Download Lecture 3b

PPA 501 – ANALYTICAL
METHODS IN ADMINISTRATION
Lecture 3b – Fundamentals of Quantitative
Research
INTRODUCTION
A key objective of most public administration and
nonprofit organization research is to improve the
quality of decisions made by managers and
administrators.
 To make effective managerial decisions,
administrators must know how to use
quantitative research methods and how to
interpret quantitative data. The proper use of
numbers can make communicating easier, faster,
and often more effective than the use of words
alone.
 These numerical data are called statistics.
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FUNDAMENTALS OF MEASUREMENT
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Nominal data.
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Ordinal data.
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Categories.
Ranked.
Equidistant interval – The differences between ranks must be
measured in equal or measurable intervals.
Ratio data.
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Categories.
Ranked – the things can be ranked on some scale.
Interval data.
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Categories – different numbers must mean different things.
Categories.
Ranked.
Equidistant interval.
Absolute zero – The zero point must mean the absence of the
phenomena under investigation.
For most social science data, interval and ratio data are the
same. Generally, both are referred to as “scale” data.
STATISTICAL TERMS AND CONCEPTS
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Descriptive statistics: Measurements or numbers used
to summarize or describe data sets.
Inferential statistics: Statistical techniques used to
make estimates or inferences about the
characteristics of interest for a population using the
data from a sample data set.
Sample: A portion of a population. The sample is
chosen as representative of the entire population.
Population: The set of all elements for which
measurements are possible. A population can consist
of products, workers, customers, firms, prices, or
other items about which the decision maker or
manager is interested. Another word used to identify
a population is a universe.
STATISTICAL TERMS AND CONCEPTS
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Statistic: A number used as a summary measure for a
sample. For example, "The mean age for the 20 students in
the sample is 20.3 years."
Parameter: A numerical value used as a summary measure
for a population or universe. For example, in the statement
"The mean age for all entering college or university
freshmen is 19.1 years"; the age of all entering freshmen is
a parameter
Variable: A characteristic or quantity that can have
different values. Examples include savings account
amounts, stock prices, package designs, weight, monthly
sales, gender, and salaries. The values of variables may be
said to be either continuous or discrete.
Continuous Variables: Quantities that are measured, such
as weight or percentage of increase in the price of a stock,
are said to be continuous. Values for these variables can be
measured on a continuous scale, such as weights, and are
not restricted to specific, discrete categories or values.
STATISTICAL TERMS AND CONCEPTS
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Discrete variables: Variables with values that can vary only in
specific steps or categories (they are sometimes called
categorical). Assuming that we assign in advance the value of
1 for female and 2 for male, the variable gender is an example
of a discrete variable.
Univariate statistics: Statistics describing a single variable.
They include such measures as the valid number of responses
(frequencies); the mean, median, and mode; and standard
deviation.
Bivariate statistics: Measurements with which two variables
are described or compared at the same time. A crosstabulation
table is an example of bivariate statistics in use. Counts,
percentages, correlations, difference tests, and many other
statistical tests can be carried out with bivariate statistics.
Multivariate statistics: Statistics, such as multiple regression
analysis, used when more than one independent variable
influences one dependent variable. For example, sales of a
product are probably influenced by aesthetics, price,
availability (distribution), and advertising.
SOME CATEGORIES OF STATISTICS
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Descriptive statistics.
Numerically describe events, concepts, people, work,
or many other things.
 Summarize a set of data.
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Inferential statistics.
To make generalizations about a larger group—called
a population—from which the sample was drawn.
 To make estimates or draw conclusions about the
characteristics of a population.
 To make predictions about some future event or state
of affairs.
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SOME CATEGORIES OF STATISTICS
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Parameters versus statistics.
A parameter is a summary measure for a population
or universe.
 A statistic is a summary measure for a sample.
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Parametric versus nonparametric statistics.
Parametric statistics require that measurements
come from a population (rather than a sample) where
the distribution of variances is normal.
 Nonparametric statistics must be used when the data
are nominal or ordinal. No assumptions are made
about the distribution or the population.
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DESCRIPTIVE AND INFERENTIAL
STATISTICS
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Descriptive statistics.
Measures of central tendency (mean, median, mode).
Measures of variability (standard deviation, range,
interquartile range).
 Measures of relative position in the set (percentiles,
standard scores).
 Measures of correlation between two or more variables
(association, correlation coefficients).
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Inferential statistics.
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Independent samples t-tests.
Dependent samples t-tests.
Correlation and regression analysis.
One-way analysis of variance.
Two- or n-way analysis of variance.
Analysis of covariance.
Simultaneous equation modeling.