ADMINISTERING THE DATA COLLECTION

In administering the data collection, we would be looking at the procedures for gathering or collating the data in the proposed research. For instance, in the qualitative research, much emphasis is placed on reviewing and anticipating the types of issues that may likely arise “in the field” that will yield less-than-adequate data. These issues include such things as the time to recruit participants, the researcher’s role in observing, the effectiveness of the performance of the recording equipment, the time to locate documents and the details of proper placements of videotaping equipment. All these concerns need to be addressed before the interview takes place (Creswell, 1998).

In addition to these issues mentioned above, the researcher also needs to be careful of how to enter the site of the study so as not to disrupt the flow of activities going on at the time. This will entail the ethical issues, such as providing reciprocity to participants for their willingness to provide data, handling of respondents’ sensitive information and disclosing the purpose of the research. All these are applicable to both the qualitative and quantitative researches (Creswell, 1998).

Considering the above ethical issues, one cannot be expected to be so rigid and tailored along these expectations to the fullest; there is a need for as little variation as possible to avoid biasness being introduced into the process. Standard procedures should be paramount for collecting data on instruments, checklists, and from public documents, and if more than one investigator is involved in data collection, training should be provided so the procedure is administered in a standard way each time data is needed (Creswell, 1998).

100 The researcher of this study conducted the interviews and administered the questionnaires personally.

3.8 DATA ANALYSIS

Due to the fact that this proposed study would be employing the mixed method approach to research, we shall be considering the ways both qualitative and quantitative data are been analysed.

Although, there is no consensus regarding the analysis of qualitative data (Creswell, 1997), however, there are a few guidelines for the researcher. Analysing qualitative data can sometimes be a complicated business, and is always a time-consuming one. Qualitative data analysis is “custom built” as qualitative researchers “learn by doing”. It is a non-linear process that involves organising, analysing, interpreting and describing the mass of collected data, and already occurs during the data collection process (De Vos, 2002; Creswell, 2003). The aim of analysis is to treat the evidence fairly, producing compelling analytical conclusions (Yin, 1998).

Creswell (2008) describes the features of qualitative data analysis as follows:

 It is inductive in form, going from the particular to the detailed data to the general codes and themes. This allows the researcher to produce broad themes and categories from various databases. This means Qualitative data analysts seek to describe their textual data in ways that capture the setting or people who produced this text on their own rather than in terms of predefined measures and hypotheses. The analyst identifies important categories in the data, as well as patterns and relationships, through a process of discovery.

 It involves a simultaneous process of analysing while also collecting data.  The phases of data analysis are iterative. The researcher cycles back and

forth between collection and analysis, more of a dynamic nature. Next to his field notes or interview transcripts, the qualitative analyst jots down ideas about the meaning of the text and how it might relate to other issues. This process of reading through the data and interpreting them continues throughout the project. The analyst adjusts the data collection process itself

101 when it begins to appear that additional concepts need to be investigated or new relationships explored. This process is termed progressive focusing (Parlett and Hamilton 1976).

 It involves reading the data several times and conducting an analysis each time, developing a deeper understanding for the information.

 There is no single accepted approach to data analysis.

 The researcher brings his/her own perception to the data analysis process, making it an interpretive process.

Going by the above features, the researcher will be applying Creswell’s (2008) approach to data analysis to organise and interpret the information in a meaningful way. The phases included in the data analysis process include: organising the data and preparing it for analysis; reading through the data and making notes; analysing the data; identifying and describing themes, subthemes and representing the themes. The figure below represents the continuous process of qualitative data analysis throughout this research process.

Figure 3.3: The Data Analysis Process (adapted from Creswell, 2008) Iterative

Codes the text for description to be used in the research report

Codes the text for themes to be used in the research report

Researcher codes the data (locates test segments and assigns a code label to them)

Researcher reads through the data (obtains a general sense of the material)

Simultaneous

Researcher prepares data for analysis (transcribes field notes and recorded conversations)

Researcher collects data during research process

102 In qualitative data analysis behavioural pattern of participants in a research are assigned numeric identifiers known as behavioural coding, transforming these qualitative behaviours into quantitative data that can then be subject to statistical analysis for precision. However, applying behavioural coding to one’s observations is extremely time consuming and expensive. In addition, only highly trained researchers are qualified to encode behaviour, hence the approach is cost prohibitive (Source: www.uxmatters.com).

