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Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The purpose of this study is to review the literature on strategic management in Africa with special emphasis on how strategy constructs have been measured and present a roadmap to help improve strategy research in Africa.

A content analysis of empirical research on strategic management published in journals using data from Africa from 2000 to 2013 is conducted to examine construct measurement practices.

The findings indicate that the average sample sizes in strategy research in Africa is not large as strategy research in general, and have low statistical power. While the studies rely heavily on single-indicator measures, there were also several studies using scale or multiple measures that report reliabilities.

Limitations of the research include small number of studies used, inability to examine journal effects’ of the findings due to few numbers of papers from many of the journals, and lack of examination of the influence of the context and topical areas of the articles on the use of the construct measurement techniques.

The study provides information about the use of construct measurement techniques and power analysis in strategy research in Africa. It further encourages the use of larger sample sizes, the examination of power, and more focus on variables which allow the assessment of reliabilities and validity.

Little is known about construct measurement practices of the empirical research in and about Africa in the discipline of strategic management. This chapter builds on extant research on construct measurement issues in strategic management research, but with the unique value-added contribution of focusing on the African environment where the discipline is beginning to take hold.

This chapter provides a presentation about Chapter 1 of The Balance of the National Economy, 1923–24, edited by Pavel Illich Popov. The Balance was issued in June 1926 by the Central Statistical Administration (CSU or TsSU) of the USSR, which Popov had headed from July 1918 to January 1926. In the first part of our chapter, we show how Popov’s work on the balance of the national economy was rooted in the specific scientific and political culture of zemstvo statisticians inherited from the Tsar. Statistical inquiry was considered an objective scientific process based on international standards. Furthermore, like zemstvo statisticians, CSU statisticians developed great autonomous political power. The balance of the national economy was built according to these principles, which met harsh criticism from revolutionaries and Bolsheviks. In the second part, we analyze the contents of Popov’s Chapter 1, especially the theoretical foundations of the balance and its connection with Soviet planning. In the third part, we discuss the balance’s significance in the years 1926–1929, years which ended the NEP and launched the first Five-Year Plan, so as to understand how CSU’s balance didn’t become a standard Soviet statistical instrument and was discarded as a “bourgeois” device.

Single subject research has long been employed to evaluate intervention effectiveness with students with learning or behavioral disabilities. Typically, the results of single subject research are presented on graphic displays and analyzed by a method of visual inspection, in which analysts simultaneously consider such data elements as level change, slope change, and variability in baseline and treatment data. However, over the years several concerns regarding visual inspection have emerged, including relatively low inter-rater reliabilities. This chapter reviews the arguments in favor of visual inspection as an analytic tool, and also summarizes the arguments favoring statistical analysis of single case data. The use of randomization tests is recommended, and an example is provided of its use in research with students with learning and behavioral disorders.

The recent “reverse”¹ discrimination decisions by the Supreme Court involving the admissions decision-making policies at the University of Michigan² illustrate the underlying need for private and public entities to justify the need to reach or maintain diversity within an organization. Clearly, the equality of the decision-making methodology and criteria used to obtain and maintain diversity was an issue, but perhaps more pressing was the question of whether such programs were necessary. The issue of parity is at the very center of these cases. If the normal admissions process would have resulted in obtaining the predicted number of minority admissions then there may no longer be a need for such programs. While the university cases have been most publicized recently, matters involving affirmative action plans and governmental programs to enhance diversity (such as minority contractor set-asides) face similar questions of parity.

Relational thinking and spatial analyses have become highly relevant for higher education research. However, choices of research methods and specifically of statistical procedures do not often correspond to the epistemological underpinnings implied by relational perspectives. Against this background, this chapter illustrates the uses and challenges of geometric data analysis (GDA) for studying the complexities and dynamics of current spaces of higher education. GDA can be described as a set of statistical techniques that allow the identification, assessment and visualisation of complex relations in social science data. Using an investigation into the social topologies of first-year students as an example, we discuss the mathematical foundations, the step-by-step procedures of data analysis, the interpretation of results and strategies for integrating GDA into multimethod research designs. In sum, we argue that GDA does not only entail a comprehensive set of statistical instruments that permit visual analysis of relational structures, but also enables the systematic integration of qualitative and quantitative methods, hence supporting the development of innovative and coherent research designs and analytical strategies.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.