Chapter 7: Summary, Conclusion and Recommendations
4.7 Data Collection Instruments and Procedures
4.7.4 Pretesting of Research Instruments
According to Babbie and Mouton (2001), pre-testing of research instruments before administering them is a pre-requisite to data collection process. The reason for this is that it is important that questionnaire items are clear, concise and unambiguous (Williams 2003), so that all respondents can read meaning into it the same way. Consequently, it was necessary to pre- test the two structured questionnaires for undergraduate students and academics respectively, as well as the semi-structured interview schedule. Face validity and the pre-test of data collection instruments was done to ensure the content validity, that is, validity of questions and the reliability of the data obtained. This also helped to confirm the clarity of questions, identify unclear and ambiguous questions, remove difficult questions, determine if relevant questions were included and gather remarks and comments from the respondents (Saunders, Lewis and Thornhill 2009).
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Babbie and Mouton (2001) recommended that questionnaire be pre-tested on ten people who are found to be appropriate to answer the questions. Sheatsley (1983) also suggested that between 12 to 25 cases or people are sufficient to reveal the major difficulties and weaknesses in a pre-test questionnaire. The questionnaire was pre-tested on 25 respondents comprising of 16 undergraduates and 8 academics. The pre-test sample units were conveniently selected from the accessible population of students and academics at the Faculty of Science, Obafemi Awolowo University (OAU) Nigeria, since they have similar characteristics with the study population. OAU is one of the Federal Universities in the southwest region of Nigeria and also among the best ten Nigerian universities based on Webometric ranking (Cybermetrics 2014).
This number is considered sufficient to provide relevant responses or answers to validate the questionnaire content. Moreover, pre-testing with experts is a way of ensuring content validity of instruments (Straub, Boudreau and Gefen 2004). As argued by Saunders, Lewis and Thornhill (2009) and Bernard (2012), the pre-test of the questionnaire helps in refining it in order to avoid difficulties in answering the questions. The results of the pre-test were useful in refining the questionnaire items and structure to collect relevant and reliable data.
The questionnaire items were subjected to Cronbach Alpha (α) test. Cronbach‟s Alpha (α) was used to test the reliability, internal consistency and the overall reliability of each of the variables of the study. Cronbach‟s alpha is a function of the average inter-correlations of items and the number of items in the scale (Kimberlin and Winterstein 2008). Only constructs with α=>0.7 were retained, questionnaire items with α=<0.7 for all items were re-formulated. The reliability of each of the variables as measured by Cronbach‟s Alpha was relatively high with 0.95 for system quality, 0.87 for information quality, 0.81 for service quality, 0.95 for media synchronicity, 0.87 for attitude towards use , 0.92 intention to use and 0.93for net benefits.
For the pre-test of interview schedule, it is suggested that studies may utilize as few as 2 to 5 cases or people, depending on the study goals and resources (Babyak, Grower, Mulvihill and Zaroski 2000). The semi-structured interview schedule designed for collecting qualitative data was pre-tested on two faculty heads and three librarians from OAU. These pre-test sample units were conveniently selected from the accessible faculty heads and librarians at OAU, as they have related characteristics with the study population. Corrections noted were effected on the instrument before it was administered.
99 4.8 Data Analysis
Data analysis consists of a number of interconnected processes that help to summarize gathered data and also to organize them in such a manner that provide responses to the research questions (Kothari 2004). Due to the nature of the data collected in this study and in line with the post-positivist paradigm; a mixed method data analysis was required. This involved a combination of qualitative and quantitative data analysis method.
Descriptive statistics and Statistical Package for Social Science (SPSS) version 17 were used to analyse the quantitative data collected through the structured questionnaire, since it allowed for easy manipulation of statistical data analysis and interpretation of quantitative study findings (Babbie and Mouton 2001, and Peugh and Craig 2005). Before analysing the raw data, each completed questionnaire was evaluated to check for missing data, ambiguity and errors. After which the questionnaire responses were coded and keyed into the computer using the SPSS software. Regression analysis was employed in evaluating the relationships that existed between the variables of the study and also the predictive abilities of the study variables, as shown in Table 1. Research hypotheses (see section 1.2.3) were tested at p= 0.05 level of significance to determine if the relationships between the study variables were significant or not. The results generated from the quantitative data analysis were presented visually using frequency counts, tables and charts.
Qualitative data gathered through the use of semi-structured interview schedule was analysed through thematic content analysis. Semi-structured interviews allow for thematic analysis of the qualitative data (Alvarez and Urla 2002, cited in Anil and Charatdao 2012). It also involves gathering and analysing the content of the text in order to make sense out of them (Newman 2006). This helps to reduce data and makes interpretation easier. The recorded interviews were transcribed after which the transcripts and notes were prepared. Responses of participants were summarised to significant statements while the data were coded. This process is consistent with Kerlinger‟s (1973) definition of analysis as the categorizing, ordering, manipulating and summarizing of data to obtain answers to research questions. Finally, themes relevant to the research objectives were developed and the responses of participants were combined and grouped under relevant themes.
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Table 4 shows the relationship that exist between the study research questions, approach for data collection, sources of data and methods of data analysis.
Table 4: Research Questions, Sources of Data and Data Analysis
S/N Research questions Approach Source of Data Method of Data Analysis
1 What kinds of Web 2.0 technologies are used by academics and students, and for what purposes?
Quantitative and
Qualitative
Survey
questionnaire, interview, literature review
Descriptive statistics, Thematic content analysis
2 To what extent are Web 2.0 technologies integrated into TAL in Nigerian
universities?
Quantitative and Qualitative
Survey
questionnaire, interview, literature review
Descriptive statistics and Thematic content analysis
3 How does system quality, information quality and service quality influence attitude towards the use of Web 2.0 technologies for TAL in the federal universities?
Quantitative Survey
questionnaire and literature review
Descriptive statistics and Multiple
Regression Analysis
4 How does attitude towards use influence intention to use Web 2.0 technologies for TAL in the federal universities?
Quantitative Survey
questionnaire, interview and literature review
Descriptive statistics, Regression Analysis and Thematic content analysis
5 How does media synchronicity influence intention to use Web 2.0 technologies for TAL in the federal universities?
Quantitative Survey
questionnaire and literature review
Descriptive statistics and Regression Analysis 6 What net benefits can be
derived from the use of Web 2.0 technologies for TAL?
Quantitative and
Qualitative
Survey
questionnaire, interview and literature review
Descriptive statistics, Regression Analysis and Thematic content analysis