Applied statistics for public and nonprofit administration pdf

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Applied Statistics for Public and Nonprofit Administration

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As the first book ever published for public administration statistics courses, APPLIED STATISTICS FOR PUBLIC AND NONPROFIT ADMINISTRATION makes a difficult subject accessible to students and practitioners of public administration who have little background in statistics or research methods. Steeped in experience and practice, this landmark text remains the first and best in research methods and statistics for students and practitioners in public--and nonprofit--administration. All statistical techniques used by public administration professionals are covered, and all examples in the text relate to public administration and the nonprofit sector. The text avoids jargon and forumlae; instead, it uses a step-by-step approach that facilitates student learning.

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Cengage Learning

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Independence, Kentucky

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Non Profit, Public Administration

Recommended Citation

Meier, Kenneth J.; Brudney, Jeffrey L.; and Bohte, John, "Applied Statistics for Public and Nonprofit Administration" (2009). Urban Affairs Books. 4.
//engagedscholarship.csuohio.edu/urban_bks/4

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Summary:Suitable for students and practitioners of public administration who have little background in statistics or research methods, this book covers all statistical techniques.

Print Book, English, 2014

Publisher:Wadsworth, Boston, MA, 2014

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Applied Statistics for Public and Nonprofit Administration, 8th Edition Kenneth J. Meier, Jeffrey L. Brudney, and John Bohte Publisher: Suzanne Jeans Executive Editor: Carolyn Merrill Acquisitions Editor: Edwin Hill

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Library of Congress Control Number: 2010936366 ISBN 13: 978-1-111-34280-7 ISBN 10: 1-111-34280-6

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Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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Statistics and Public and Nonprofit Administration

CHAPT E R

The Advantages of a Statistical Approach

tatistics have many advantages for the study and practice of public and nonprofit administration and other disciplines. We can summarize these advantages simply by noting that statistics offer insight into issues and problems in a field that would otherwise go unnoticed and unheeded. Although each book on the subject appraises statistics somewhat differently, we can briefly relate the major advantages of this approach. First, statistics have great power to describe systematically a body of information or data. No other approach matches the precision and quantification that statistics bring to this task. Statistics can elucidate very precisely the main tendencies, as well as the spread of the data about them, in a subset or sample of a population or the population as a whole. Consider the local food pantry, for example. The manager would like to know, What is the average number of hours donated by volunteers to the organization in a typical week? On average, how much do the hours donated vary week by week? Or consider the regional office of the department of motor vehicles. The director needs to know, How many clients on average seek service in a typical day? By how much does this number vary day to day? This information is essential for decision makers. Answering such questions is the descriptive function of statistics. Second, statistics are very useful for subjecting our intuitive ideas about how a process or phenomenon operates to empirical test. Empirical means observable or based on data. This confrontation of informed conjecture and speculation with actual data and observation is called hypothesis testing. A hypothesis is an informed guess or conjecture about an issue or problem of interest—for example, that the increasing involvement of the nonprofit sector in the delivery of publicly financed services will lead to greater government calls for accountability, or that developing skills in statistics in a master’s of public administration (MPA) program or a concentration in nonprofit management will enhance a student’s prospects in the job market upon graduation. Statistics are helpful not only for determining the extent to which the data available support or refute our hypotheses but also for generating the kind of hypotheses that can be tested. Hypotheses should be clear, observable, and falsifiable. This perspective expresses the hypothesis-testing function of statistics. 3

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Statistics and Public and Nonprofit Administration

