Writing with Discipline: A Call for Avoiding APA Style Guide Errors in Manuscript Preparation

The education community in the United States—as in many countries—is extremely large and diverse. Indeed, as documented by Mosteller, Nave, and Miech (2004), The United States has more than 3.6 million teachers in elementary and secondary education, more than 100,000 principals, and about 15,000 school districts, each with its own set of district administrators, school board members, and concerned citizens. The parents and family members of the 60 million students in elementary and secondary education represent another constituency, as do the policymakers and legislators in the 50 states (along with the District of Columbia) and at the federal level. Postsecondary education represents another 1 million faculty members, along with an enrollment of 15 million undergraduates and 1.8 million graduate students. (p. 29) Indeed, with the number of individuals involved in the educational system, educational research has the potential to play a pivotal role in improving the quality of education—from Kindergarten through primary, through secondary, through tertiary education. Yet, for educational research to play such a role, its findings must be disseminated to individuals (e.g., educators, administrators, stakeholders, policymakers) and groups (e.g., teacher associations) who can most effectively use them (Mosteller et al., 2004; Onwuegbuzie, Leech, & Whitmore, 2008). Unfortunately, research findings do not disseminate themselves, regardless of how statistically, practically, clinically, or economically significant they are for the field of education. Rather, it is educational researchers in general and practitioner-researchers in particular who must convey these findings

在混合方法研究中实现全面整合：典型相关分析在整合定量和定性数据中的作用

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.Uno de los mayores avances en la investigación con métodos mixtos ha sido la conceptualización de uno o más tipos de análisis asociados con una tradición (por ejemplo, el análisis cualitativo) que se utilizan para analizar datos asociados con una tradición diferente (por ejemplo, datos cuantitativos), lo que Onwuegbuzie y Combs (2010) denominaron análisis mixtos cruzados o, más sencillamente, análisis cruzados. Una característica distintiva de los análisis cruzados es la noción de cuantificación, que, en su forma más simple, implica la conversión de datos cualitativos en formas numéricas que puedan analizarse estadísticamente. La cuantificación se ha centrado en enfoques descriptivos, como el recuento de temas emergentes. Lamentablemente, apenas existen orientaciones sobre la cuantificación inferencial, que se refiere a la cuantificación de datos cualitativos con fines de predicción o estimación. Aunque ha aparecido literatura reciente sobre unos pocos enfoques de cuantificación basados en la inferencia (es decir, análisis de regresión lineal múltiple, modelización de ecuaciones estructurales, modelización lineal jerárquica), todavía quedan algunos análisis de modelos lineales generales para los que los investigadores de métodos mixtos, en la búsqueda de la realización de análisis cruzados, pueden beneficiarse de las directrices. Uno de estos análisis es el análisis de correlación canónica. Su importancia radica en el hecho de que el análisis de datos cualitativos suele arrojar múltiples patrones de significado (ej., códigos, temas), que luego pueden correlacionarse con otras variables disponibles (ej., variables demográficas, variables de personalidad, variables afectivas) mediante el uso del análisis de correlación canónica. Por lo tanto, el propósito de este artículo es (a) describir el análisis de correlación canónica e (b) ilustrar cómo los análisis de correlación canónica pueden servir como cuantificación basada en la inferencia utilizando un ejemplo heurístico.Одним из самых значительных достижений в области исследований смешанных методов стала концептуализация одного или нескольких видов анализа, связанных с одной традицией (например, качественный анализ), которые используются для анализа данных, связанных с другой традицией (например, количественных данных) - то, что Onwuegbuzie и Combs (2010) назвали перекрестным смешанным анализом, или, проще говоря, перекрестным анализом. Отличительной чертой перекрестного анализа является понятие квантификации, которое в своей простейшей форме предполагает преобразование качественных данных в числовые формы, которые могут быть проанализированы статистически. Основное внимание при количественном анализе уделялось количественным подходам, основанным на описательном подходе, таким как подсчет встречаемости возникающих тем. К сожалению, существует мало рекомендаций по количественному анализу на основе инференции, который относится к количественному анализу качественных данных с целью прогнозирования или оценки. Хотя в последнее время в литературе появилось несколько подходов к количественной оценке на основе инференции (например, множественный линейный регрессионный анализ, моделирование структурных уравнений, иерархическое линейное моделирование), все еще остаются некоторые общие линейные модельные анализы, для которых исследователи смешанных методов, стремящиеся провести перекрестный анализ, могут воспользоваться рекомендациями. Одним из таких анализов является канонический корреляционный анализ. Его важность обусловлена тем, что анализ качественных данных, как правило, дает множество моделей смысла (например, коды, темы), которые затем могут быть соотнесены с другими доступными переменными (например, демографическими переменными, переменными личности, аффективными переменными) с помощью канонического корреляционного анализа. Поэтому целью данной статьи является (а) описание канонического корреляционного анализа и (б) иллюстрация того, как канонический корреляционный анализ может служить в качестве квантификации на основе инференции на эвристическом примере.混合方法研究的最大发展之一是将与一种传统（例如，定性分析）相关的一种或多种分析类型概念化，用于分析与不同传统（例如，定量数据）相关的数据——Onwuegbuzie 和 Combs (2010) 称为交叉混合分析，或者更简单地说，交叉分析。交叉分析的一个标志是量化的概念，其最简单的形式涉及将定性数据转换为可以进行统计分析的数字形式。量化的重点是基于描述的量化方法，例如计算出现的主题。不幸的是，基于推理的量化缺乏指导，推理量化是指为了预测或估计的目的对定性数据进行量化（Onwuegbuzie，出版中）。尽管最近出现了一些基于推理的量化方法（即多元线性回归分析、结构方程建模、层次线性建模）的文献，但仍然存在一些通用线性模型分析，混合方法研究人员在进行交叉分析时进行分析, 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），然后可以通过使用将其与其他可用变量（例如，人口变量、性格变量、情感变量）相关联典型相关分析。因此，本文的目的是 (a) 描述典型相关分析和 (b) 使用启发式示例说明典型相关分析如何用作基于推理的量化

