Data Analysis

This current research commenced with the premise that the implementation of learning statistics policies, in the selected academic institutions, had failed. The study was aimed at analysing the reasons for this failure, as well as introducing reforms in the statistics learning process. Before engaging with this task, it became necessary to search for a theoretical framework that could guide the investigation, analysis and interpretation of the findings (Creswell, 2014). This was decided by the following questions: “What are the differences in the misconceptions of how to select a statistics test?” “What

constitutes the failure of the learning statistics?” “How is this research conducted?” as well as “How is the relationship between students‟ SELS beliefs and their independent factors, determined?” Therefore, the Explorative design was selected as a theoretical framework to guide the investigation, analysis and interpretation of the findings.

Explorative design is divided into two modules, namely, the descriptive research approach, which examines distributions and relationships among different distributions, and the interpretive or explanatory approach. Interpretations move beyond the explicit descriptions provided by the individual participants, drawing on the researchers‟

interpretation, and evidently, interpretations that were more abstract, were related to the data provided by the study participants (Ormston, Spencer, Barnard & Snape, 2003).

4.7.5.1. Descriptive research and relationships

Postgraduate programmes in the academic environment offer complete assistance that endorse learning. These programmes offer structure, information, activities (workshops, conferences), practice, as well as feedback, and are organised in such a manner that postgraduate students can study, even if they do not have the benefit of a mentor or classmates (peers students). In addition, the development team comprises postgraduate students, faculty content experts, human-computer interaction experts, and statistics monitors, which enable this framework to utilise the best multidisciplinary knowledge to design effective learning (Naidu, 2003).

Garfield and Ahlgren (1988) highlight the need for collaborative research on how students accurately comprehend probability and statistics. This current study aims to examine the empirical results that characterize different circumstances of choosing the right statistical test, to explain and predict observed data.

Therefore, principles and practices, to describe the variation in learning statistics among postgraduate students, should be developed. A reasonable length of the narrative, allows good coverage of many of the issues. In addition, the researcher should write succinctly, ensure that the narrative reads well, should not become bogged down in any one issue, and communicate meticulously, the range of skills, needed in learning statistics (Creswell & Clark, 2007).

4.7.5.2. Interpretive approach

The explanation assessment tool considers the views of postgraduate students in the analysis of their knowledge about using statistics skills to choose the right statistics test. It appears that the increase of participation during seminars, presentations, as well as the writing-up of theses by postgraduate students confirms the declaration of various authors that postgraduate students are the only experts on their life experiences and priorities (Petre & Rugg, 2010; Lee, 2011).

In addition, the explanation assessment tool is usually open-ended questions, and interactive in design; therefore, they facilitate the exploration of issues, as well as shared learning between postgraduate students at different universities (Laurillard, 2013). Consequently, it appears that participation in seminars/presentations, empowers weaker students to define learning on their own terms, based on their perceptions and understanding of the phenomenon (Earl, 2012).

Obviously, when faced with the question of how students chose the right statistical procedures, it is not easy to understand human attitudes and behaviours, unless the relevant meanings are understood (Skinner, 1953; Schank & Abelson, 2013). These attitudes or behaviours include, reason, intentions, beliefs or emotions. The students explain connotations in diverse ways, using logical progression, the characteristics of individuals, as well as the structural links between concepts and knowledge or performance (Keller, 2009). Students act on their beliefs and preferences; however, many scholars protest that such clarifications absence the power of general applicability, as beliefs and preferences are impossible to corroborate (Bevir & Rhodes, 2002). For example, researchers pursue to avoid beliefs, by relating statistical procedures with objective evidences, to build the rationality of student attitudes; however, rational students tend to raise interest when performance increases (Scott & Davis, 2015).

The interpretive approach cannot separate student opinions and inclinations from objective evidences, such as the socio-characteristics, including ethnic group, post-graduate programme, gender, marital status, student status, and type of study.

This impossibility of pure experiences implies that they cannot reduce beliefs, interests, expectations, motivations and partialities to mere overriding variables (Ormston, Spencer, Barnard & Snape, 2014).

The various interpretive approaches are subjective, rationality and relativism (Bernstein, 2011). However, a true and full understanding of another student‟s thoughts is possible only when its affective aspect is understood (Laurillard, 2013). To set the tone on the core of this matter, observations made by post-graduate students are insightful, and supply valuable material for stimulating reflections on learning (Kerr, 2005). The close relationships with their fellow students enable them to have more access to their thoughts and feelings, than is usually possible for someone learning statistics at University (Brophy, 2013).

The exploration of the students‟ ability facilitates the understanding of difficulties that emerge during their learning process (Meyer & Land, 2013). In support of this view, Lee (2011) proposes that an important function of this methodology is the empowerment of the supervisors with the primacy of natural knowledge being asserted over externally determined measurement criteria. This technique emphasizes the ability of weak postgraduate students to understand and analyse their own reality, while the supervisors define or determine criteria for identifying the slow, or inefficient, student usually during presentations, seminars and the final write-up.

The interpretive approach attempts to measure (understand) the self-efficacy learning in individual characteristics, behaviours, as well as the social environment of a specific university (Shea & Bidjerano, 2010). Consequently, this assessment identifies weak students based on a supervisor‟s own criteria (definition and perceptions of self-efficacy learning). Deane, Samuels & Williams (2009) acknowledge that the application of statistical procedures needs to be addressed in the formulation of strategies, which aim to improve self-efficacy learning statistics, in university policy. Chen (2007), Meyer (2009) and Hassen (2013) claim that, based on their experiences, postgraduate students display multiple dimensions of positive/negative anxiety and attitudes, as the values and priorities of these weak postgraduate students. Some students argue that self-efficacy in learning statistics is not extremely effective in assessing the application of statistical procedures in academic research (Rust, O‟Donovan &

Price, 2005; Colthart et al., 2008). Shute (2008), Papastergiou (2010), as well as Biggs and Tang (2011) mention that the ability of postgraduate students to

acquire self-efficacy is easy, compared with solving and learning tasks, while it was generally well accepted by the target population.

This analysis encounters diverse perceptions that sometimes adhere, and other times diverge; therefore, the objective is to leave space for additional negotiation and rational on these issues. However, the intention is not to reach conclusion, and settle any of the issues, but to open up threads for on-going discussions (Luitel & Taylor, 2007).