Third, statistics are the foremost method for drawing an accurate inference from a subset or sample of data to its parent, the full population. Rarely does the public or nonprofit administrator have the luxury of working with the complete population; instead, the data available are almost always a sample of observations. For example, the analyst may have a sample of all agency employees, or clients, or audits, or records—whatever the units of analysis might be—and may want to generalize to the entire population. Public and nonprofit administrators need to know what the sample suggests about the population. For example, the nonprofit administrator may want to estimate the likely number of financial donors in the county based on a random sample of residents. Statistics provide an excellent methodology for drawing this linkage. They allow the analyst to evaluate the risk of error when making an inference from sample to population. They also allow us to derive a confidence band or interval about an estimate that expresses the inherent uncertainty in generalizing from a sample of data to the full population. Because we do not have the data from the entire population, we can still make an error in inferring from the sample. Yet statistics are valuable, for they enable the analyst to estimate the probability or extent of this error. This function is the essence of statistical inference. To these classic uses of statistics we can add two others. First, in public and nonprofit administration, managers face situations and challenges of daunting complexity, such as homelessness, poverty, illiteracy, crime, drug and alcohol dependency, and child and spousal abuse. We entrust to public and nonprofit managers some of the most difficult problems in society. A major advantage of statistics is that they can help the manager keep track of an almost innumerable collection of measured characteristics or attributes, called variables, at the same time. Statistics allow the manager to manipulate the variables and evaluate the strength of their influence on desired outcomes, such as agency performance and citizen satisfaction in the public sector, or success in obtaining grant funding and retaining volunteers in the nonprofit sector. The ability to examine a large number of variables simultaneously—and to sort out and make sense of the complicated relationships among them—is a great advantage of statistical methods for dealing with highly complex situations. Second, an appreciation of statistics can help the public and the nonprofit manager become a much more discerning consumer of quantitative information. Like it or not, managers in all sectors are bombarded with “facts” or assertions based on statistical analysis. There is no escape from them. They appear regularly in myriad sources, including reports, evaluations, memoranda, briefings, hearings, press releases, newspaper accounts, electronic communications, books, academic journals, and many other outlets. Public and nonprofit managers need the skills to evaluate the conflicting claims and representations often made and to avoid being misled. Statistics offer major benefits in this area. Perhaps this reason is the best one of all for the study and application of statistics in public and nonprofit administration. As reflected in the MPA curriculum, statistics are certainly not all there is to know about public or nonprofit organizations and management. One must

Licensed to: iChapters User Statistics and Options for Managers

acquire or hone additional skills as well as develop a general understanding of broader political, legal, economic, and social forces. But statistics, too, have a rightful place in the program of study for the MPA, as well as in academic concentrations and degree programs in nonprofit organizations and management.

Statistics and Options for Managers For these reasons, statistics and quantitative analysis have become a major element of public and nonprofit management. Agencies that only a few years ago made decisions based on seat-of-the-pants guesses and convenient assumptions now routinely use computer printouts, contingency tables, regression analyses, decision trees, and other statistical techniques to help understand complex situations and make decisions. Human resources managers receive personnel projections to schedule recruitment efforts. Transportation planners rely on complex computer simulations to design urban transportation systems. Budget officers and accountants scour economic projections and analyses. Program evaluators are charged with making quantitative assessments of a program’s effectiveness. Nonprofit managers weigh the benefits against the costs of hiring a fund-raising firm. They compare volunteer recruitment and turnover rates by age and education level. They distribute surveys to donors and potential donors to learn about them and inspire further giving. Quantitative analyses have become so prevalent that no midlevel manager in the public or nonprofit sector can—or should—hope to avoid them. The increasing sophistication of quantitative techniques affords public and nonprofit managers few options. At one extreme, a manager untutored in these methods can act as if they did not exist and refuse to read reports containing statistics. Unfortunately, this option is exercised all too often and at considerable cost: The public or nonprofit manager loses valuable information presented in quantitative form. This option is not acceptable. At the other extreme, public and nonprofit managers may choose to accept, uncritically, the findings of the data analyst rather than to reveal to others an ignorance of statistics. This option leads to an error as serious as the first. Although quantitative analysts will almost certainly possess a stronger background in statistics than does the manager (that’s their job), the analysts lack the experience and management skills—and the responsibility—to make the decisions. Those decisions rest with public and nonprofit managers, based on the best statistical (and other) advice available. This book is intended for students who consider public or nonprofit management and analysis their present or future occupation. The third option open to the manager—and the one favored by the authors—is to receive training in quantitative techniques. The training advocated and offered in this book, however, is not a standard course in statistics, which often remains a required (and dreaded) element of most MPA programs and increasingly of nonprofit degree programs. Instead, we seek to develop appreciation for and intuitive understanding of basic elements of statistics and quantitative analysis for managers in the public and nonprofit sectors.