A Typology of Mixed Methods Sampling Designs in Social Science Research

This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and qualitative approaches. Third, we provide a sampling design typology and we demonstrate how sampling designs can be classified according to time orientation of the components and relationship of the qualitative and quantitative sample. Fourth, we present four major crises to mixed methods research and indicate how each crisis may be used to guide sampling design considerations. Finally, we emphasize how sampling design impacts the extent to which researchers can generalize their findings

Enhancing the Interpretation of Significant Findings: The Role of Mixed Methods Research

The present essay outlines how mixed methods research can be used to enhance the interpretation of significant findings. First, we define what we mean by significance in educational evaluation research. With regard to quantitative-based research, we define the four types of significance: statistical significance, practical significance, clinical significance, and economic significance. With respect to qualitative-based research, we define a significant finding as one that has meaning or representation. Second, we describe limitations of each of these types of significance. Finally, we illustrate how conducting mixed methods analyses can be used to enhance the interpretation of significant findings in both quantitative and qualitative educational evaluation and policy research. Consequently, mixed methods research represents the real gold standard for studying phenomena

Sampling Designs in Qualitative Research: Making the Sampling Process More Public

The purpose of this paper is to provide a typology of sampling designs for qualitative researchers. We introduce the following sampling strategies: (a) parallel sampling designs, which represent a body of sampling strategies that facilitate credible comparisons of two or more different subgroups that are extracted from the same levels of study; (b) nested sampling designs, which are sampling strategies that facilitate credible comparisons of two or more members of the same subgroup, wherein one or more members of the subgroup represent a sub-sample of the full sample; and (c) multilevel sampling designs, which represent sampling strategies that facilitate credible comparisons of two or more subgroups that are extracted from different levels of study

Linking Research Questions to Mixed Methods Data Analysis Procedures 1

The purpose of this paper is to discuss the development of research questions in mixed methods studies. First, we discuss the ways that the goal of the study, the research objective(s), and the research purpose shape the formation of research questions. Second, we compare and contrast quantitative research questions and qualitative research questions. Third, we describe how to write mixed methods research questions, which we define as questions that embed quantitative and qualitative research questions. Finally, we provide a framework for linking research questions to mixed methods data analysis techniques. A major goal of our framework is to illustrate that the development of research questions and data analysis procedures in mixed method studies should occur logically and sequentially

Without Supporting Statistical Evidence, Where Would Reported Measures of Substantive Importance Lead? To No Good Effect

Although estimating substantive importance (in the form of reporting effect sizes) has recently received widespread endorsement, its use has not been subjected to the same degree of scrutiny as has statistical hypothesis testing. As such, many researchers do not seem to be aware that certain of the same criticisms launched against the latter can also be aimed at the former. Our purpose here is to highlight major concerns about effect sizes and their estimation. In so doing, we argue that effect size measures per se are not the hoped-for panaceas for interpreting empirical research findings. Further, we contend that if effect sizes were the only basis for interpreting statistical data, social-science research would not be in any better position than it would if statistical hypothesis testing were the only basis. We recommend that hypothesis testing and effect-size estimation be used in tandem to establish a reported outcome’s believability and magnitude, respectively, with hypothesis testing (or some other inferential statistical procedure) retained as a “gatekeeper” for determining whether or not effect sizes should be interpreted. Other methods for addressing statistical and substantive significance are advocated, particularly confidence intervals and independent replications

Mixed Methods Analysis and Information Visualization: Graphical Display for Effective Communication of Research Results

In this paper, we introduce various graphical methods that can be used to represent data in mixed research. First, we present a broad taxonomy of visual representation. Next, we use this taxonomy to provide an overview of visual techniques for quantitative data display and qualitative data display. Then, we propose what we call “crossover” visual extensions to summarize and integrate both qualitative and quantitative results within the same framework. We provide several examples of crossover (mixed research) graphical displays that illustrate this natural extension. In so doing, we contend that the use of crossover (mixed research) graphical displays enhances researchers’ understanding (i.e., increased Verstehen) of social and behavioral phenomena in general and the meaning that underlies these phenomena in particular

Interviewing the Interpretive Researcher: An Impressionist Tale

In this manuscript, we describe the use of debriefing interviews for interviewing the interpretive researcher. Further, we demonstrate the value of using debriefing questions as part of a qualitative research study, specifically, one doctoral student’s dissertation study. We describe the reflexivity process of the student in her study and the debriefing data that were coded via qualitative coding techniques. Thus, we provide an exemplar of the debriefing process and the findings that emerged as a result. We believe that our exemplar of interviewing the interpretive researcher provides evidence of an effective strategy for addressing the crises of representation and legitimation for researchers and instructors of qualitative methods courses alike

A Framework for Using Qualitative Comparative Analysis for the Review of the Literature

Onwuegbuzie, Leech, and Collins (2012) demonstrated how the following 5 qualitative data analysis approaches can be used to analyze and to synthesize information extracted from a literature review: constant comparison analysis, domain analysis, taxonomic analysis, componential analysis, and theme analysis. In a similar vein, Onwuegbuzie and Frels (2014) outlined how discourse analysis can be used. Thus, the purpose of this article is to provide a framework for using another qualitative data analysis technique to analyze and to interpret literature review sources—a process that we call a Qualitative Comparative Analysis-Based Research Synthesis (QCARS). Using a real review of the literature, we illustrate how to conduct a QCARS using a qualitative comparative analysis software program