Multivariate Data Analysis Questions And Answers G
Multivariate Data Analysis Questions And Answers G
Multivariate Data Analysis questions and Answers G
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Multivariate Data Analysis questions and Answers Grade A+ 2023
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Multivariate Data Analysis questions and Answers G
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Multivariate Data Analysis Questions And Answers G
Bootstrapping - -An approach to validating a multivariate model by drawing a large number of sub- samples and estimating models for each subsample. Estimates from all the subsamples are then com- bined, providing not only the "best" estimated coefficients (e.g., means of each estimated coefficient ...
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Multivariate Data Analysis questions and Answers
Grade A+ 2023
Bootstrapping - -An approach to validating a multivariate model by drawing a large number of
sub- samples and estimating models for each subsample. Estimates from all the subsamples are
then com- bined, providing not only the "best" estimated coefficients (e.g., means of each
estimated coefficient across all the subsample models), but their expected variability and thus
their likelihood of differing from zero; that is, are the estimated coefficients statistically different
from zero or not? This approach does not rely on statistical assumptions about the population to
assess statistical significance, but instead makes its assessment based solely on the sample data.
-Composite measure - -See summated scales.
-Dependence technique - -Classification of statistical techniques distinguished by having a
variable or set of variables identified as the dependent variable(s) and the remaining variables as
independent. The objective is prediction of the dependent variable(s) by the independent
variable(s). An example is regression analysis.
-Dependent variable - -Presumed effect of, or response to, a change in the independent
variable(s). Dummy variable Nonmetrically measured variable transformed into a metric variable
by assign- ing a 1 or a 0 to a subject, depending on whether it possesses a particular
characteristic.
-Effect size - -Estimate of the degree to which the phenomenon being studied (e.g., correlation or
difference in means) exists in the population.
-Independent variable - -Presumed cause of any change in the dependent variable.
-Indicator - -Single variable used in conjunction with one or more other variables to form a
composite measure.
-Interdependence technique - -Classification of statistical techniques in which the variables are
not divided into dependent and independent sets; rather, all variables are analyzed as a single set
(e.g., factor analysis).
-Measurement error - -Inaccuracies of measuring the "true" variable values due to the fallibility
of the measurement instrument (i.e., inappropriate response scales), data entry errors, or
respondent errors.
-Metric data - -Also called quantitative data, interval data, or ratio data, these measurements
iden- tify or describe subjects (or objects) not only on the possession of an attribute but also by
the amount or degree to which the subject may be characterized by the attribute. For example, a
person's age and weight are metric data.
,-Multicollinearity - -Extent to which a variable can be explained by the other variables in the
analy- sis. As multicollinearity increases, it complicates the interpretation of the variate because
it is more difficult to ascertain the effect of any single variable, owing to their interrelationships.
-Multivariate analysis - -Analysis of multiple variables in a single relationship or set of
relationships.
-Multivariate measurement - -Use of two or more variables as indicators of a single composite
measure. For example, a personality test may provide the answers to a series of individual ques-
tions (indicators), which are then combined to form a single score (summated scale) representing
the personality trait.
-Nonmetric data - -Also called qualitative data, these are attributes, characteristics, or categorical
properties that identify or describe a subject or object. They differ from metric data by indicating
the presence of an attribute, but not the amount. Examples are occupation (physician, attorney,
professor) or buyer status (buyer, nonbuyer). Also called nominal data or ordinal data.
-Power - -Probability of correctly rejecting the null hypothesis when it is false; that is, correctly
finding a hypothesized relationship when it exists. Determined as a function of (1) the statistical
significance level set by the researcher for a Type I error ( ), (2) the sample size used in the
analysis, and (3) the effect size being examined.
-Practical significance - -Means of assessing multivariate analysis results based on their
substantive findings rather than their statistical significance. Whereas statistical significance
determines whether the result is attributable to chance, practical significance assesses whether
the result is useful (i.e., substantial enough to warrant action) in achieving the research
objectives.
-Reliability - -Extent to which a variable or set of variables is consistent in what it is intended to
measure. If multiple measurements are taken, the reliable measures will all be consistent in their
a values. It differs from validity in that it relates not to what should be measured, but instead to
how it is measured.
-Specification error - -Omitting a key variable from the analysis, thus affecting the estimated
effects of included variables.
-Summated scales - -Method of combining several variables that measure the same concept into
a single variable in an attempt to increase the reliability of the measurement through multivariate
measurement. In most instances, the separate variables are summed and then their total or
average score is used in the analysis.
-Treatment - -Independent variable the researcher manipulates to see the effect (if any) on the
dependent variable(s), such as in an experiment (e.g., testing the appeal of color versus black-
and- white advertisements).