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Reading this book and working the problems at the end of the chapters will not transform public and nonprofit managers from quantitative novices into master statisticians. Such a transformation is neither desired nor necessary. By and large, public and nonprofit managers do not set up research designs and select and calculate appropriate statistical measures. Far more often, they receive information of this kind and are expected to make reasoned and responsible decisions based on it. For this task, a course in mathematical statistics is not required. However, it is essential that managers become intelligent and critical consumers of quantitative information. Toward that end, this book stresses the application, interpretation, and evaluation of basic statistics. This book is intended primarily for students who have no or only a very limited knowledge of, or background in, mathematics, statistics, or other quantitative methods. Material is presented in an applied, nonrigorous, easily readable format centered on practical problems of public and nonprofit management. The text is designed to engage readers in the discussion of these problems and to encourage students to seek and understand numerical or statistical answers to them. Often we present a step-by-step approach to the various techniques to build confidence and mastery. Statistical theory is discussed only rarely, and the computational formulas that pepper most statistics books are reserved for those instances in which they enlighten rather than mystify. We have elaborated on some of the advantages of our approach, and we hope that they will become evident as you read and use the book and work the examples and problems presented (highly recommended!). However, we would be remiss were we to overlook the book’s shortcomings. The most obvious is that this is not a comprehensive text in formal statistics. As noted before, the book is not rigorous, and we have ignored and probably violated many elements of standard statistical theory. Whereas this approach may arouse the disapproval of some professional colleagues, we believe that it has its place—as an introduction to statistics for managers in the public and nonprofit sectors. Too often, students are alienated by more formal courses that emphasize precision over application, and a first course in statistics becomes an eminently disliked and forgettable last one. We have endeavored to develop a text that will engage and hold the interest of public and nonprofit sector managers and at the same time present fundamental applied statistics—and, perhaps, whet the appetite for further training in this area. For those who seek a more mathematical and theoretical approach to managerial statistics, several good books are available (see the Annotated Bibliography at the end of the text).

The Role of Calculation Whenever possible in this book, we have provided step-by-step instructions for performing statistical procedures and evaluating the results. We strongly recommend that you do these calculations and follow along. Statistics is not a spectator sport: You learn by doing.

Licensed to: iChapters User Academic Standards for Master’s Degree Programs

But, you may be wondering, with laptop and desktop computer programs featuring an entire repertoire of statistics seemingly available at the push of a button or the click of a computer mouse, why worry about calculating statistics? Why go to the trouble? Our answer is that, precisely because statistics have become so immediately accessible, it is all the more important to see how they are derived and computed. We know of no better way to understand the various statistics, their advantages and limitations, their assumptions and anomalies, than to experiment with a few observations or data points, make the appropriate calculations, and observe what values of the statistic are generated in return. Whatever the strengths or peculiarities of the statistic, they will soon become apparent to you. Given the profusion of user-friendly statistical package programs loaded onto laptop and desktop computers, however, many students and managers in the public and nonprofit sectors are becoming exposed to them through a different mechanism: Instead of learning about the statistics beforehand, they may plunge into using them because they are readily accessible on their microcomputers, but they may not readily understand them. We do not want to discourage healthy curiosity or interest in statistics; nurturing it is difficult enough. But, in effect, these students and managers practice a tempting statistical version of the popular television quiz show Jeopardy; for those who aren’t aware, in this quiz show contestants are given the answer but must state the question (instead of the reverse—fun, huh?). For instance, you can easily obtain “the regression” (answer) on the computer, but what is it, how should you interpret it, and why, are questions that require prior study for appropriate application and use. In this book we address the important questions before explaining the answers. With statistical package programs increasingly loaded onto computers, students untrained in quantitative techniques can easily generate the statistical “answers” on their computer monitor at the click of a computer mouse—but then can only guess at the question, use, or purpose behind those answers. In our judgment, these students have not learned statistics for public and nonprofit managers; they have acquired a potentially useful computer skill. There is a big difference. In this book, we emphasize building knowledge of the former.

