Part 1

copyright 2013 All rights reserveⓒd by LIU YING

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Research Methods for

Organizational Studies- 2ed, 2004

LIU YING


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1. Introduction

• Research Activities• A Point of View• Objectives and Organization• Summary• For Review• Terms to Know


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Consider these courses of action:

• An elementary school principal establishes a set of difficult teacher goals to improve students' academic performance.• A medical director has staff members make suggestions anonymously to en-courage participation.• A company president joins an alliance with other firms in the industry to im-prove returns from research and development expenditures.• Parents take their children to a concert to stimulate an interest in music.• A union leader calls for a strike vote to increase members' solidarity.• A basketball coach has team members take dancing lessons to improve agility.• A director of marketing recommends that a product be renamed, repackaged, and increased in price to attract more affluent customers.• A captain in the Salvation Army posts names of the bell ringers who obtain the greatest contributions each day to encourage bell ringer solicitations.• A human resource manager proposes a flexible benefit plan to reduce em-ployee turnover.


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• Each of the expected rela-tionships is causal. In causal rela-tionships one factor influ-ences another.


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• Empirical research can help obtain evidence on the veracity of expected causal relationships of the type de-scribed here. Empirical research addresses expected re-lationships through the systematic study of relation-ships between scores obtained from cases on measures.

• Cases are the entities investigated in research. • Measures are instruments used to obtain scores on the

cases studied.• scores (or data) represent information obtained from

cases on the measures used. Typically, scores are recorded in numerical form. Researchers use these scores to identify whether relationships exist as ex-pected.


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Research Activities

• The scenario described indicates that empirical re-search involves three activities: measurement, de-sign, and analysis. Measurement involves activities associated with measuring the factors that form the expected relationship.

• Research design establishes procedures to obtain cases for study and to determine how scores will be obtained from those cases.

• Empirical research also involves analyses of scores. Analyses are performed to describe scores on sin-gle measures and, especially, to identify relation-ships that may exist between scores across differ-ent measures.


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Research Activities

The lines linking the three research activities signal two things. • First, they signal that

these research activities are related in practice.

• Second, they signal that knowledge of any one research activity is helpful in learning about the other activi-ties.


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A Point of View• Research is sometimes described as a major tool of "the scientific

method," and that method is described in terms so abstract as to be nearly incomprehensible. Research methods may then be seen as a myste-rious set of practices that only a chosen few can accomplish, probably in cloistered laboratories.

• This book is written with a less deferential view of research methods. Re-search methods are easily accessible. These methods do not differ qualita-tively from our everyday practices of observing events and making sense of them.

• This book takes the view that research methods have two advantages for obtaining knowledge and that these are only advantages when research is appropriately conducted and reported.

• First, research methods properly conducted address questions systemati-cally.

• Second, research properly performed is a public process; it is transpar-ent.

• These are modest claims. Single research studies do not answer questions definitively.


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Objectives and Organization

• The book is written to help you acquire skills to conduct research with this view of research methods and outcomes in mind. The systematic nature of the research enterprise is emphasized. This applies to all three major research activities: measurement, design, and analysis.

The book is organized into eight parts consistent with its viewpoint and objec-tives. • Part I includes this and the next chapter. Chapter 2 presents a model of the entire research en-

terprise. This model introduces research objectives and shows how measurement, design, and analysis contribute to knowledge generation.

• Part II contains two chapters on measurement. These chapters describe measurement objec-tives and introduce criteria used to evaluate measures against these objectives. These chapters also describe measurement procedures commonly used in organizational studies.

• Part III addresses research design. Chapter 5 identifies challenges for research design and iden-tifies major decisions that researchers make when designing empirical research studies. The chapter also shows how these decisions affect conclusions that can appropriately be drawn from research studies. It concludes by introducing major types of designs that researchers use. Chap-ters 6 and 7 elaborate on these major design types.

• Chapters in part IV focus on data analysis. Chapter 8 provides an overview of data analysis and introductory material on important characteristics of scores for analysis purposes. Chapter 9 de-scribes methods for summarizing information about scores obtained from a single measure. These include statistics of central tendency, variability, and shape. Chapters 10 and 11 describe simple and multiple correlation and regression, respectively. These statistics provide useful ways to summarize relationships between scores from two or more measures.


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Objectives and Organiza-tion

• Part V has two chapters on the use of statistics and probability theory for drawing inferences that transcend the relations observed on scores. These statistical inferences are made to ad-dress causal relationships and to address whether a statistic observed on the group of cases studied likely applies in the broader population from which the sample group was drawn. Chapter 12 introduces the statistical inference process. Chapter 13 describes two methods for performing generalizations: hypothesis testing and confidence intervals.

• Part VI has a chapter on other types of inferences researchers seek to make from their research. It discusses the important role of repeating research studies to obtain information on the likely generalizability of research findings. It also describes two methods that researchers use to make these sorts of generalizations: narrative reviews and meta-analysis.

• Part VII contains a chapter on research report writing. Research reports have a special obligation to satisfy the second advantage of research mentioned earlier—namely, to provide a public record of the research for evaluation. Chapter 15 identifies the elements of research that should be included in a report to meet this obligation.

• Part VIII contains six chapters that extend topics covered earlier in the book. The first three of these address incomplete data sets, a challenge facing nearly every empirical study; relia-bility, a challenge for nearly all measurement efforts; and mutlicollinearity, an analysis is-sue that typically confronts researchers in even moderately complex studies. Two chapters fol-low that draw on earlier chapters to show how researchers carry out research studies to ad-dress causal questions and the challenges they confront when doing so. Finally, the last chap-ter draws on all earlier chapters to suggest what makes for conducting a persuasive research study. This chapter also serves as a guide for evaluating whether research conducted by oth-ers is persuasive.


