Profile Analysis via Multidimensional Scaling for the "Revised Two-Factor Learning Process Questionnaire".

The Revised Learning Process Questionnaire has been part of the development of a conceptual understanding of how students learn and what motivates them to engage in particular tasks. We obtained responses from 329 student volunteers at a mid-sized public university in the southeast United States. While looking at the psychometric properties of this questionnaire in a different educational context from which the instrument had been originally validated was one purpose of the study, the main thrust of this research was to use Profile Analysis via Multidimensional Scaling (PAMS) to improve the diagnostic functionality of the instrument as well as further explore the validity of the questionnaire. We found that interpreting the latent structure in terms of the dimensions of Strategy and Motive as opposed to the factors of Deep and Surface approaches to be more appropriate for diagnostic use. We also found that PAMS has the inherent ability to assess an individual’s fit within the model, thereby acting as a measure of self-report credibility. Both the Strategy and Motive dimensions were found to have ecological validity through analyzing its relationship to academic performance. Profile Analysis via Multidimensional Scaling for the Revised Two-Factor Learning Process Questionnaire Learning and study strategies have been an interest in higher education for decades. From the earliest studies done by Entwistle and Ramsden (1982), looking at the self-perceptions of learning and motivation in the academic environment, researchers have been interested in recording how students study. In recent years, instruments have been developed to record and analyze the phenomenon, to better understand how people study and what factors can be attributed to success or failure in the academic setting. Kember, Biggs, and Leung (2004) and Everson, Weinstein, and Laitusis (2000) are examples of those who have endeavored to evaluate student learning goals, and in addition suggest the realignment of many general education and core curricula across many higher education institutions to help better cater to learning styles and study strategies of college students. Although some research has concentrated on more economic concerns, such as retention of students who struggle academically (Kember & Leung, 2009), other studies have been focused on the development of instruments to delve more into the construct of learning and study strategies (Kaplan, 2008). The development of learning and studying as a construct has taken various paths. In the early 1970’s some of the earlier research was geared toward evaluating how students learn, and within what context does that learning occur. Marton and Säljö (1976a, 1976b), one of the first studies to attempt to describe levels of processing and examine qualitative differences in how students learn, has been the foundation for many studies attempting to develop instruments to measure these constructs. Some of these researchers see learning to be more related to the impetus or motivation of the learner, whether it be intrinsic or extrinsic (Grant & Dweck, 2001), while others recognize the motivational aspects but combine it with cognitive factors (Lufi, Parish-Plass, & Cohen,2003; Scraw, 1994; Tobias & Everson,1997) . Yet even combining these aspects, there are differing opinions of how they are manifested. For example, Weinstein, Palmer, and Schulte (2002) see the learning and study strategies to be a composite of constructs related to skill, will, and self-regulation, while Biggs, Kember, and Leung (2001), conceptualize learning as being composed of motive and strategy, which in either case is ether surface or deep depending on the complexity of the particular attribute. For this paper, however, our interest was not to debate the difference between theories, or to champion one specific explanation, but to move forward in the evolution of the instrument itself. That is, as the course of the history of researching learning and study strategies, numerous studies have been conducted in order to determine a more perfect understanding of the representation of these ideas from the data collected. In all cases research designs have been constructed and analysis undertaken to better able to researcher to understand not only how the construct manifests itself, but also how the instrument that assesses the construct operates. For example, Entwistle and Ramsden (1982) looked at the data qualitatively to help first identify the rudimentary questions to be utilized on a survey instrument, in the earliest development of the Learning Process Questionnaire. Later, Kember et al. (2004) ran a variety of exploratory factor analyses to determine the latent structure of the constructs, and later, confirmatory factor analyses to see how well the instrument represents that construct. More specifically, these instruments have been evaluated and validated. Utilizing factor analytic procedures helped refine the constructs that emerged from the data collected. For example, for the Learning Process Questionnaire (LPQ) the conceptualization of Deep Approach and Surface Approach were realized. Biggs et al. (2001) went further to demonstrate that motives and strategies are found within these learning approaches by the representation of the factor structure. In the analysis of the LPQ and its subsequent revisions, Kember et al. (2004) were able to clearly show the factors found in the instrument, and how those factors related to one another. It is now imperative that we take yet another step forward, and that is to show how to apply the instrument when describing samples of people. In short, the construct has been confirmed, the instrument’s ability to detect that construct has been confirmed, and it is now time to apply the instrument to help students. Factor analysis determines how the construct is manifested within the instrument (exploratory) and whether the instrument consistently demonstrates the existence of the factors for that construct (confirmatory), while PAMS looks at the instrument in terms of what it tells us about people. The current study was conducted with two major purposes. The first is to evaluate the psychometric properties of the Revised Two-Factor Learning Process Questionnaire, a later version of the LPQ (R-LPQ-2F; Kember et al., 2004). It is important to note that the sample used to confirm the two-factor version of the questionnaire was comprised of secondary students in Hong Kong, which required that the survey be translated into Chinese. As there may be some subtle issues in translation from English to Chinese, as well as cross-cultural differences in academic experiences, it is essential to determine if the Deep Approach and Surface Approach structure can be replicated with a more rural, American population. In addition, in order to use this survey for a sample of post-secondary students, it is important to replicate the findings with that age group. This included evaluating the ecological validity of the instrument by looking at relationships between the instrument factors and academic performance indicators such as GPA and student retention. In addition, this study engaged in the next level of progression in the analysis, by using Profile Analysis via Multidimensional Scaling (PAMS) in order to explore the characteristics of respondents of the R-LPQ-2F. As PAMS can be used to develop profiles for individual test takers, it can be used as a means for improving the diagnostic functionality of the instrument. The two-factor structure, identified by Kember et al. (2004), is indeed useful for describing the construct of the learning process and identifying how the instrument operates in terms of Deep Approach and Surface Approach. However, being able to use the instrument to interpret the data affords a richer utilization of the instrument, both diagnostically and prescriptively. For example, which students are more likely to be retained, maintain high GPA’s, or finish within 4 years? Describing student in terms of the dimensions of strategy and motive will allow us to identify “those with him motive low strategy” or “high strategy use, high motivation” and be better prepared to associate that with their academic successes or failures. PAMS is a method that can be used to explore individual profiles of test takers for assessments with multiple subtests. Profiles such as these can be instrumental in looking at strengths and weaknesses of test takers within the subtests as well as affording the ability to make an evaluation that can be used to remediate problem behaviors (Kim, Davison, & Frisby, 2007). Also, PAMS allows a researcher to identify students who develop in an idiographic manner or are not explained by the model for whatever reason, whereas this capability is not inherent in factor analysis. Method Participants We distributed a web-based version of the R-LPQ-2F to students at a mid-sized public university in the southeast United States. A total of 329 volunteers (227 women and 102 men) responded. Ages ranged from 17 years old to 64 years old (M = 27). Approximately 89.4% were white and 31.3% were graduate students. Since the R-LPQ-2F and its psychometric properties were originally produced by a large sample of secondary school students in Hong Kong, our sample is important in determining whether those psychometric properties are consistent in a university setting and across cultures. Materials The R-LPQ-2F consists of 22 items which can be summarized into two broad factors of Deep Approach and Surface Approach. Within Deep Approach, there are subscales for Deep Motive and Deep Strategy, and, within Surface Approach, there are subscales for Surface Motive and Surface Strategy. Further, these subscales contain two subcomponents each. Items were answered on a 5-point Likert-type scale ranging from 1 (this item is never or only rarely true of me) to 5 (this item is always or almost always true of me). Sta