Research in Organizations: Foundations and Methods in Inquiry

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Richard A. Swanson and Elwood F. Holton, leading scholars in the field, bring together contributions from more than twenty distinguished researchers from multiple disciplines to provide a comprehensive introductory textbook on organizational research.

Designed for use by professors and students in graduate-level programs in business, management, organizational leadership, and human resource development, Research in Organizations teaches how to apply a range of methodolgies to the study of organizations. This comprehensive guide covers the theoretical foundations of various research methods, shows how to apply those methods in organizational settings, and examines the ethical conduct of research. It provides a holistic perspective, embracing quantitative, qualitative, and mixed-methodology approaches and illuminating them through numerous illustrative examples.

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1 The Challenge of Research in Organizations

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The title of this book, Research in Organizations, was purposeful. It is not simply about research on organizations. The context of the organization is fundamentally interesting to most people. Without any obvious initiation, organizational questions arise about leaders, purposes, strategies, processes, effectiveness, trends, workers, customers, and more.

Organizations are human-made entities. There are for-profit and nonprofit organizations, global and small locally held organizations, organizations having multiple purposes, and organizations producing a mind-boggling range of goods or services. As human-made entities, organizations engage all kinds of human beings. No wonder organizations and the functioning of human beings in relation to organizations are of such great interest to so many fields of applied endeavor.

Applied disciplines, by their very nature, require that theory and practice come together (Dubin, 1978; Lynham, 2002; Van de Ven, 2002). When they do not come together, there is angst. This angst of not knowing is a signal to both practitioners and scholars that there is work to be done. Clearly, scholars from disciplines such as human resources, business, organizational behavior, education. sociology, and economics see organizations as meaningful contexts for their inquiry.

 

2 The Process of Framing Research in Organizations

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This chapter focuses on the task of identifying important research problems and connecting them to appropriate research questions, paradigms, and methods.

This is viewed as the process of framing research in organizations (see Figure

2.1). To accomplish this, the chapter aims to move from valuing the idea that research and the generation of new knowledge is important (chapter 1) to learning about specific research approaches and methodologies (remainder of the book).

Although this transition sounds easy enough, it is indeed a thorny patch. Three hurdles are standing in the way:

Identifying important problems from the milieu of existing knowledge

Understanding the philosophy of research

Choosing the most appropriate research question and method

The process of framing research in organizations begins with an initial problem area and ends up with specific research-planning decisions. The three hurdles in this process serve as organizers for the remainder of the chapter.

IDENTIFYING IMPORTANT PROBLEMS

 

3 The Basics of Quantitative Research

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Quantitative methods and the scientific method are the foundation of modern science. This approach to research usually starts with a specific theory, either proposed or previously developed, which leads to specific hypotheses that are then measured quantitatively and rigorously analyzed and evaluated according to established research procedures. This approach has a rich tradition and has contributed a substantial portion of the knowledge in human resource development (HRD).

This chapter attempts to demystify the quantitative research process and tools that HRD researchers use. It is not a statistics chapter, though we will discuss statistical tools. The purpose of this chapter is to give you a basic overview of quantitative research so you can do two things: (1) read research reports more easily and (2) understand choices made by researchers. It is not complete in describing every statistical tool or in explaining all the nuances of the various methods.

The chapters that follow in this section will explain each of the concepts in more detail. Rather, this chapter should provide a frame of reference to feel comfortable in the world of quantitative research.

 

4 Sampling Strategies and Power Analysis

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Sampling in research is a tightrope act that requires a fine balance between information and its costs. Information from a complete enumeration of a population seems desirable. For example, managers might require feedback to improve the quality of a trust-building workshop from all 10,000 of the company’s workshop participants. Yet, each piece of information collected, organized, analyzed, and reported exacts costs. The available budget might support obtaining feedback only from far fewer than all workshop participants. Sampling—selection of some elements from a population containing all elements—helps obtain information within budget. Using a sampling strategy, perhaps managers can afford to obtain feedback about workshop improvement from a sample of 300 of the population of 10,000 participants.

