DISCOVERING STATISTICS EXAM PREP
QUESTIONS AND ANSWERS
COMPREHENSIVE STUDY SHEET FULL
SOLUTION
●● α-level
Answer: the probability of making a Type I error (usually this value is
0.05).
●● Adjusted mean
Answer: in the context of analysis of covariance this is the value of the
group mean adjusted for the effect of the covariate.
●● Adjusted predicted value
Answer: The predicted value of a case from a model estimated without
that case included in the data, calculated by re-estimating the model
without the case in question, then using this new model to predict the
value of the excluded case.
*A measure of the influence of a particular case of data*
●● How to interpret adjusted predicted value
,Answer: If a case does not exert a large influence over the model then its
predicted value should be similar regardless of whether the model was
estimated including or excluding that case.
●● Adjusted R²
Answer: This tells us how much variance in the outcome would be
accounted for if the model had been derived from the population from
which the sample was taken.
*measure of the loss of predictive power/shrinkage in regression*
●● AIC (Akaike's information criterion)
Answer: a goodness-of-fit measure that is corrected for model
complexity (takes account of how many parameters have been
estimated).
It is not intrinsically interpretable, but can be compared in different
models to see how changing the model affects the fit. A small value
represents a better fit to the data.
●● AICC (Hurvich and Tsai's criterion)
Answer: a goodness-of-fit measure that is similar to AIC but is designed
for small samples. It is not intrinsically interpretable, but can be
compared in different models to see how changing the model affects the
fit.
Small value=better fit
,●● Alpha factoring
Answer: a method of factor analysis.
●● Alternative hypothesis
Answer: the prediction that there will be an effect (i.e., that your
experimental manipulation will have some effect or that certain variables
will relate to each other).
●● Analysis of covariance (ANCOVA)
Answer: a statistical procedure that uses the F-statistic to test the overall
fit of a linear model, adjusting for the effect that one or more covariates
have on the outcome variable. In experimental research this linear model
tends to be defined in terms of group means and the resulting ANOVA is
therefore an overall test of whether group means differ after the variance
in the outcome variable explained by any covariates has been removed.
●● Analysis of variance (ANOVA)
Answer: a statistical procedure that uses the F-statistic to test the overall
fit of a linear model.
In experimental research this linear model tends to be defined in terms
of group means, and the result is therefore an overall test of whether
group means differ.
●● Anderson-Rubin method
, Answer: a way of calculating factor scores which produces scores that
are uncorrelated and standardized with a mean of 0 and a standard
deviation of 1.
●● AR(1)
Answer: this stands for first-order autoregressive structure. It is a
covariance structure used in multilevel linear models in which the
relationship between scores changes in a systematic way. It is assumed
that the correlation between scores gets smaller over time and that
variances are assumed to be homogeneous. This structure is often used
for repeated-measures data (especially when measurements are taken
over time such as in growth models).
●● Autocorrelation
Answer: when the residuals of two observations in a regression model
are correlated.
●● bi
Answer: unstandardized regression coefficient. Indicates the strength of
relationship between a given predictor, i, of many and an outcome in the
units of measurement of the predictor. It is the change in the outcome
associated with a unit change in the predictor.
●● βi
Answer: standardized regression coefficient. Indicates the strength of
relationship between a given predictor, i, of many and an outcome in a
QUESTIONS AND ANSWERS
COMPREHENSIVE STUDY SHEET FULL
SOLUTION
●● α-level
Answer: the probability of making a Type I error (usually this value is
0.05).
●● Adjusted mean
Answer: in the context of analysis of covariance this is the value of the
group mean adjusted for the effect of the covariate.
●● Adjusted predicted value
Answer: The predicted value of a case from a model estimated without
that case included in the data, calculated by re-estimating the model
without the case in question, then using this new model to predict the
value of the excluded case.
*A measure of the influence of a particular case of data*
●● How to interpret adjusted predicted value
,Answer: If a case does not exert a large influence over the model then its
predicted value should be similar regardless of whether the model was
estimated including or excluding that case.
●● Adjusted R²
Answer: This tells us how much variance in the outcome would be
accounted for if the model had been derived from the population from
which the sample was taken.
*measure of the loss of predictive power/shrinkage in regression*
●● AIC (Akaike's information criterion)
Answer: a goodness-of-fit measure that is corrected for model
complexity (takes account of how many parameters have been
estimated).
It is not intrinsically interpretable, but can be compared in different
models to see how changing the model affects the fit. A small value
represents a better fit to the data.
●● AICC (Hurvich and Tsai's criterion)
Answer: a goodness-of-fit measure that is similar to AIC but is designed
for small samples. It is not intrinsically interpretable, but can be
compared in different models to see how changing the model affects the
fit.
Small value=better fit
,●● Alpha factoring
Answer: a method of factor analysis.
●● Alternative hypothesis
Answer: the prediction that there will be an effect (i.e., that your
experimental manipulation will have some effect or that certain variables
will relate to each other).
●● Analysis of covariance (ANCOVA)
Answer: a statistical procedure that uses the F-statistic to test the overall
fit of a linear model, adjusting for the effect that one or more covariates
have on the outcome variable. In experimental research this linear model
tends to be defined in terms of group means and the resulting ANOVA is
therefore an overall test of whether group means differ after the variance
in the outcome variable explained by any covariates has been removed.
●● Analysis of variance (ANOVA)
Answer: a statistical procedure that uses the F-statistic to test the overall
fit of a linear model.
In experimental research this linear model tends to be defined in terms
of group means, and the result is therefore an overall test of whether
group means differ.
●● Anderson-Rubin method
, Answer: a way of calculating factor scores which produces scores that
are uncorrelated and standardized with a mean of 0 and a standard
deviation of 1.
●● AR(1)
Answer: this stands for first-order autoregressive structure. It is a
covariance structure used in multilevel linear models in which the
relationship between scores changes in a systematic way. It is assumed
that the correlation between scores gets smaller over time and that
variances are assumed to be homogeneous. This structure is often used
for repeated-measures data (especially when measurements are taken
over time such as in growth models).
●● Autocorrelation
Answer: when the residuals of two observations in a regression model
are correlated.
●● bi
Answer: unstandardized regression coefficient. Indicates the strength of
relationship between a given predictor, i, of many and an outcome in the
units of measurement of the predictor. It is the change in the outcome
associated with a unit change in the predictor.
●● βi
Answer: standardized regression coefficient. Indicates the strength of
relationship between a given predictor, i, of many and an outcome in a
