: The idea behind the R squared in Factorial ANOVA differs from that of a one way ANOVA
Ø False
Q11: The sum of squares of the model in a Factorial ANOVA can be subdivided into separate
components which represent
Ø The sum of squares of the separate PVs and the interaction
Q12: In a Factorial ANOVA with 2 PVs and interaction, there are a total of four meaningful F
ratios that we can calculate
Ø True
Q13: The null hypotheses of the F test for the main effect of a single PV in a Factorial ANOVA Met opmerkingen [HS6]:
is Met opmerkingen [HS7]: NO
Ø All the mean scores of groups on the outcome variable are the same
Q14: The null hypothesis of the F test for interaction in a Factorial ANOVA is
Ø There is no interaction
Q15: The total sum of squares represents
Ø The variability in scores on an outcome variable, that we can attribute to our model
and the residual
Q16: The sum of squares (SS) calculations are a
Ø Quantification of variability of scores of a quantitative outcome
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