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• Summary• For Review• Terms to Know


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2. A Model of Empirical Research

• Research Variables– Conceptual and Operational Variables– Dependent and Independent Variables

• The Model– Conceptual Relationships– Operational Relationships

• Empirical Relationships• Causal Relationships at an Empirical Level

– Conceptual to Operational Relationships

• Generalizing from the Model– Statistical Generalization– External Generalization

• Summary• For Review

– Terms to Know– Things to Know– Issues to Discuss


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Research Variables• Variables are characteristics of objects or events that can take on two or

more values. Variables are central to research. Most research is concerned with relationships between variables.

• Conceptual and Operational Variables– At this level of abstraction variables are called conceptual variables or con-

structs. Constructs are mental definitions of objects or events that can vary. – Empirical research activities are carried out at an operational level of abstraction.

Empirical research obtains scores from cases on measures. These measures repre-sent operational variables.

• Dependent and Independent Variables– Dependent variables are outcomes or consequences;– Independent variables are those thought to influence or at least predict depen-

dent variables. – Dependent variables typically are influenced by more than one independent vari-

able.– Variables can be dependent in one context and independent in another.Researchers are usually interested in causation. In such research, the independent variable represents a cause; the dependent variable represents the consequence. How-ever, independent and dependent variables are not necessarily causally linked. Inde-pendent variables may simply predict dependent variables without causal linkages.


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The Model

1. Independent and dependent variables are identified by X and Y, respectively.2. The symbol prime, ', is used to designate that a variable is specified at the conceptual level.3. Arrows represent the direction of influence or cause.


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The Model

• Conceptual Relationships• Operational Relationships– Empirical Relationships– Causal Relationships at an Empirical

Level

• Conceptual to Operational Relation-ships


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Conceptual Relationships

• A causal conceptual relationship describes a situation in which an independent construct is thought to influence a dependent con-struct.

• Researchers usually have an expectation about this relationship before conducting a study. In research, such expectations are called hypotheses, tentative beliefs about re-lationships between variables. Research is done to obtain information about whether the hypothesized relationship is valid.


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Operational Relationships

• Empirical RelationshipsAn Empirical Relationship, represented by line (d), refers to the correspondence between scores on measures of X and Y. Line (d) is solid to signal that this relationship can actually be ob-served, typically by using some statistical proce-dure (see part IV).

• Causal Relationships at an Empirical Level– Internal validity is present when variation in

scores on a measure of an independent vari-able is responsible for variation in scores on a measure of a dependent variable.


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Causal Relationships at an Empiri-cal Level


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Causal Relationships at an Empiri-cal Level

• Line (c), as (a), is broken, because internal validity cannot be established with certainty. Internal validation procedures (see part III) are used to infer internal validity indirectly. – The first criterion states that a relationship must be observed be-

tween scores on measures of X and Y. Although not sufficient, an empirical relationship is necessary for causation.

– The second criterion follows from a linear time perspective. It is based on an assumption that things occurring later in time are not responsible for those occurring earlier. A causal (indepen-dent) variable occurs before a consequence (dependent) vari-able.

– The third criterion has two parts. First, it requires that there is a reasonable conceptual explanation for why X causes Y. Re-searchers often use theory to help them in this process. A the-ory provides a tentative explanation for why a causal relation-ship(s) obtains (see Research Highlight 2.1).


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Conceptual to Operational Rela-tionships

• Conceptual validity requires that activities con-ducted at the operational level be linked to the concep-tual level. This link depends on relationships between measures and their respective constructs; these are represented by lines (b1) and (b2) in Exhibit 2.2.

• The construct X' is measured by the set of operations X; the construct Y' is measured by the set of opera-tions Y. Construct validity is present when there is a high correspondence between the scores obtained on a measure and the mental definition of the con-struct it is designed to represent. Lines (b1) and (b2) are also broken to show that construct validity is also tentative.


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Construct validation

• Construct valida-tion (see part II) involves proce-dures researchers use to develop measures and to make inferences about a measure's construct validity.

STEP 1• Define the construct

and develop concep-tual meaning for it

STEP 2• Develop/choose a

measure consistent with the definition

STEP 3

• Perform logical analyses and empir-ical tests to deter-mine if observations obtained on the measure conform to the conceptual def-inition


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Generalizing from the Model

• Statistical Generalization• External Generalization


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Statistical Generalization

Researchers have two methods to obtain validity ev-idence about research generalization. One, statisti-cal validation (see part V), uses probability theory to generalize a relationship observed on a sample of cases to the relationship that applies to the broader population from which the sample was drawn. Sta-tistical generalization validity is obtained when the empirical relationship observed on a sample of cases validly estimates the relationship in the popu-lation of cases from which the sample was drawn. (Statistical validation relies on probability theory for both internal and statistical generalization validity.)


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Statistical Generalization


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External Generalization

• External validation (see part VI) refers to proce-dures researchers use to investigate all other types of research generalization. External validity is present when generalizations of findings obtained in a re-search study, other than statistical generalization, are made appropriately. Exhibit 2.6 provides exam-ples of external generalization. Substantial progress has been made in methods to address external gen-eralization during the last three decades. This exter-nal validation technology usually goes by the name meta-analysis. Meta-analysis is a research proce-dure designed to provide quantitative estimates of the generalizability of relationships across studies.


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• Summary• For Review– Terms to Know– Things to Know– Issues to Discuss

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