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Multivariate Data Analysis – Questions/Answers
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Multivariate Data Analysis – Questions/Answers
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Multivariate Data Analysis – Questions/AnswersBootstrapping ✔️Ans - 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 ✔️Ans - See summated scales.
Dependence technique ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - Estimate of the degree to which the phenomenon being
studied (e.g., correlation or difference in means) exists in the population.
Independent variable ✔️Ans - Presumed cause of any change in the
dependent variable.
Indicator ✔️Ans - Single variable used in conjunction with one or more
other variables to form a composite measure.
Interdependence technique ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - Analysis of multiple variables in a single
relationship or set of relationships.
Multivariate measurement ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - Omitting a key variable from the analysis, thus
affecting the estimated effects of included variables.
Summated scales ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - 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) ✔️Ans - Statistical technique used
to determine, on the basis of one dependent measure, whether samples are
from populations with equal means.
, Validity ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - Imputation method for missing data that
computes values based on all-available valid observations, also known as the
pairwise approach.
Boxplot ✔️Ans - 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 ✔️Ans - 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 ✔️Ans - See reference category.
Complete case approach ✔️Ans - 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 ✔️Ans - 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).
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