This article discusses some procedural issues related to the mixed-methods sequential explanatory design, which implies collecting and analyzing quantitative and then qualitative data in two consecutive phases within one study. Such issues include deciding on the priority or weight given to the quantitative and qualitative data collection and analysis in the study, the sequence of the data collection and analysis, and the stage/stages in the research process at which the quantitative and qualitative data are connected and the results are integrated. The article provides a methodological overview of priority, implementation, and mixing in the sequential explanatory design and offers some practical guidance in addressing those issues. It also outlines the steps for graphically representing the procedures in a mixed-methods study. A mixed-methods
Comment [CT1]:This describes the type of MM design that the study employs.
Comment [CT2]:Both QUAN and QUAL data are gathered and analyzed in this study.
Comment [CT3]:This MM study involves the integration of both QUAN and QUAL results.
sequential explanatory study of doctoral students' persistence in a distance-learning program in educational leadership is used to illustrate the methodological discussion.Keywords: mixed methods; quantitative; qualitative; design; survey; case studyIn recent years, more social and health sciences researchers have been using mixed-methods designs for their studies. By definition, mixed methods is a procedure for collecting, analyzing, and "mixing" or integrating both quantitative and qualitative data at some stage of the research process within a single study for the purpose of gaining a better understanding of the research problem (Tashakkori and Teddlie 2003;Creswell 2005). The rationale for mixing both kinds of data within this one study is grounded in the fact that neither quantitative nor qualitative methods are sufficient, by themselves, to capture the trends and details of a situation. When used in combination, quantitative and qualitative methods complement each other and allow for a more robust analysis, taking advantage of the strengths of each (Green, Caracelli, and Graham 1989;Miles and Huberman 1994;Green and Caracelli 1997; Tashakkori and Teddlie 1998).There are about forty mixed-methods research designs reported in the literature (Tashakkori and Teddlie 2003). Creswell et al. (2003) identified the six most often used designs, which include three concurrent and three sequential designs. One of those designs, the mixed-methods sequential explanatory design, is highly popular among researchers and implies collecting and analyzing first quantitative and then qualitative data in two consecutive phases within one study. Its characteristics are well described in the literature (Tashakkori and Teddlie 1998; Creswell 2003Creswell , 2005 Creswell et al. 2003),
Comment [CT4]:The survey is a QUAN research design, while the case study is often classified as a QUAL research design.
Comment [CT5]:This is the definition of MM research.
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Ten objects were used to assess comprehension, production, and imitation of comparative and superlative suffixes in 100 children ranging in age from 2; 6 to 4;6. The results indicated that comprehension of both suffix forms was similar at each of five age levels studied; although incrementally better comprehension scores occurred with succeeding ages. Virtually all subjects were successful in imitating forms missed during the comprehension task. Analysis revealed that the -er suffix was produced more often than the -est suffix, at the youngest age (256): at succeeding age levels this difference was reversed. Big was the primary lexical unit selected by the subjects for positive and negative polar forms connoting size; little was the second most frequently used adjective form.
The wide variability in DIO characteristics found in this study supports the premise that the DIO role is underdeveloped. The authors established the reliability and validity of the DIORS, which now may be used in future research of the DIO role. The data from this study may be used by DIOs and institutions to develop the role further, improve DIO performance, and create more useful job descriptions.
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