Sampling can contain costs for obtaining information, but often at the expense of the quality of the information obtained. Information about the entire population inferred from the sample contains error because a sample does not contain all members of a population. Consider estimating the feedback of all

 

5 Effects Sizes versus Statistical Significance

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Statistical significance tests can be traced back to applications more than three centuries ago (Huberty, 1999). Work in the early 1900s, including Gossett’s development of the t-test, Pearson’s formulation of the product-moment correlation, and the later elaboration of ANOVA by Sir Ronald Fisher and others, all facilitated the use of this logic. However, the uptake of statistical significance actually occurred primarily beginning in the 1950s (Hubbard & Ryan, 2000).

Criticisms of statistical significance testing arose almost as soon as the applications themselves (cf. Boring, 1919). However, in recent years the frequency of published criticisms has grown exponentially, and these indictments have been published in fields as diverse as economics, education, psychology, and wildlife science (cf. Altman, 2004; Anderson, Burnham, & Thompson, 2000).

This chapter has three purposes. First, some of the criticisms leveled against statistical testing are briefly summarized. Second, effect sizes as a supplement or an alternative to statistical tests are explained. Third, uses of confidence intervals, and especially confidence intervals for effect sizes, are presented.

 

6 Experimental and Quasi-experimental Designs

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As chapter 3 states, “Quantitative methods and the scientific method are the foundation of modern science.” Such methods typically employ a theoretical framework to derive hypotheses that are then tested and accepted or rejected using appropriate statistical techniques. The purpose of these studies typically is to draw some causal inference. Underpinning all such studies, however, are the research designs that are used—be they preexperimental, experimental, or quasiexperimental. This chapter will introduce some of the most commonly used designs and discuss the advantages and challenges of each design. Where appropriate, examples of these designs taken from the HRD research literature will be reviewed.

ISSUES OF CONCERN

Before launching into a detailed discussion of the various experimental and quasi-experimental designs that can be used, we need to address six general issues: internal validity, external validity, frame of reference, longitudinality, frequency, and nested factors. The following sections provide an overview of each of these issues.

 

7 Survey Research in Organizations

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To paraphrase Charles Dickens, it could be said that these are the best of times for survey research within organizations yet also the worst of times. Perhaps we are not experiencing the worst of times, but certainly numerous challenges now face the researcher using surveys despite the fact that the survey has achieved a well-established reputation for being the preferred method for data collection in organizations. Recent studies on survey research are providing new insights, requiring revisions on much of the conventional wisdom that has guided this research method for much of the past few decades (Krosnick, 1999).

This chapter will briefly describe the history and emergence of the survey as one, if not the, dominant method for doing research in organizations. A five-step process for conducting survey research will be summarized, with key principles and best practices highlighted. Major challenges facing survey research will be reviewed, including a summary of literature related to the rapidly evolving and increasing popular survey mode of the Internet.

 

8 Multivariate Research Methods

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Research questions in organizational research rarely involve only two variables.

For example, we would not (hopefully) try to predict or explain learning in training, efficacy beliefs, team performance, or climate for safety in an organization from a single independent variable. Although it would often be more convenient

(but far less interesting) if the study of human behavior and performance in organizations were this simple, we are nearly always faced with trying to explain, predict, or understand phenomenon that are influenced by a plethora of potentially important variables. Good theory is an indispensable tool for guiding and interpreting research. But, in conjunction with solid foundation in theory, competent researchers must also understand and be able to use appropriate analytic strategies that can handle data on at least two but probably more variables. Multivariate analysis methods are key tools for organizational researchers because of their ability to incorporate multiple variables and to help us in our quest to understand complex behavioral and organizational phenomenon.

