Business Research Methods Download.zip
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This paper discusses three common research approaches, qualitative, quantitative, and mixed methods, along with the various research designs commonly used when conducting research within the framework of each approach. Creswell (2002) noted that quantitative research is the process of collecting, analyzing, interpreting, and writing the results of a study, while qualitative research is the approach to data collection, analysis, and report writing differing from the traditional, quantitative approaches. This paper provides a further distinction between quantitative and qualitative research methods. This paper also presents a summary of the different research methods to conduct research in quantitative, qualitative, and mixed methods studies.
JIBS receives many manuscripts that report findings from analyzing survey data based on same-respondent replies. This can be problematic since same-respondent studies can suffer from common method variance (CMV). Currently, authors who submit manuscripts to JIBS that appear to suffer from CMV are asked to perform validity checks and resubmit their manuscripts. This letter from the Editors is designed to outline the current state of best practice for handling CMV in international business research.
Lindell and Whitney (2001), Podsakoff et al. (2003) and Malhotra, Kim, and Patil (2006) review several statistical methods that are more sophisticated than Harman's test, which can be used to test and possibly control for CMV. Different statistical remedies are available for different types of research settings and different sources of CMV. Promising statistical remedies include a partial correlation procedure and a direct measure of a latent common method factor. The former method partials out the first unrotated factor from the exploratory factor analysis, and then continues to determine whether the theoretical relationships among the variables of interest do still hold. The latter method allows questionnaire items to load on their theoretical constructs, as well as on a latent CMV factor, and examines the significance of theoretical constructs with or without the common factor method. Both methods have their own limitations, however, one of which is the assumption that the sources of CMV can be well identified and validly measured.Footnote 7 A recommended solution is to use multiple remedies, not just one remedy, in order to assuage the various concerns about CMV.
These statistics, of course, tell us only how frequently common methods appear in recently published JIBS articles, not the magnitude of the potential bias from CMV in these articles. Previous research estimating the magnitude of the effects did not include JIBS articles; see for example, Doty and Glick (1998) and Cote and Buckley (1987). So, the most we can say is there may be a problem based on frequency of usage of common methods, but at present we have no estimates of the magnitude of the problem.
Based on this short survey, it appears that common method bias has not been recognized nor addressed by most IB scholars, even in JIBS, the top journal in the field of international business. We recognize, of course, that standards for rigor in empirical work are continually rising. What were acceptable methodological practices even five years ago can easily and rapidly become unacceptable as social science scholars better understand the limitations of their empirical techniques and develop more rigorous methods for identifying and correcting for potential biases in their work. The purpose of our Letter from the Editors is therefore not to criticize earlier research, but rather to encourage IB scholars to implement current best practices in research methods. We argue that the hurdle barrier must now be set higher in JIBS vis à vis CMV. It is time for IB scholars to address, and reduce or offset where feasible, the use of common methods in their empirical work.
As international business researchers, we also recognize that sometimes common methods cannot be totally avoided, for example, if the research probes into difficult waters where data of any kind are scarce such as in severely understudied parts of the world (Africa, the Middle East), or where the research undertaken is so novel or insightful that this may be considered over standard methodological considerations. Editors and reviewers should not reject innovative manuscripts that push the boundaries of our knowledge of international business solely on the grounds of common methods. In such situations, the gains in creativity and impact might outweigh the loss of methodological purity.
The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information. However, in recognition of the potential utility of health information even when it is not individually identifiable, 164.502(d) of the Privacy Rule permits a covered entity or its business associate to create information that is not individually identifiable by following the de-identification standard and implementation specifications in 164.514(a)-(b). These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual.4 As discussed below, the Privacy Rule provides two de-identification methods: 1) a formal determination by a qualified expert; or 2) the removal of specified individual identifiers as well as absence of actual knowledge by the covered entity that the remaining information could be used alone or in combination with other information to identify the individual.
This book is designed to introduce doctoral and graduate students to the process of scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any course on research methods.
The contents and examples are designed for anyone interested in behavioral research (not just information systems people), and so, the book should appeal to most business programs, social sciences, education, public health, and related disciplines.
While some acceptable methods of combining ZIP codes and Census geography exist, they have limitations. To provide additional avenues for merging these data, PD&R has released the HUD-USPS Crosswalk Files. These unique files are derived from data in the quarterly USPS Vacancy Data. They originate directly from the USPS; are updated quarterly, making them highly responsive to changes in ZIP code configurations; and reflect the locations of both business and residential addresses. The latter feature is of particular interest to housing researchers because many of the phenomena that they study are based on housing unit or address. By using an allocation method based on residential addresses rather than by area or by population, analysts can take into account not only the spatial distribution of population, but also the spatial distribution of residences. This enables a slightly more nuanced approach to allocating data between disparate geographies. Please note that the USPS Vacancy Data is constructed from ZIP+4 data that contains records of addresses, it does not contain ZIP+4 data that are associated with ZIP codes that exclusively serve Postal Office Boxes (PO Boxes). As a result, ZIP codes that only serve PO Boxes will not appear in the files.
Our technical reports written by CCRAM experts provide statistical software use advice, introduce new or expand on existing methods, or otherwise provide guidance on data analysis and research design topics. These reports are unpublished but some may be working their way through the publication process. Some of the titles of our technical reports include:
PROCESS is a computational tool invented by CCRAM expert Andrew F. Hayes. It is freely-available for SPSS, SAS, and R, and has become widely used throughout the behavioral sciences as well as in business research, medicine, and public health for easing the estimation of effects in mediation, moderation, and conditional process analysis.
You learned about research methods and data analysis as a part of your current or past graduate training. But you probably felt soon after graduation (if not before) that this training was incomplete and that what you learned was truly just the beginning of a continuous journey of discovery and staying up to date on advances in data analysis and design. Furthermore, whether you do your research in academia, industry or government, time becomes increasingly scarce to stay current in newly developed research tools and methodologies.
ERMA 7240 THINKING WITH THEORY IN QUALITATIVE RESEARCH (3) LEC. 3. Pr. ERMA 7210 or ERMA 7216. This course is designed to give an overview and introduction to a variety of theorists and theoretical approaches that are often used in qualitative inquiry. We will compare and contrast the assumptions, design, and methods of different philosophies and theories in qualitative research.
ERMA 7300 DESIGN AND ANALYSIS IN EDUCATION I (3) LEC. 3. Departmental approval. Basic methods of inferential analysis including t-tests, between and within subjects ANOVA, mixed ANOVAs and hierarchical designs as they are utilized in educational research.
ERMA 7400 MIXED METHODS RESEARCH (3) LEC. 3. Pr. ERMA 7200 or ERMA 7206 or ERMA 7210 or ERMA 7216 or ERMA 7300 or ERMA 7306. Overview and introduction to the use of mixed methods research in the social and behavioral sciences.
ERMA 7410 RESEARCH METHODS FOR SOCIAL JUSTICE AND EQUITY (3) LEC. 3. Pr. ERMA 7300 or ERMA 7306 or ERMA 7210 or ERMA 7216 or ERMA 7400. This course focuses on critical educational research in the scholar-activist model. Through engaging with critical theoretical frameworks, critical empirical research, and research methods, this course prepares students to produce research and scholarship for social justice and equity in education.
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