,-Type I error - -Probability of incorrectly rejecting the null hypothesis-in most cases, it means
saying a difference or correlation exists when it actually does not. Also termed alpha ( ). Typical
levels are 5 or 1 percent, termed the .05 or .01 level, respectively.
-Type II error - -Probability of incorrectly failing to reject the null hypothesis-in simple terms,
the chance of not finding a correlation or mean difference when it does exist. Also termed beta
(?), it is inversely related to Type I error. The value of 1 minus the Type II error (1 - ?) is defined
as power.
-Univariate analysis of variance (ANOVA) - -Statistical technique used to determine, on the
basis of one dependent measure, whether samples are from populations with equal means.
-Validity - -Extent to which a measure or set of measures correctly represents the concept of
study- the degree to which it is free from any systematic or nonrandom error. Validity is
concerned with how well the concept is defined by the measure(s), whereas reliability relates to
the consistency of the measure(s).
-Variate - -Linear combination of variables formed in the multivariate technique by deriving
empirical weights applied to a set of variables specified by the researcher.
-All-available approach - -Imputation method for missing data that computes values based on all-
available valid observations, also known as the pairwise approach.
-Boxplot - -Method of representing the distribution of a variable. A box represents the major
portion of the distribution, and the extensions-called whiskers-reach to the extreme points of the
dis- tribution. This method is useful in making comparisons of one or more variables across
groups.
-Censored data - -Observations that are incomplete in a systematic and known way. One example
occurs in the study of causes of death in a sample in which some individuals are still living.
Censored data are an example of ignorable missing data.
-Comparison group - -See reference category.
-Complete case approach - -Approach for handling missing data that computes values based on
data from complete cases, that is, cases with no missing data. Also known as the listwise
approach.
-Data transformations - -A variable may have an undesirable characteristic, such as
nonnormality, that detracts from its use in a multivariate technique. A transformation, such as
taking the logarithm or square root of the variable, creates a transformed variable that is more
suited to portraying the relationship. Transformations may be applied to either the dependent or
independent variables, or both. The need and specific type of transformation may be based on
theoretical reasons (e.g., trans- forming a known nonlinear relationship) or empirical reasons
(e.g., problems identified through graphical or statistical means).
, -Dummy variable - -Special metric variable used to represent a single category of a nonmetric
variable. To account for L levels of a nonmetric variable, L - 1 dummy variables are needed. For
example, gender is measured as male or female and could be represented by two dummy
variables (X1 and X2). When the respondent is male, X1 = 1 and X2 = 0. Likewise, when the
respondent is female, X1 = 0 and X2 = 1. However, when X1 = 1, we know that X2 must equal
0. Thus, we need only one variable, either X1 or X2, to represent the variable gender. If a
nonmetric variable has three levels, only two dummy variables are needed. We always have one
dummy variable less than the number of levels for the nonmetric variable. The omitted category
is termed the reference category.
-Effects coding - -Method for specifying the reference category for a set of dummy variables
where the reference category receives a value of minus one (-1) across the set of dummy
variables. With this type of coding, the dummy variable coefficients represent group deviations
from the mean of all groups, which is in contrast to indicator coding.
-Heteroscedasticity - -See homoscedasticity.
-Histogram - -Graphical display of the distribution of a single variable. By forming frequency
counts in categories, the shape of the variable's distribution can be shown. Used to make a visual
comparison to the normal distribution.
-Homoscedasticity - -When the variance of the error terms (e) appears constant over a range of
predictor variables, the data are said to be homoscedastic. The assumption of equal variance of
the population error E (where E is estimated from e) is critical to the proper application of many
multivariate techniques. When the error terms have increasing or modulating variance, the data
are said to be heteroscedastic. Analysis of residuals best illustrates this point.
-Ignorable missing data - -Missing data process that is explicitly identifiable and/or is under the
control of the researcher. Ignorable missing data do not require a remedy because the missing
data are explicitly handled in the technique used.
-Imputation - -Process of estimating the missing data of an observation based on valid values of
the other variables. The objective is to employ known relationships that can be identified in the
valid values of the sample to assist in representing or even estimating the replacements for
missing values.
-Indicator coding - -Method for specifying the reference category for a set of dummy variables
where the reference category receives a value of zero across the set of dummy variables. The
dummy variable coefficients represent the category differences from the reference category. Also
see effects coding.
-Kurtosis - -Measure of the peakedness or flatness of a distribution when compared with a
normal distribution. A positive value indicates a relatively peaked distribution, and a negative
value indi- cates a relatively flat distribution.
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