Aim This paper is a description of inductive and deductive content analysis. Background Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. Discussion When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. Conclusion Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.

/pdf/the-qualitative-content-analysis-process-4mo68rv7vy.pdf

The qualitative content analysis process

Qualitative content analysis is commonly used for analyzing qualitative data. However, few articles have examined the trustworthiness of its use in nursing science studies. The trustworthiness of q...

https://journals.sagepub.com/doi/pdf/10.1177/2158244014522633

Qualitative Content Analysis: A Focus on Trustworthiness

Titles & abstract s Titles & abstracts Include N=54 Include N=121 Include N=89 Exclude N = 0 Exclude N = 1,024 Exclude N = 21

Guidance on the conduct of narrative synthesis in sytematic reviews

Policy-makers and managers have always used a wide range of sources of evidence in making decisions about policy and the organization of services. However, they are under increasing pressure to adopt a more systematic approach to the utilization of the complex evidence base. Decision-makers must address complicated questions about the nature and significance of the problem to be addressed; the nature of proposed interventions; their differential impact; cost-effectiveness; acceptability and so on. This means that Cochrane-style reviews alone are not sufficient. Rather, they require access to syntheses of high-quality evidence that include research and non-research sources, and both qualitative and quantitative research findings. There is no single, agreed framework for synthesizing such diverse forms of evidence and many of the approaches potentially applicable to such an endeavour were devised for either qualitative or quantitative synthesis and/or for analysing primary data. This paper describes the key stages in reviewing and synthesizing qualitative and quantitative evidence for decision-making and looks at various strategies that could offer a way forward. We identify four basic approaches: narrative (including traditional 'literature reviews' and more methodologically explicit approaches such as 'thematic analysis', 'narrative synthesis', 'realist synthesis' and 'meta-narrative mapping'), qualitative (which convert all available evidence into qualitative form using techniques such as 'meta-ethnography' and 'qualitative cross-case analysis'), quantitative (which convert all evidence into quantitative form using techniques such as 'quantitative case survey' or 'content analysis') and Bayesian meta-analysis and decision analysis (which can convert qualitative evidence such as preferences about different outcomes into quantitative form or 'weights' to use in quantitative synthesis). The choice of approach will be contingent on the aim of the review and nature of the available evidence, and often more than one approach will be required.

Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field

Introduction Using an appropriate research method for inquiry is critical to successful research. Grounded theory and qualitative content analysis share similarities. Both are based on naturalistic inquiry that entails identifying themes and patterns and involves rigorous coding. They are both used to analyze and interpret qualitative data; however, the similarities and differences in grounded theory and qualitative content analysis have not been clarified in the literature (Priest, Roberts, & Woods, 2002), nor have they been consistently considered. To illustrate, both have been considered equivalent approaches to interpret qualitative data (e.g., Priest et al., 2002). Grounded theory was treated as a research methodology, and content analysis as a method (e.g., Crotty, 2003); furthermore, grounded theory was considered a theoretical framework and content analysis a research method of textual data analysis (e.g., Patton, 2002). Qualitative content analysis was considered a strategy for the analysis of qualitative descriptive studies (Sandelowski, 2000) and a technique with overtones of other research methods, such as ethnographic and grounded theory (Altheide, 1987). Qualitative content analysis was unknown as a research method until recently, especially in English-speaking countries, because of the dominance of quantitative content analysis (Schreier, 2012). Moreover, a researcher's approach purportedly following grounded theory actually seems closer to qualitative content analysis or other methods (Sandelowski & Barroso, 2003; Suddaby, 2006). Sandelowski and Barroso (2003) cited the discrepancy between "method claims and the actual use of methods" (p. 905) in research papers. Novice researchers, especially students who want to conduct qualitative research, are often confused by the characteristics of the two as result of the lack of comparative references. Some researchers who stated they had used grounded theory actually used qualitative content analysis, which incorporates some procedures of grounded theory, such as open coding or memoing (Sandelowski & Barroso, 2003). Thus, the purpose of this paper is to clarify ambiguities about the characteristics of grounded theory and qualitative content analysis. Using our own research as examples, we have discussed the similarities and differences in the two in the following six areas: a) background and philosophical basis, b) unique characteristics of each method, c) goals and rationale of each method, d) data analysis process, e) outcomes of the research, and f) evaluation of trustworthiness of research. We have also discussed the strengths and weaknesses of each. Through this paper, we expect to provide knowledge that can assist novice researchers in the selection of appropriate research methods for their inquiries. Background and Philosophical Basis Grounded Theory The term grounded theory was introduced in The Discovery of Grounded Theory (1967) by Glaser and Strauss as "the discovery of theory from data--systematically obtained and analyzed in social research" (p. 1). Instead of verification of theories, they introduced a research method to arrive at a "theory suited to its supposed uses" contrasting with a "theory generated by logical deduction from a priori assumptions" (p. 3). According to Strauss and Corbin (1994) it is "a general methodology, a way of thinking about and conceptualizing data" (p. 275). The Grounded Theory Institute, run by Glaser, one of the founders of grounded theory, defined it as follows: Grounded Theory is an inductive methodology. Although many call Grounded Theory a qualitative method, it is not. It is a general method. It is the systematic generation of theory from systematic research. It is a set of rigorous research procedures leading to the emergence of conceptual categories. …

/pdf/reducing-confusion-about-grounded-theory-and-qualitative-3tjmfa2e6g.pdf

Reducing Confusion about Grounded Theory and Qualitative Content Analysis: Similarities and Differences

/pdf/an-overview-of-content-analysis-53zvb3be0k.pdf

An Overview of Content Analysis.

Researchers rely on psychometric principles when trying to gain understanding of unobservable psychological phenomena disconfounded from the methods used. Psychometric models provide us with tools to support this endeavour, but they are agnostic to the meaning researchers intend to attribute to the data. We define method effects as resulting from actions which weaken the psychometric structure of measurement, and argue that solution to this confounding will ultimately rest on testing whether data collected fit a psychometric model based on a substantive theory, rather than a search for a model that best fits the data. We highlight the importance of taking the notions of fundamental measurement seriously by reviewing distinctions between the Rasch measurement model and more generalised 2PL and 3PL IRT models. We then present two lines of research that highlight considerations of making method effects explicit in experimental designs. First, we contrast the use of experimental manipulations to study measurement reactivity during the assessment of metacognitive processes with factor-analytic research of the same. The former suggests differential performance-facilitating and -inhibiting reactivity as a function of other individual differences, whereas factor-analytic research suggests a ubiquitous monotonically predictive confidence factor. Second, we evaluate differential effects of context and source on within-individual variability indices of personality derived from multiple observations, highlighting again the importance of a structured and theoretically grounded observational framework. We conclude by arguing that substantive variables can act as method effects and should be considered at the time of design rather than after the fact, and without compromising measurement ideals.

/pdf/sophisticated-statistics-cannot-compensate-for-method-1mmxfz5v.pdf

Steve Stemler

Papers

An Overview of Content Analysis.

Sophisticated Statistics Cannot Compensate for Method Effects If Quantifiable Structure Is Compromised

Exploring the links between household chores, learning, and mathematics performance in Zambia