Academic Standards for Master’s Degree Programs in Public Affairs and Curricular Guidelines for Nonprofit Academic Programs If we have still not persuaded you of the advantages—or at least the need—for learning and using applied statistics in public administration, nonprofit administration, and allied fields, we can offer you one more reason: the accreditation standards in the field. The National Association of Schools of Public Affairs and Administration (NASPAA) has formulated standards for accreditation of

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master’s degree programs in public affairs, policy, and administration. Many public administration programs also offer concentrations or certificates in nonprofit administration and include pertinent courses in the curriculum. In fact, across all disciplines public administration schools and departments most often provide courses and concentrations in nonprofit management education. NASPAA adopted revised Standards for Accreditation for Master’s Degree Programs in 2009.* Under NASPAA Standard 5, “Matching Operations with the Mission: Student Learning,” section 5.1,“Universal Required Competencies,” states that an MPA program, “As the basis for its curriculum … will adopt a set of required competencies related to its mission and public service values” in five domains. The domains encompass the ability to: • Lead and manage in public governance; • Participate in and contribute to the policy process; • Analyze, synthesize, think critically, solve problems, and make decisions; • Articulate and apply a public service perspective; • Communicate and interact productively with a diverse and changing workforce and citizenry. The chapters and material presented in this book are intended especially to raise the ability of students in public administration to “analyze, synthesize, think critically, solve problems, and make decisions.” Increasing and honing this ability, in turn, will contribute to the other four required NAPAA competencies of leading and managing in governance, participating and contributing in the policy process, articulating and applying a public service perspective, and communicating and interacting with the workforce and citizenry. This book will help to create and refine the ability of students in public and nonprofit administration to synthesize information, understand and perform crucial data analysis and interpret the results, and support problem solving and decision making that underlie sound and effective practice in the other domains specified by NASPAA in its accreditation standards. Although it does not yet have authority or responsibility to accredit academic programs in nonprofit studies, the Nonprofit Academic Centers Council (NACC) published revised Curricular Guidelines for Graduate Study in Nonprofit Leadership, the Nonprofit Sector and Philanthropy in 2007.** Section 16.0 treats “Assessment, Evaluation and Decision-Making Methods” and includes three guidelines for nonprofit academic programs to meet in this area: • Methods and modes to evaluate performance and effectiveness at both organizational and programmatic levels; • Decision-making models and methods and how to apply them in nonprofit organizational settings; and • The use and application of both quantitative and qualitative data for purposes of strengthening nonprofit organizations, the nonprofit sector, and society at large. * //www.naspaa.org/accreditation/standard2009/docs/NS2009FinalVote10.16.2009.pdf. ** //www.naccouncil.org/pdf/GradCG07.pdf Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Licensed to: iChapters User Academic Standards for Master’s Degree Programs

Just as is the case with the NASPAA Standards for Accreditation in public affairs and administration with regard to the standard to “analyze, synthesize, think critically, solve problems, and make decisions,” this book provides thorough, accessible coverage for students in nonprofit administration of NACC Curricular Guidelines in the area of “Assessment, Evaluation and DecisionMaking Methods.” This book can form the basis for courses (and/or coverage) that satisfy the NASPAA Accreditation Standards and the NACC Curricular Guidelines relating to quantitative techniques of analysis and requisite skills in program evaluation, information synthesis, decision making, and problem solving. The book elaborates statistical methods as a tool for assisting public and nonprofit managers in making decisions. By focusing on the assumptions underlying the various techniques, the careful interpretation of results, and the limitations as well as the strengths of the information conveyed, the text stresses the ethical and effective utilization of statistics and quantitative analysis. With respect to the competencies identified by NASPAA and NACC, Part I of the book addresses “Foundations of Quantitative Analysis.” The chapters in this section set out the rationale for a statistical approach in public and nonprofit administration and provide essential background in measurement and research design. The chapters are strong in the methodology of research and treat a wide range of issues, including problem diagnosis, the logic of inquiry, causal inference, and threats to the validity of a quantitative study. Part II, “Descriptive Statistics,” introduces basic statistical analysis. The chapters here are also useful for acquainting students with the presentation and interpretation of statistical charts, graphs, and tables to inform themselves as well as other decision makers. Part III, “Probability,” explores the many uses of this tool in public and nonprofit management. The chapters in this section assist students in defining and diagnosing decision situations and selecting and evaluating a course of action. The chapters in Part IV, “Inferential Statistics,” not only develop sophisticated analytic skills but also help in the definition of problems, formulation of alternatives, choice of decision, and evaluation of results. They help the manager to understand the promise—and the limitations—of a sample of data for reaching conclusions about the entire population. Part V, “Analysis of Nominal and Ordinal Data,” introduces another set of quantitative skills useful for the public and nonprofit administrator. This type of analysis is employed frequently in written memoranda and technical reports and in the evaluation of survey data. These data distinguish public administration and nonprofit administration (and other social science fields) from the natural, physical, and biological sciences, in which measurement is typically much more precise. Part VI presents “Regression Analysis.” Regression is one of the most flexible and often utilized statistical techniques in the social sciences. The chapters in this section greatly enhance the decision-making, analytic, and evaluative capabilities of public and nonprofit managers. The first five chapters in this section discuss the methods of regression analysis and the varied applications of regression-based Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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techniques in public and nonprofit management. The last chapter in this section explains how to read and interpret regression output generated by statistical software packages, which is often complicated and difficult to understand. The chapter, thus, provides a needed skill that is too often overlooked. The final part of the book discusses “Special Topics in Quantitative Management”: performance measurement and decision theory. A full treatment of linear programming can be found on the companion Website for the book. These materials expose students to techniques for measuring organizational performance, different models of logical analysis, bases for decisions, and evaluation of alternatives. In sum, this book provides essential coverage pertaining to the NASPAA Standards for Accreditation in public affairs in administration and the NACC Curricular Guidelines for Nonprofit Administration.