In the use of qualitative data collection instrument (that is Observations, surveys and Audio or video recordings), after a careful observation by the researcher to determine the participants for the study, these participants will then be given survey questions that will involve attempting to answer the sections outlined in the questionnaire attached at the beginning of this study (that is Section 1 – 3) regarding:

 Participants’ bio data, interests or hobbies, intentions to study and part-time work

 The motives of participants for entering higher education for a Pre-degree programme, and possibly to determine who/what influenced their decision to attend this programme.

 Lastly, to address the preparedness of students for higher education after their pre-degree programme.

After this, the researcher will engage the participants in a one-on-one interview, which will be recorded in writing by the respondents and verbatim transcription will later be done by the researcher. This interview will focus on the other sections (that is, Sections 4 & 5) in the interview schedule regarding,

 Students’ attitude towards mathematics (focus on bridging mathematics) at the Pre-degree level, and possible reasons for their dismal performance in this module.

 Students’ expectations of the bridging mathematics module, its operations at present, how confident they are about their studies here at the Midrand Graduate Institute (MGI) and how important it is for them to do well in their studies.

103 Quantitative data analysis on the other hand helps to draw meaningful results from a large body of qualitative data. The main beneficial aspect is that it provides the means to separate the large number of confounding factors that often obscure the main qualitative findings (Abeyasekera, Lawson-McDowall and Wilson, 2000).

Furthermore, quantitative data analysis allows the reporting of summary results in numerical terms to be given with a specified degree of confidence. The features of quantitative data analysis include the following:

 The researcher uses mathematics and statistical models as the methodology of data analysis.

 Data collection is typically numeric so that it can be quantified and subjected to statistical testing, which brings about reliability and validity in the outcomes.

 Researcher uses the inquiry method to ensure alignment with statistical data collection methodology.

 It involves statistical significance testing of hypotheses and establishing a theory or fact

 Quantitative data are socially constructed as those of the qualitative ones.  It makes use of a linear model of relationship among the variables, which is

fitted into the data, this leads to statistical summaries (such as means or explained variances) being obtained, and these are tested against the probability that values as high as those obtained could have occurred by chance.

The instrument that will be used for gathering data for statistical testing will be a questionnaire, prepared by the researcher using a 5-point Likert scale.

The questionnaire will focus mainly on the anxiety and motivation aspect of mathematics (bridging mathematics) using the self-report instrument known as Mathematics Anxiety Rating Scale (MARS), as originally designed by Richardson and Suinn (1972), which was a 98-item self-rating scale. The instrument was once considered as the best available measure of mathematics anxiety with the highest validity and reliability. The contemporary view (Hopko, Lejuez, Ashcraft, Eifert and

104 Riel, 2003; Bai, 2010) seems to be that MARS has two major shortcomings: i) it takes too long to administer and to score; and ii) it was developed with one- dimensional representation of negative affects towards mathematics.

To this end, in order to overcome the above-mentioned shortcomings researchers started developing several, multidimensional versions of MARS. For example, Betz’s (1978) Mathematics Anxiety Scale (MAS), adapted from Fennema and Sherman’s (1976) Mathematics Attitude Scale, was set up as a 10-items bi-dimensional instrument, also Bai (2010) Mathematics Anxiety Testing Scale which was a 14-item meant to capture also a bi-dimensional affective scale of measuring mathematics anxiety with high psychometric quality.

From the above statements, it has become imperative to adopt the Bai’s 14-item Mathematics anxiety testing questionnaire with a 5-point Likert scale in order to capture the essence of the affective nature of students’ anxiety on their mathematics performances.

There will be another questionnaire designed to test students’ motivation and learning strategies towards mathematics. This questionnaire is adapted from the Motivational Strategies for Learning Questionnaire (MSLQ), it includes only three original factors (value, expectancy and affect) of the mathematics motivation scale, although 36 items were originally included in this scale. However, for the scope of this study we shall only be considering the value and affect factors. For the value factors, we shall be considering the angle of intrinsic goal (6 items), extrinsic goal (6 items), and task value (6 items). In addition, the affect factor will have a new item added in the questionnaire to test for the effectiveness of teaching strategies (6 Items) on motivation for learning and passing mathematics by students in addition to the Test Anxiety (7 items).

To test for validity and reliability using the MSLQ we shall be using inferential statistics, which includes, the practical mean and variance, the student t-test to analyse the difference between the different factor groups of motivation and with a Motivation for Academic Preference Scale (MAPS of α > 0.80). This inferential statistical testing will also be applied to the anxiety scale test of Bai (2010). The

105 Dependability

sequence of the presentation of the results will be in accordance with that of the hypotheses. In this study, three null hypotheses were tested for significance level at 0.05 margin error.