Comparison Tests in the Research

Researchers in management often find they want to test hypotheses. For example, a researcher may want to test the hypothesis that the introduction of formalized strategic planning improves the financial performance of banks (Robinson and Pearce, 1983). Comparison tests are powerful tools for testing such a research hypothesis. Statistical tests generally fall into one

Statistical Tests in the Research

In this section we explore the general context in which researchers carry out statistical tests. We define the essential concepts involved and outline the gene­ral steps followed when using this type of test. 1. Inference and Statistics Inference has a very important place in management research. Conclusions or generalizations often have to be established

Parametric Tests in the Research

1. Tests on Means 1.1. Comparing the mean m of a sample to a reference value μ0 when the variance σ2 of the population is known Research question Does the mean m, calculated from a sample taken from a population with a known variance of σ2, differ significantly from a hypothetical mean μ0? Application

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Practicing Non-Parametric Tests in the Research

Non-parametric tests use statistics (that is, calculations) that have been estab­lished from observations, and do not depend on the distribution of the cor­responding population. The validity of non-parametric tests depends on very general conditions that are much less restrictive than those that apply to para­metric tests. Non-parametric tests present a number of advantages, in

Specifying the Phenomenon or System to be Modeled in the Research

Models can be defined as abstract representations of real phenomena. They represent the components of the phenomena studied as much as they do the interrelationships among these components. Identifying the phenomenon or system to be modeled is a three-step process. The first step is to determine its components. The interrelationships between these components must

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Specifying Variables and Concepts of the Research

Specifying a model’s different concepts and variables is, above all, dependent on the researcher’s chosen approach, which can be inductive or deductive, qualita­tive or quantitative. There are two levels of specification. One is conceptual, and enables researchers to determine the nature of concepts. The other is opera­tional, and enables researchers to move from concepts

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Identifying Relationships Among a Model’s Variables and Concepts in the Research

As with identifying the model’s concepts and variables, identifying relationships depends above all on the method the researcher adopts: qualitative or quantita­tive, inductive or deductive. Its aim is to determine whether there is a relation­ship between the model’s concepts (and variables), the nature of this relationship (causal or simple association) and the relationship’s sign

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Evaluating and Testing the Research Model

Evaluating and testing a model does not simply mean testing the hypotheses or relationships between the model’s concepts or variables one after the other, but also judging its internal global coherence. 1. Qualitative Methods In some studies, researchers want to test the existence of a causal relationship between two variables without having recourse to

Classification and Structuring Methods in Data Analysis

Data analysis manuals (Aldenderfer and Blashfield, 1984; Everitt, 1993; Hair et al., 1992; Kim and Mueller, 1978; Lebart et al., 1984) provide a detailed pre­sentation of the mathematical logic on which classification and structuring methods are based. We have chosen here to define these methods and their objectives, and consider the preliminary questions that

Application Classification and Structuring Methods in Data Analysis

This section puts the principal methods into practice. We recommend researchers seeking a more detailed discussion of the practical application of these structuring and classifying methods refer to a specialized data analysis manual, such as Hair et al. (1992). 1. Cluster Analysis After clearly defining the environment from which the objects to be classified

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General Presentation and Data Collection for Social Network Research

Network analysis essentially involves revealing the links that exist between units. These units can be individuals, actors, groups, organizations or projects. When researchers decide to use network analysis methods, and to look at links between units of analysis, they should be aware that they are implicitly enter­ing into the paradigm of structural analysis. Structural

Analysis Methods in Social Network Research

Once researchers have collected sociometric data (data that measures links between individuals), and so know which actors have links with which other actors, they then need to reconstitute the network, or networks, of actors before they can conduct a global network analysis (or analyses). The methods of analysis fall into two main categories. In

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Fundamentals of Longitudinal Analyses

1. Definition and Role of Time Longitudinal analyses form a group of analyses focusing on the study of pheno­mena over the course of time. Longitudinal analyses are often contrasted with cross-sectional studies, by arguing that the data collected for longitudinal analyses relate to at least two distinct points in time, while for cross-sectional studies

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Quantitative Longitudinal Analyses

A large number of quantitative longitudinal methods are not specific to longitu­dinal analysis, and therefore will not be developed in detail here. An example of this is regression. Nevertheless, often additional conditions have to be respected when applying these methods to longitudinal analyses – notably concerning error terms, which should be homoscedastic, and free

Qualitative Longitudinal Analysis

The mass of information collected when doing qualitative research can be impressive. But this mass of data cannot simply be analyzed directly – it must first be manipulated and put into usable form. 1. Preliminary Processing of Qualitative Longitudinal Data Very often the first question for researchers using qualitative longitudinal data is how to

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Analyzing Representations and Discourse

Research in management and organizational science often relies on the analysis of communications, either oral (conversations, individual or group interviews) or written (annual reports, strategic plans, letters to shareholders, etc.). Researchers may simply want to analyze the content or the structure of these communica­tions; or they may attempt to establish, through the text or

Methods of Discourse and Representation Analysis

Discourse and representation analysis methods generally require three major steps: data collection (Subsection 1), coding (Subsection 2), and analysis (Subsection 3). 1. Collecting Discourse or Representations There are two main types of methods for collecting representations or dis­course: structured (or a priori) and non-structured methods. 1.1. Structured or a priori methods The objective of

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Content Analysis

Content analyses were developed to study newspaper articles and political speeches in the USA in the 1920s. They are based on the theory that repeating certain elements of discourse (words, expression or similar meanings) revealed the interests or concerns of the persons involved. Their purpose is to analyze the manifest contents of a communication.

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Cognitive Mapping for Analyzing Representations and Discourse

As stated earlier, cognitive mapping is aimed at establishing and analyzing cognitive maps, that is, representations of a person or organization’s beliefs concerning a particular domain (Axelrod, 1976). A cognitive map is made up of two types of elements: Concepts, also known as constructs or variables. The links between these concepts. These links can

Communication Media for the Research

Communication of research work can be directed towards various readers. Research on organizations may be of interest to three particular kinds of readers: researchers, managers and other members of the organization, and, to a lesser extent, the general public. The same research project may then need to be adapted to different target audiences –

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