A Road Map for This Book This book is designed so that each of its parts is self-contained yet builds on the other parts. Part I lays the foundations for the use of statistics and quantitative analysis in public and nonprofit administration. Chapter 1 explains why statistics have become important to this enterprise and Chapter 2 elaborates measuring critical concepts in these fields. The chapter shows that measurement of key concepts such as organizational effectiveness, job satisfaction, volunteer competence for a task, and public trust in an agency can be difficult but is necessary. Chapter 2 also provides twin evaluative criteria for assessing measurement: reliability and validity. Chapter 3 explains how to model or depict a problem or issue of importance (for example, service delivery by a public or nonprofit agency) and to follow up with a systematic study based on data. The chapter examines different research plans, called research designs, that direct how and when data are to be collected, analyzed, and interpreted to answer questions about topics of interest, such as how to improve service delivery, recruit volunteers more effectively, integrate the work of paid and nonpaid (volunteer) human resources, or redesign public organizations. The topics considered in the chapter provide an essential foundation for understanding the uses, advantages, and limitations of the statistics presented in the other chapters of the book. Part II covers basic descriptive statistics. This part of the book is devoted to the analysis of one variable at a time, or univariate statistics. Chapter 4, “Frequency Distributions,” begins this discussion with a treatment of how to categorize and display a large volume of data, or a (frequency) distribution, in a graphical format, such as a table, chart, or graph, and how to work with percentages. The chapter following, “Measures of Central Tendency,” is concerned with finding and interpreting the average in a distribution of data: You may be familiar with the main measures of central tendency, the mean, median, and mode. The chapter shows how to calculate these statistics both for data that have been

