Towards Full (er) Integration in Mixed Methods Research: The Role of Canonical Correlation Analysis for Integrating Quantitative and Qualitative Data

IF 0.4 Q4 EDUCATION & EDUCATIONAL RESEARCH
A. Onwuegbuzie
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引用次数: 0

Abstract

One of the biggest developments in mixed methods research has been the conceptualization of one or more analysis types associated with one tradition (e.g., qualitative analysis) being used to analyze data associated with a different tradition (e.g., quantitative data)—what Onwuegbuzie and Combs (2010) called crossover mixed analyses, or, more simply, crossover analyses. A hallmark of crossover analyses is the notion of quantitizing, which, in its simplest form, involves converting qualitative data into numerical forms that can be analyzed statistically. The focus on quantitizing has been on descriptive-based quantitizing approaches such as counting the occurrence of emergent themes. Unfortunately, scant guidance exists on inferential-based quantitizing, which refers to the quantitizing of qualitative data for the purpose of prediction or estimation (Onwuegbuzie, in press). Although recent literature has emerged on a few inferential-based quantitizing approaches (i.e., multiple linear regression analysis, structural equation modeling, hierarchical linear modeling), there still remains some general linear model analyses for which mixed methods researchers, in pursuit of conducting crossover analyses, can benefit from guidelines. One such analysis is canonical correlation analysis. Its importance stems from the fact that the analysis of qualitative data typically yields multiple patterns of meaning (e.g., codes, themes), which then can be correlated with other available variables (e.g., demographic variables, personality variables, affective variables) via the use of canonical correlation analysis. Therefore, the purpose of this article is (a) to describe canonical correlation analysis and (b) to illustrate how canonical correlation analyses can serve as an inferential-based quantitizing using a heuristic example.
迈向混合方法研究的全面整合:典型相关分析在整合定量和定性数据中的作用
混合方法研究的最大发展之一是将与一种传统相关的一种或多种分析类型(如定性分析)概念化,用于分析与不同传统相关的数据(如定量数据)——Onwuegbuzie和Combs(2010)称之为交叉混合分析,或更简单地说,交叉分析。交叉分析的一个标志是量化的概念,它最简单的形式是将定性数据转换为可以进行统计分析的数字形式。量化的重点一直是基于描述性的量化方法,如统计突发主题的发生。不幸的是,很少有关于基于推理的定量的指导,即为了预测或估计的目的对定性数据进行定量(Onwuegbuzie,出版中)。尽管最近出现了一些基于推理的定量方法的文献(即多元线性回归分析、结构方程建模、层次线性建模),但仍有一些通用的线性模型分析,混合方法研究人员在进行交叉分析时可以从指南中受益。一种这样的分析是典型相关分析。它的重要性源于这样一个事实,即定性数据的分析通常会产生多种意义模式(例如,代码、主题),然后可以通过使用规范相关性分析将其与其他可用变量(例如,人口统计变量、个性变量、情感变量)相关联。因此,本文的目的是(a)描述规范相关性分析,(b)使用启发式示例说明规范相关性分析如何作为基于推理的定量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Revista Publicaciones
Revista Publicaciones EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
2.00
自引率
0.00%
发文量
23
审稿时长
18 weeks
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