Chapter 8 gives an overview of the general structure and composition of oceanic lithosphere – the most common type of lithosphere on Earth. Its thickness, layered structure, seismicity, variation in seafloor elevation, magnetic anomaly pattern and composition are put in context of oceanic spreading and associated processes. The range of spreading rates, from ultraslow to superfast, is discussed. Differences in spreading rate have implications for the size of the magma chamber under the spreading ridge and therefore for the thicknesses of the different oceanic crustal layers. Slow spreading also favors exhumation of mantle and the formation of extensional detachments and core complexes. In this chapter the crystallization of melt to form oceanic crust is discussed along with the formation of hydrothermal mineralization and smokers. Hydrothermal activity produces ores that represent important metal resources that may be mined in the future. This chapter also presents ophiolites, oceanic crust on land, and simple models for obduction.

In Chapter 7, we explore why people choose to join groups and the factors that influence their membership decisions. We discuss factors that make groups more attractive as well as less attractive in the eyes of prospective members. We also discuss the challenges and benefits that can be derived from multiple group memberships.

Chapter 8 examines the t-test and its assumptions as it applies to mean comparison between samples and populations and experimental designs such as between-subjects, within-subjects, and matched designs. The t-test or student’s test is the most simple and elegant test of significance. The chapter examines the t-test for a single sample used for simple test designs, as well as the t-test for independent means, used when two populations are being compared, followed by the t-test for dependent means, used when measurements are related or correlated.

The strategies of MNEs at the start of the twenty-first century were shaped by the turbulent international environment that redefined global competition in the closing decades of the twentieth century. It was during that turmoil that a number of different perspectives and prescriptions emerged about how companies could create strategic advantages in their worldwide businesses.

Historically, the strategic challenge for a company has been viewed primarily as one of protecting potential profits from erosion through either competition or bargaining. Such erosion of profits could be caused not only by the actions of competitors but also by the bargaining powers of customers, suppliers, and governments. The key challenge facing a company was assumed to be its ability to maintain its independence by maintaining strong control over its activities. Furthermore, this strategic approach emphasized the defensive value of making other entities depend on it by capturing critical resources, building switching costs, and exploiting other vulnerabilities.

Chapter 9 introduces students to one-way or one-factor analysis of variance (ANOVA) and factorial designs involving two or more factors. Step-by-step calculation demonstrations are provided. However, a greater emphasis is placed on conceptual understanding than on computation, especially for factorial designs or multifactor ANOVA. The first part of the chapter explores the logic of ANOVA and the steps required, after rejecting a false null hypothesis, to understand and apply ANOVA. The second part introduces factorial designs involving multiple factors.

This book focuses on the management challenges associated with developing the strategies, building the organizations, and managing the operations of companies whose activities stretch across national boundaries. Operating in an international rather than a domestic arena clearly presents managers with many new opportunities. Having worldwide operations not only gives a company access to new markets and low-cost resources, it also opens up new sources of information and knowledge and broadens the options for the strategic moves the company might make to compete with its domestic and international rivals. However, with all these new opportunities come the challenges of managing strategy, organization, and operations that are innately more complex, diverse, and uncertain.

Chapter 2 discusses the different graphic techniques for describing data. These include bar graphs, histograms, and frequency polygons, as well as shapes and patterns of distributions. Raw scores are often arranged into frequency distributions, which are orderly arrangements of intervals of a given size which contain frequencies. Frequency distributions organize data in ways that can be viewed and interpreted by investigators. Constructing intervals of specified sizes to encompass scores in a distribution enables investigators to capture certain features that suggest particular statistical techniques for data analysis. Large amounts of collected data from questionnaires, observations, or experiments can be summarized by a simple chart or graph. The meaningfulness, relevance, and understanding of this graphic representation cannot be overstated.

Chapter 7 introduces statistical power and effect size in hypothesis testing. Guidelines for interpretation of effect size, along with other sources of increasing statistical power, are provided. Point estimation and interval estimation and their relationship to population parameter estimates and the hypothesis-testing process are considered. Statistical significance is highly sensitive to large sample sizes. This means that researchers, in addition to selecting desired statistical significance p-values, need to know the magnitude of the treatment effect or the effect size of the behavior under consideration. Effect size determines sample size, and sample size is intimately related to statistical power or the likelihood of rejecting a false null hypothesis.

Chapter 10 examines correlation, the statistical procedure used to measure the degree of association or relationship between variables. It is bivariate, since we typically apply this statistical technique to measure or describe the association between two variables or groups. The correlation coefficient, which measures the degree and direction of an association, is discussed, as are some of the issues regarding the application and interpretation of correlations. The chapter also outlines the many different measures of association but focuses on Pearson’s r. It emphasizes the definitional formula and z-scores for understanding and computing the correlation coefficient.

Chapter 3 examines measures of central tendency and their correspondence to normality and skewness. The three measures of central tendency presented include the mode, median, and mean. The mean is typically thought of as the average. The mode is the score occurring most frequently in a distribution of scores; the median is the central score, or the point which divides a distribution into two equal parts. The median is a robust statistic. The level of measurement assumption is crucial in selecting the best measure of central tendency for specific analyses.