Licensed to: iChapters User A Road Map for This Book

arranged in a table or chart and for data that have not, which we affectionately term “raw data.” Once you have calculated or read the average for a group or distribution of data, the next question to ask is how closely the data cluster or spread about this average—that is, whether the observed values or observations are relatively concentrated or dispersed about the measure of central tendency. Chapter 6, “Measures of Dispersion,” introduces the two major statistics for measuring dispersion in a sample of data: the variance and its close relative, the standard deviation. It also discusses the range and other statistics. The next part of the book addresses probability. Probability can be confusing for students to understand; do not become discouraged! To learn the basic rules and applications of probability, please see Chapter 7, which presents an introduction to the topic. In that chapter, you will learn the basic law of probability as well as what is meant by a priori probabilities, posterior probabilities, joint probabilities, and conditional probabilities. With this background, the remaining chapters on probability will be much easier to follow. Chapter 8 presents the most common probability distribution, the normal curve. The familiar bell-shaped curve has numerous uses and applications in public and nonprofit administration. When data follow a normal distribution, it is practical and easy (OK, maybe not easy, but surely within your reach) to determine the percentage of job applicants who fall above or below a criterion score on a test of job-related skills. Or you can find the score that distinguishes the top 5% of applicants for further consideration (for example, those applicants for whom a follow-up interview is warranted). Have you ever wanted to know the probability that an agency could hire 3 minorities for 10 positions when 50% of the job applicants were minorities? For problems similar to this one, Chapter 9 introduces the binomial probability distribution. The chapter also shows how the normal distribution can be applied to simplify complex binomial problems, provided certain conditions are met. The chapter following discusses other useful probability distributions for public and nonprofit managers. The hypergeometric probability distribution is used when the manager wants to make a generalization from a sample to a finite population. The Poisson and the exponential probability distributions are used whenever the manager needs to include time or distance in a probability statement—for example, 1.2 computer failures per day or 15 potholes per 100 meters. Part IV explores statistical inference and focuses on the issue of how the manager can generalize (infer) results from a small sample of data to the much larger population from which the sample was drawn. This technique is useful in its own right and also to support advanced statistical procedures presented later in the book, such as regression analysis. Because the public or nonprofit manager must work almost always with a sample rather than the full population of data—but seeks reliable information about the entire population—knowledge of statistical inference is essential. To learn how to estimate the value of the mean or average for a population from a sample of data, consult Chapter 11. This chapter also discusses procedures for constructing confidence bands or intervals around the mean estimate.

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Chapter 12 applies the techniques of statistical inference to testing hypotheses. Although it is not possible to avoid the risk of error in inferring from a sample of data to the population (we cannot be sure of results if we do not have population information), public or nonprofit managers may be willing to take an acceptable risk in drawing an inference. The chapter shows how, by using the techniques of classical hypothesis testing on a small sample of data, the manager can make a decision regarding the full population—for example, that the average number of times the population of agency clients seeks assistance is four times or more per year, or that the average is less—at the risk of error of, say, 5%. You will thus learn a technique that in the long run will allow you to make the correct decision 95% of the time but be in error the remaining 5% (remember, because we do not know the “answers” in the population, we cannot be right all the time). Chapter 13 shows how to estimate population proportions, rather than mean or average values, from a sample—for example, the proportion (or percentage) of motorists in a county who drive faster than 65 miles per hour on a stretch of highway. For those situations in which the manager needs to compare the performance or characteristics of two groups (for example, experimental and control groups, groups before and after the implementation of a program or intervention or treatment, and so forth), Chapter 14 explains how to test for differences between groups using the statistical technique called analysis of variance. Beginning with Part V, the remainder of the book deals with relationships between two or more variables. The study of relationships between two variables is called bivariate analysis. Bivariate statistical techniques can help to answer myriad research and practical questions: Is agency budget related to performance? Do police patrols reduce crime? Does greater inclusiveness in government hiring lead to a more responsive bureaucracy? Does government contracting with nonprofit organizations produce more efficient delivery of services? Do employees in nonprofit organizations display greater job motivation than those in other sectors of the economy? Do smaller nonprofit organizations adapt more quickly to their environments than larger ones? Is there a relationship between delegating decision-making authority to lower levels of the organization and innovativeness of employees? Part V explains how to construct tables and analyze data at the nominal and ordinal levels of measurement—that is, information measured in terms of categories (for example, gender) or rating scales (for example, attitude toward balancing the federal budget, or clients’ evaluations of the training provided by a volunteer center). Chapter 15 shows how to use percentages to analyze and interpret tables called contingency tables or cross-tabulations that pair data from two nominal or ordinal variables. Chapter 16 builds on this foundation to provide more sophisticated techniques for analyzing tables, including statistical inference (chi-square) and measures of association (gamma, lambda, and so forth). Chapter 17 discusses statistical control table analysis, a procedure for examining the relationship between two variables while taking into account or “controlling for” or “holding constant” a third variable. The analysis of three or more variables simultaneously presented in this chapter introduces multivariate analysis, a topic covered more extensively in later chapters of the text.

