Quality & Quantity 

About: Quality & Quantity is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Population & Medicine. It has an ISSN identifier of 0033-5177. Over the lifetime, 3553 publications have been published receiving 82531 citations. The journal is also known as: Quality & quantity.

A caution regarding rules of thumb for variance inflation factors.

Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.

Saturation in qualitative research: exploring its conceptualization and operationalization

Abstract: Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

A purposeful approach to the constant comparative method in the analysis of qualitative interviews

Abstract: The constant comparative method (CCM) together with theoretical sampling constitutethe core of qualitative analysis in the grounded theory approach and in other types ofqualitative research. Since the application of the method remains rather unclear, researchers do not know how to `go about' the CCM in their research practice. This study contributes to a purposeful approach of the CCM in order to systematize the analysis process and to increase the traceability and verification of the analyses. The step by step approach is derived from and illustrated with an empirical study into the experience of multiple sclerosis (MS) by patients and their spousal care providers. In this study five different steps were distinguished on the basis of four criteria: (1) the data involved and the overall analysis activities, (2) the aim, (3) the results and (4) the questions asked. It is concluded that systematization of qualitative analysis results from the researcher using a sound plan for conducting CCM regarding these four aspects.

Determinants of social desirability bias in sensitive surveys: a literature review

Abstract: Survey questions asking about taboo topics such as sexual activities, illegal behaviour such as social fraud, or unsocial attitudes such as racism, often generate inaccurate survey estimates which are distorted by social desirability bias. Due to self-presentation concerns, survey respondents underreport socially undesirable activities and overreport socially desirable ones. This article reviews theoretical explanations of socially motivated misreporting in sensitive surveys and provides an overview of the empirical evidence on the effectiveness of specific survey methods designed to encourage the respondents to answer more honestly. Besides psychological aspects, like a stable need for social approval and the preference for not getting involved into embarrassing social interactions, aspects of the survey design, the interviewer’s characteristics and the survey situation determine the occurrence and the degree of social desirability bias. The review shows that survey designers could generate more valid data by selecting appropriate data collection strategies that reduce respondents’ discomfort when answering to a sensitive question.

A typology of mixed methods research designs

Abstract: The mixed methods paradigm is still in its adolescence, and, thus, is still relatively unknown and confusing to many researchers. In general, mixed methods research represents research that involves collecting, analyzing, and interpreting quantitative and qualitative data in a single study or in a series of studies that investigate the same underlying phenomenon. Over the last several years, a plethora of research designs have been developed. However, the number of designs that currently prevail leaves the doctoral student, the beginning researcher, and even the experienced researcher who is new to the field of mixed methods research with the challenge of selecting optimal mixed methods designs. This paper presents a three-dimensional typology of mixed methods designs that represents an attempt to rise to the challenge of creating an integrated typology of mixed methods designs. An example for each design is included as well as a notation system that fits our eight-design framework.