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Business Research Methods And Statistics Using Spss Pdf 60


Inferential statistics is used for several purposes, such as research, in which we wish to draw conclusions about a population using some sample data. This is performed in a variety of fields, ranging from government operations to quality control and quality assurance teams in multinational corporations.




business research methods and statistics using spss pdf 60



These are typical questions that require statistical analysis for the answers. In order to answer these questions, a good random sample must be collected from the population of interests. We then use descriptive statistics to organize and summarize our sample data. The next step is inferential statistics, which allows us to use our sample statistics and extend the results to the population, while measuring the reliability of the result. But before we begin exploring different types of statistical methods, a brief review of descriptive statistics is needed.


Descriptive measures of samples are called statistics and are typically written using Roman letters. The sample mean is (x-bar). The sample variance is s2 and the sample standard deviation is s. Sample statistics are used to estimate unknown population parameters.


This essay builds on the exposition by Thomas et al. and focuses on analyzing cause and effect in international business research. We attempt to explain how endogeneity problems occur and why they are so prevalent in international business research in a non-technical fashion. We then discuss the importance of explicitly identifying how the chosen research design best approximates a randomized-controlled experiment. Finally, we provide some guidelines on achieving this goal and emphasize the practices that seem most relevant to JIBS reviewers in evaluating high-quality international business research.


Empirical research in international business (IB) is difficult. Our interests typically center on whether some particular IB phenomenon causes a specific outcome or effect. We might, for instance, be interested in how expatriate postings influence future career opportunities. Or we might be seeking to understand how firm-level internationalization affects corporate decision-making. In an ideal research setting, to test such a cause and effect, we would examine the impact of firm internationalization on a particular outcome (such as profitability) by randomly assigning some firms to be multinational corporations (MNCs) and other firms to be domestic corporations (DCs). Experimentalists would characterize these as the treatment and control groups. Preferably, we would then observe and compare the subsequent decision-making of the firms in the treatment and control groups over the next few years regarding the specific variable of interest (i.e., profitability). Inherent in this approach is the notion that we would randomly select the firms to place into the treatment and control groups (i.e., the MNC and DC groupings) in our sample. In general, the iconic test procedure involves developing a random experiment, regardless of whether the unit of analysis centers on individuals, firms, industries or countries.


An illustration at the individual level often serves as the best example to highlight this non-random treatment problem. Consider an international business researcher who is interested in testing a program to help facilitate cross-cultural teamwork. For convenience, the researcher provides the training to a group of professors at the university where s/he is employed. One year later s/he observes faculty effectiveness in cross-cultural teams and compares this to cross-cultural team effectiveness in the general population. Specifically, s/he regresses the cross-cultural teamwork effectiveness on faculty appointment and discovers that, consistent with a positive treatment, the university professors in the sample have greater cross-cultural team effectiveness than does the general population. The researcher then reports an effective cross-cultural teamwork effect with the treatment group and concludes that firms should consider approving the training program for workers in their companies.


This section provides a brief (and hopefully intuitive) explanation of some of the statistical remedies that IB scholars use in their cross-sectional tests. These short descriptions of several common methods for dealing with the non-random treatment problem are not the main focus of this essay (however, Roberts and Whited, 2011 provide a thorough analysis). Rather our emphasis is on the importance of careful research design that incorporates field research or institutional knowledge to develop tests with observational data to facilitate causal inference.


Theoretical predictions in international business research are often direct and straightforward, suggesting that internationalization causes some activity to occur. The simplest test in this circumstance is to focus on univariate statistical differences between the groups of interest (i.e., MNCs vs DCs). Yet we all appreciate that we must control for other individual or firm attributes to properly gauge the relation of interest. At the most basic level this occurs because we do not have randomized controlled experiments. In essence, the inclusion of control variables in a multivariate regression is an attempt to deal with the non-random nature of the treatment effect in our analysis. Unfortunately, in many circumstances, this control variable approach is insufficient to deal with the non-random treatment effect problems that we encounter. Potential sources of problems include the omission of some important variables, reverse causality, and measurement error in the variables of interest (Roberts & Whited, 2011). Thus, it appears to reviewers that this empirical approach is chosen because of its ease of use rather than because it emerged as a well-designed strategy to make the tests more like an experiment.


One formal approach to dealing with the non-random treatment effect centers on developing a structural model. Structural models provide rigid and explicit equations of individual or firm behavior that rely on idealistic assumptions.Footnote 4 Although structural models are often couched in technical jargon, the intuition behind using them suggests a simple framework for developing the theoretical underpinnings of the eventual empirical specification. Fundamentally, one should think about how the observed variations in the right-hand-side variable of interest may have emerged. The scholar's institutional knowledge and ideas about how the treatment decision emerges are critical components to sound empirical research design (Angrist & Pischke, 2008). As we are unable to randomly assign firms into the treatment/control groups, understanding how the firms were initially assigned to the treatment or control group is essential to developing testable hypotheses.


Consider an example regarding the determinants of FDI. An IB scholar might be interested in evaluating the idea that firm-level FDI is driven by firms seeking to find low cost employees. Accounts in the business press routinely describe investment in China and job migration of the US in this fashion. One approach to test this idea would be to compare FDI within a country, across different states/provinces, based on the average wage rate. Alternatively, one could make the same sort of comparison across multiple countries or geographic regions. A typical premise to test this maintained hypothesis might be: FDI is negatively related to wages. Basile (2004) provides such a test in the context of FDI across Italy using foreign acquisitions. Specifically, evidence is found to suggest foreign direct investment is positively related to wage rates. One might be tempted to conclude that our theoretical prediction was incorrect; instead we found that firms were targeting FDI in provinces with high wages.


The success of empirical business research over the past three decades is based on simple, straightforward theories that provide qualitative predictions and conform to observed real world phenomena. Yet we still need to incorporate the notion that the independent variable is unlikely to be randomly distributed across firms. Generally speaking, the theoretical predictions that we develop should incorporate, by design, the non-random component of our right-hand-side variable. Reinterpreting Marschak (1953), it is not necessary or desirable to fully specify a structural model of the dependent variable, but one does need to consider the fundamental economic issues that lead to non-random assignments of the treatment effect among the firms in the sample. In the absence of a randomized controlled experiment, we need to incorporate the non-random assignment into the treatment group in our research design to improve causal inference.


Across multiple international business subfields, we find that researchers who carefully consider how a phenomenon arose in the cross section are often the most successful at JIBS. Following these examples we encourage researchers to speak to managers, market participants, bankers or consultants in the area to obtain the institutional details that are crucial to understanding the nature of causality in a particular phenomenon. The acid test is whether the research design in an empirical study with observational data is the one that best approximates a randomized-controlled experiment for the hypothesis of interest.


The qualitative research methods above (in-depth interviews, focus group discussions, and observation) are most commonly used for collecting qualitative data. However, lesser-known qualitative research methods include literature and document review of existing material on the research topic. These can be helpful for identifying if the research questions have been partly or fully answered in the past.


Once an association has been established, our attention turns to determini


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