Licensed to: iChapters User A Road Map for This Book

Part VI is concerned with relationships between variables assessed on equal interval scales, or “interval” data, such as variables measured in years, dollars, or miles. Chapter 18 begins the discussion with an “Introduction to Regression Analysis,” a highly flexible and often used statistical technique that is helpful in a variety of managerial situations in the public and nonprofit sectors. The chapter shows how a line or linear relationship depicted in a graph or set of coordinate axes can summarize the relationship between two interval variables—for instance, the relationship between the number of intake workers at a government facility and the number of clients who receive service in a given day. Chapter 19 explains the assumptions and limitations of regression analysis. Estimating and predicting trends in the future based on past data is the subject of Chapter 20 on time series analysis. Consult this chapter to forecast such trends as future population, the number of people likely to volunteer to government agencies, service usage, sewage output, the number of organizations that will participate in the community walk-a-thon to raise cancer awareness, and other over-time information important to public and nonprofit managers. Chapter 21, “Multiple Regression,” extends this technique to the multivariate context: It shows how to use regression to analyze and understand relationships among three or more variables. For example, how well can the average age of housing and the percentage of renter-occupied buildings in a neighborhood explain or predict the incidence of fires across a city? To what extent do the number of volunteers working in nonprofit agencies and the number of community events sponsored by these organizations affect the amount of money collected in their annual fund-raising campaigns? Chapter 22, on interrupted time series analysis, explains how to estimate the impact of a program or policy over time. The manager can use this technique to evaluate whether a program, such as a senior citizens’ center or a municipal volunteer office, has had a short-term, long-term, or short-term temporary impact (or perhaps no impact) on the health and welfare of city residents. Chapter 23 focuses on the interpretation of regression output—that is, output generated by statistical software packages. The earlier chapters in this section (Chapters 18–22) present a variety of regression examples in equation form to illustrate how relationships between variables can be summarized using linear equations. Statistical software packages generally do not present regression results in equation form, however, which can make the leap from textbook to computer applications and printout confusing. Although it is perfectly acceptable to write up regression results in either equation or summary table form (the format used by most statistical software packages), managers need to have a clear understanding of the similarities and differences between these formats to interpret and use the information provided effectively. Regression analysis is almost always performed with computers. As a result, public and nonprofit managers need exposure to how regression is carried out and presented in statistical software packages before conducting such analyses on their own. Part VII presents two special topics in quantitative management that are sometimes useful to public and nonprofit managers. From years of teaching, as

Chapter 1

Statistics and Public and Nonprofit Administration

well as feedback from instructors who have been kind enough to adopt this book (thank you!) and their students (thank you, too!), we know that not all students will be exposed to this part of the book. But some students are—and we include treatment of these topics as an aid and courtesy to them. Many of you will need to use these techniques later in your career in public or nonprofit management. Statistical methods are often used as tools for measuring and improving organizational performance in both government and nonprofit settings. Chapter 24 on performance measurement techniques provides an overview of some of the key issues that managers in both sectors need to know when designing performance measurement systems and reporting performance results to external audiences. If you are interested in how to make decisions given various amounts of information, Chapter 25 on decision theory can be very helpful. The chapter presents useful ways to evaluate alternatives and select among them. Previous editions of the book included a chapter on linear programming. This technique is useful for decision situations that involve maximizing or minimizing some output under certain constraints. That chapter has been moved to the companion Website for the book. Following the chapters in the book, you will find other materials useful for the study of applied statistics for public and nonprofit administration. For those motivated to learn more about statistics (don’t laugh—by the time you have read a chapter or two, this student could be you!), we have included an Annotated Bibliography with a brief description of each entry. The bibliography contains a wide assortment of texts valuable for assistance and reference. For ease of use of the book, you will also find, at the back, a Glossary of key terms that have been boldfaced at their first appearance in the text except in the first chapter, where they are highlighted in italics to draw attention to later use. And, of course, to make the book self-contained, you will find all of the statistical tables (normal, t-test, etc.) essential for applied statistics for public and nonprofit administration both for relevant courses and for present and future careers. Finally, you will quickly make friends with the section containing answers to the odd-numbered computational questions from the problem sets at the end of each chapter. Whenever possible, we have attempted to include problems faced by public and nonprofit administrators in the real world. Many of our midcareer as well as more senior students suggested problems and examples for the book. Although all the data and problems are hypothetical, they represent the types of situations that often confront practicing public and nonprofit administrators. We hope that you find them useful and interesting. Now you have a road map for the book. Good luck on the journey!

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