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

Toward a Conceptualization of Mixed Methods Phenomenological Research

Increasingly, researchers are recognizing the benefits of expanding research designs that are rooted in one tradition (i.e., monomethod design) into a design that incorporates or interfaces with the other tradition. The flexibility of phenomenologically driven methods provides one such example. Indeed, phenomenological research methods work extremely well as a component of mixed methods research approaches. However, to date, a mixed methods version of phenomenological research has not been formally conceptualized. Thus, the purpose of this article is twofold. First, we provide a philosophical justification for using what we call mixed methods phenomenological research (MMPR). Second, we provide examples of MMPR in practice to underline a number of potential models for MMPR that can practically be used in future research

Surveying the Landscape of Mixed Methods Phenomenological Research

As the popularity and scope of mixed methods research (MMR) develops, there is increasingly a need to map the growing body of literature in order to provide more inclusive frameworks of this form of research. Whilst mixed methods phenomenological research (MMPR) has been recently conceptualized, there is a lack of systematic evidence that outlines how this approach is being adopted by researchers. Thus, the purpose of this article was to explore the current implementation of MMPR within empirical research studies in order to provide a clearer picture of how, why, and where this research approach is being adopted. Findings demonstrated that whilst the prevalence of MMPR is increasing, the majority of this work is conducted within the health sciences and prioritises the phenomenological phase. Further, a model of the five key purposes of conducting MMPR is proposed. Finally, it is concluded that MMPR articles often fail to outline adequately their methodological procedures

The Ecumenical Theology of Joseph Ratzinger in the light of Unitatis redintegratio

The task of Evangelization becomes even more pressing in our times owing to the need to make Christ known to the ends of the earth. Over the years, disunity inhibited Christians from working together towards attaining this goal. The Decree on Ecumenism of the Second Vatican Council, Unitatis Redintegratio, marked a turning point in the history of the relationship between the Catholic Church and other Churches and Ecclesial Communities because it paved the way for a concrete step, on the part of the Catholic Church, towards the realization Christian unity. The Ecumenical theology of Joseph Ratzinger in the light of the Decree Unitatis Redintegratio studies his stance on how the self-understanding of the Catholic Church guides her relationship with other Churches and Ecclesial Communities. Ratzinger affirms that Christian unity is possible, and it is a slow, painstaking and thorough process. Ratzinger emphasises the fact that true Christian unity is the action of the Trinity. The work is divided into three Chapters. The First Chapter traces the development of the Decree while paying attention to the themes raised therein. It helps to situate the context within which Ratzinger’s Ecumenical theology should be understood. The Second Chapter demonstrates how Ratzinger’s ecclesiology influences his approach to ecumenism. The Third Chapter examines the main themes in Joseph Ratzinger’s Ecumenical Theology in the light of Unitatis Redintegratio, the official Church teachings and the works of contemporary theologians both within and outside the Catholic Church.La tarea de evangelización se vuelve aún más apremiante en nuestros tiempos debido a la necesidad de dar a conocer a Cristo hasta los confines de la tierra. Con los años, la desunión inhibió a los cristianos de trabajar juntos para lograr este objetivo. El Decreto sobre Ecumenismo del Concilio Vaticano II, Unitatis Redintegratio, marcó un punto de inflexión en la historia de la relación entre la Iglesia Católica y otras Iglesias y las Comunidades Eclesiales porque allanó el camino para un paso concreto, por parte de la Iglesia Católica, hacia la realización de la unidad cristiana. La teología ecuménica de Joseph Ratzinger a la luz del Decreto Unitatis Redintegratio es un estudio de su postura sobre cómo la autocomprensión de la Iglesia católica guía su relación con otras Iglesias y Comunidades Eclesiales. Ratzinger afirma que la unidad cristiana es posible y que es un proceso lento y minucioso. Ratzinger enfatiza que la verdadera unidad cristiana es la acción de la Trinidad. Este trabajo tiene tres capítulos. El Primer Capítulo traza el desarrollo del Decreto y presta atención a los temas planteados en él. Ayuda a situar el contexto de la teología ecuménica de Ratzinger. El Segundo Capítulo demuestra cómo la eclesiología de Ratzinger influye en su enfoque del ecumenismo. El tercer capítulo examina los temas principales de la teología ecuménica de Joseph Ratzinger a la luz de Unitatis Redintegratio, las enseñanzas de la Iglesia y las obras de los teólogos contemporáneos tanto dentro como fuera de la Iglesia católica

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

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