 

9 Structural Equation Modeling: An Introduction to Basic Techniques and Advanced Issues

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Structural equation modeling (SEM), a statistical modeling technique offering a comprehensive approach to research questions, has become increasingly popular in the behavioral sciences. The ease of a simple bivariate experiment is often not a feasible option when researchers investigate human behavior in its natural setting. Consequently, over the years, researchers have developed advanced statistical techniques to handle multiple independent and dependent variables, some of which are measured and others of which are unobserved. Researchers in areas of organizational behavior, management, business, and applied psychology are often interested in multivariate relationships among some or all of the variables in a specified model, and SEM provides a viable statistical tool for exploring all of these relationships. The models investigated typically depict processes presumed to underlie values obtained with sample data, and these processes are assumed to result in measures of association (e.g., correlation) among the variables in the models (Williams, Edwards, & Vandenberg, 2003). SEM tests models of predicted relationships among observed and unobserved variables and offers numerous advantages over traditional approaches to model testing.

 

10 Scale Development Principles and Practices

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The focus of research in organizational behavior is typically on the relationships between constructs. Often, however, much less attention is paid to the relationship between the construct and the manner in which the construct is measured.

A construct is a conceptual term used to describe a phenomenon of theoretical interest that cannot be observed directly (Edwards, 2003). Examples include employee satisfaction, organizational commitment, and trust. A measure is a quantifiable assessment of the degree to which a respondent believes the construct exists, is felt, or is expressed. Questionnaires continue to be the most commonly used method for the collection of data in organizations using a variety of different measures (Hinkin, 1995). Data are collected today in several ways, including Web-based surveys, telephone surveys (IVR), and paper-and-pencil questionnaires.

A number of potential pitfalls are associated with collecting data such as negative attitudes about surveys on the part of respondents that may bias their responses (Rogelberg, Fisher, Maynard, Hakel, & Horvath, 2001) and poor response rates that do not adequately represent the population of interest

 

11 Factor Analysis Methods

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Factor analysis has just celebrated its centennial birthday in 2004. Its seminal idea was established by Spearman (1904) in discovering whether there is a common factor underlying a variety of branches of intellectual activity. Factor analysis nowadays is preferred as the common term representing several related statistical procedures that explain a set of observed variables in terms of a small number of hypothetical variables, called factors. It is a powerful statistical technique widely used for organizational research.

Two factor analysis techniques are commonly used: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is normally used to discover a set of a small number of latent constructs (i.e., factors) for a given larger number of observed variables, whereas CFA is more appropriate for confirming a predetermined factor structure based on theory or prior research. Factor analysis is a particularly useful research tool in developing and/or validating measurement instruments and in assessing theories on which instruments are established. Researchers can also use this tool in data analysis to discover new constructs in organizational study and thus to facilitate theory development.

 

12 Meta-Analysis Methods

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Meta-analysis is a relatively new, but increasingly popular, quantitative research method for synthesizing findings across studies. Niemi (1986) defines metaanalysis as “the application of statistical procedures to collections of empirical

findings from individual studies for the purpose of integrating, synthesizing, and making sense of them” (p. 5). It is a special approach to reviewing the research literature on a topic; it reviews and synthesizes empirical studies in the literature.

Meta-analysis originated in the medical field where the demand to answer complex and multifaceted questions with sometimes quite disparate findings is high

(Rosenthal & DiMatteo, 2001). The first meta-analysis can be traced to more than

100 years ago when Karl Pearson (1904) collected correlation coefficients to determine the extent to which inoculation against smallpox was related to survival.

Merriam and Simpson (2000) maintain that literature review is a crucial step in the research process, and its purpose is to summarize and integrate previous work and thus to offer suggestions for future studies. While most literature reviews tend to be descriptive and narrative, a carefully designed meta-analysis should be inferential and conclusive. It goes beyond the conventional literature review with the aid of sophisticated statistical methods. Consequently, metaanalysis is more than a narrative review of the literature. For example, hundreds of studies have examined the factors that influence the transfer of learning

 

13 Content, Lived Experience, and Qualitative Research

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Organizations are made up of human beings, who bring with them attitudes, prior knowledge, values, beliefs, motivations, hopes, worries, prejudices, spiritualities, politics, standpoints, social locations, and other characteristics that mark their lives and ultimately affect their performance, both as individuals and within groups in the workplace. Thus, showing the impact of efforts to improve organizations is always a task left unfinished, because it cannot account for many things unseen.

