Mixed methods data analysis procedures
Summary, this concordance analysis used initial and revised solutions to generate an “optimal solution,” while also working to create “strong thematic categories. Of sample size and approach qualitative studies are idiographic in approach, typically focusing on depth of analysis in small samples of participants.
Mixed methods data analysis
Clearly, some aspects of this question lend themselves to survey data – the timing and frequency of sexual risk behavior among adolescents. 2008) but one that as a discovered variable can aid in describing new and important conditional and interactive 5: data analytic approachesoverview of data analytic approaches descriptive and correlation analyses may now be conducted to examine associations among the qualitatively constructed thematic and the quantitatively based measured variables (castro & coe, 2007).
In contrast, concurrent designs are characterized by the collection of both types of data during the same stage. 1paradigm for the integrative mixed methods research approachbased on a specified theory or conceptual framework, a core category or construct, such as machismo, can be featured as a study's core construct.
A core feature of this approach is parallelism in study design, where integration begins with a unified conceptualization of information as “research evidence,” which can take the form of verbal text narrative evidence (qualitative) or numeric data evidence (quantitative). 2003) have indicated that, “there is still limited guidance for how to conduct and analyze such transformations [the qualitative–quantitative exchange of data] in practice” (p.
The results of this exploratory factor analysis provided initial confirmatory evidence in support of the content validity of the constructed machismo thematic variables, as these thematic variables aptly captured the expected two-factor structure for this construct of machismo self-identification. 2005) to facilitate rich, “deep structure,” data analyses (resnicow, soler, braithwait, ahluwalia, & butler, 2000) and ucting and deconstructing factorially complex constructs the imm approach offers procedures to study factorially complex constructs, such as the latino gender-role construct of machismo (torres, 1998).
Although such associations can be explored using visual case-ordered and predictor-outcome matrix methods that allow a cross-tabulation of categorical information (miles & huberman, 1994), nonetheless, these methods have lacked the capacity to reliably assess the strength of association among key categories or constructs, as can be accomplished with quantitative methods such as correlational among mixed methods studies, a common limitation has been the use of qualitative and quantitative approaches in a sequential temporal order, thus limiting the integration of both data forms under a unified process of data analysis (bryman, 2007). While the weight of each phase may be equal, it is more common that one phase is emphasized based on the primary logic that guides the mixed-method study.
However, given the convention that, “the case is the unit of analysis,” each case should contribute only one scale code value to a given thematic category, so what to do? Research investigator may choose to establish a different convention or decision rule if a review of the response codes presents several responses where truncating these according to a, “highest code rule,” introduces distortions that compete with the principal aim of “allowing the data to speak for itself.
In concurrent nested designs, both qualitative and quantitative data are collected during the same stage, although one form of data is given more weight over the other (creswell et al. Though this is the most common approach to mixed-methods research it can be challenging for researchers to design two equally-strong phases of research, and the integration of results can be difficult especially when contradictions emerge from the data.
Similar to sequential nested designs, concurrent transformative designs are theoretically driven to initiate social change or advocacy, and these designs may be used to provide support for various ative mixed methods designs within the context of these design approaches, the need persists for a methodology that affords a rigorous and integrative analysis of qualitative textual evidence and quantitative numeric data (schwandt, 1994). The purpose of concurrent triangulation designs is to use both qualitative and quantitative data to more accurately define relationships among variables of interest.
In principle, a well-crafted study with this design would allow “seamless” data conversions, for example, the conversion of qualitative thematic categories into numeric thematic variables (castro & coe, 2007). Matching thematic categories produced by the independent raters as we have developed this methodology, in a concordance analysis, we examine both independent coder solutions to reconcile them into an “optimal solution,” as defined above.
Examining selected text narratives identified by the results of a regression model analysis allows the creation of story lines that can contribute to a deep-structure analysis that moves “beyond description to conceptualization” (strauss & corbin, 1990, p. The researcher therefore decides to use a nationally-representative data set to explore risk behavior and to concurrently conduct in-depth interviews with adolescents to understand how they view gender expectations and how these relate to their sexual decision making.
From our prior research, “weak thematic categories” later produce “skewed thematic variables,” which are problematic for quantitative data analyses. A second goal is to describe methodological adaptations of our original imm approach (castro & coe, 2007), which was originally developed using an earlier-generation text analysis software program, textsmart 1.
In each of these designs, the quantitative and qualitative data are collected during the same stage, although priority may be given to one form of data over the other. In sequential designs, either the qualitative or quantitative data are collected in an initial stage, followed by the collection of the other data type during a second stage.
Generally, the greater the qualitative–quantitative parallelism that is designed a priori into a study, the easier to transform, transfer, and interpret textual and numeric data forms across modalities (plano clark et al. Ideally equal weight is given to each phase, with the results of both interpreted concurrently to determine whether there is agreement in the data collected through each approach.
Within a hierarchical regression analysis, the predictive effects of the inductively derived thematic variables can also be examined (a) as a unified block consisting of a set of thematic variable predictors along with a set of measured variable predictors or (b) as thematic variable predictors of an effect above and beyond (in sequentially introduced blocks) the effects of a previously entered block of measured variable predictors (cohen, cohen, west, & aiken, 2003). It aims to incorporate the strengths of qualitative and quantitative approaches for conducting rigorous data analyses that meet scientific standards of reliable and valid measurement and methods design approachessequential mixed methods designs creswell, plano clark, gutmann, and hanson (2003) classified mixed methods designs into two major categories: sequential and concurrent.