Organizations and the people who work in them possess the same rough or uneven edges that are found mathematically on fractals. Given the history and development of Western colleges and universities (both European and U.S.), the legitimacy of those disciplines focused on research in organizations is frequently found in a field’s approximation of scientific pursuits—“scientific” in the sense of proceeding with inquiries via use of hypothetico-deductive models, building theories for the purposes of hypotheses testing, establishing short causal chains for their explanatory power, and creating models of certain specified segments of the social world.

 

14 Analyzing Qualitative Data

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Good research, regardless of the mode or methodology, presents rationally compelling conclusions that are supported by evidence. In qualitative research, this can sometimes feel like a daunting and elusive challenge. Anyone who has attempted to analyze qualitative data has surely experienced that all too familiar feeling of being overwhelmed with the sheer volume of data to be explored or drowning in the data once immersed in it.

This chapter focuses on demystifying and simplifying the qualitative data analysis process to help researchers enhance their ability to work with qualitative data. It rests on the assumption that one of the keys to generating excellent qualitative research is to conduct a rigorous analysis of the data. It is meant to be a highly practical chapter focused on “the basics” that have been culled from leading scholars specializing in this area as well as my own experience. The first two sections will introduce some prerequisite concepts that are foundational to qualitative data analysis. Next, I outline four general stages of the data analysis process and briefly discuss specific strategies to enhance the trustworthiness of your analysis process.

 

15 Grounded Theory Research Methods

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The purpose of this chapter is to discuss the function and methodology of grounded theory research. For many, the term is synonymous with the use of qualitative research methods. The bond began in 1967 with a landmark publication by two clinical sociologists, Barney Glaser and Anselm Strauss, entitled The

Discovery of Grounded Theory. Their text offered a new approach for qualitative researchers to make theoretical contributions to their domains of inquiry. Moreover, it appealed to naturalistic scholars who sought the empirical rigor found in quantitative study without sacrificing flexibility and the rich description associated with qualitative work. While grounded theory research emerged from the discipline of sociology, it is now employed in virtually all domains where the study of human interaction and organizational behavior is conducted. In particular, this approach to research has been embraced by scholars in management, education, and the health sciences.

This chapter focuses on four questions: (1) How did grounded theory originate? (2) What is its underlying logic and assumptions? (3) What is unique about the methodology? (4) How does theory emerge from data? It is organized into two major sections. The first describes the features of grounded theory research: its history, assumptions, and key aspects of the methodology. The second section reviews issues and procedures for the creation of theory from grounded data.

 

16 Ethnographic Research Methods

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This chapter describes the methods used and challenges encountered when conducting ethnographic research in organizations. After describing ethnographic methods, we present two case studies that use these methods in contemporary business organizations. We conclude with a general discussion of the challenges of organizational ethnography: framing appropriate research questions, gaining access, maintaining the research role, confronting ethical challenges, and accounting for issues of scale when using firsthand observation.

ETHNOGRAPHIC METHODS

Ethnographic methods originated in the encounter of European imperial powers with indigenous populations. Colonial administrators found that explorers’ reports were often too superficial, and missionaries’ narratives too colored by their own emotional biases, to present a useful accounting of the customs and modes of livelihood of the indigenous peoples that had become the administrators’ responsibilities. From the two founders of the discipline, Franz Boas (at

 

17 Historical Research Methods

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There are increasing calls for a historical perspective in organization studies. The hope is that a “historic turn” might help make the study of organizations less deterministic and more ethical, humanistic, and managerially relevant (Clark &

Rowlinson, in press). In this chapter, I quickly overview the historical research process and use the example of my own research on the extensive collection of historical documents held by Cadbury, the British chocolate company, to explore issues to be considered when analyzing company documents from a historical perspective.

My intention is to address the question of why historical analysis of company documents is rarely pursued as a research strategy by organizational researchers.

The discussion is centered on the theme of exploring the differences between organization studies and business history, starting with a series of misconceptions concerning archival research on the part of organizational researchers.

Then I contrast the problem of periodization in business history, with the focus on everyday life in qualitative organizational ethnography and how this affects writing strategies in history and organization studies.

 

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