132 What are the
perceptions of fourth- year psychology students towards information literacy?
-Questions 2,12 in Appendix X- questionnaire for students -Questions 2,16 in Appendix XI – interview schedule for librarians
-Questions 2,17 in Appendix XII – interview schedule for lecturers
-Survey questionnaire - interview
-Content analysis -Factor analysis using SPSS -Discourse analysis
What are the challenges experienced by fourth-year
psychology students in learning
information literacy?
-Questions 9,10 in Appendix XI – interview schedule for librarians
-Question 12 in Appendix XII – interview schedule for lecturers
-Survey questionnaire - interview
-Content analysis
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Mugenda (2003:95) defined reliability as a measure of the degree to which a research instrument yields consistent results or data, after repeated trials. In research, the ability of the instruments to minimise or allow more errors affects the quality of data collected and consequently the results and their interpretation. In other words, reliability deals with the stability of research instruments to ensure that data collected from the same or similar source at different times, using the same instruments and in the same conditions, will yield the same results (Easterby-Smith, Thorpe & Lowe 2002:135). Although Sheppard (2004:242) posited that reliability and validity were strongly related to quantitative research, Johnson and Christensen (2008:275) stated that both quantitative and qualitative research desired valid and reliable results. However, Rubin and Babbie (2008:181) noted that reliability does not guarantee accuracy.
Furthermore, reliability of the instruments in this study was achieved by testing the instruments in order to minimize errors in their construction (Babbie & Mouton 2001:244). Pre-test results were subjected to Cronbach’s Alpha coefficient measurement and calculated using SPSS to test for internal consistency. From the pilot study, a reliability analysis was carried out for the Likert scale items in the student questionnaire. The regression and correlation test showed a Cronbach's Alpha coefficient of 0.843, well above the threshold of 0.67 recommended by Mugenda and Mugenda (2003) and 0.72 by Yin (2013). From the results of the test it can be concluded that the questionnaire used for this study is a reliable data collection tool. Table 4.5 shows the results of the analysis for the questionnaire.
Table 4.5: Reliability Statistics Cronbach's
Alpha
Cronbach's Alpha Based on
Standardized Items N of Items
0.832 0.848 33
Rubin and Babbie (2008:184) and Mugenda and Mugenda (2003:96) observed that the Alpha coefficient calculation was among the common and reliable ways to measure reliability of instruments used in research. The value of the Alpha coefficient ranges from 0 to 1 and is used to describe the reliability test. Usually, a measurement level of 0.8 or above is considered very good, while a measurement level of less than 0.7 requires that the instruments be modified and
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a re-test done until the expected level is attained. Pre-testing was done at the Africa International University. The Africa International University was chosen for pre-testing because it is not among the universities where the actual study was conducted and subjects have similar characteristics to the subjects for this study. Mugenda and Mugenda (2003:186) recommended pre-testing of instruments as a way of ensuring that items in the instruments are clearly stated, understood and elicit the same responses from different respondents. After pre- testing, the researcher modified interview questions in the interview schedules to remove ambiguities and errors in the instrument that could impede quality data collection during the study (Babbie, 2007:257).
4.9.2 Validity
Validity is described as the extent to which the results from data analysed in a study accurately represent the concept under consideration (Mugenda and Mugenda, 2003: 99; Babbie 2007:146). Validity therefore “estimates how accurately the data obtained in the study represents a given variable or construct in the study” (Mugenda, 2008:256). Leedy and Ormond (2005:280) posited that validity assesses the accuracy of whether measurements for an attribute collected are really what was supposed to be measured. Validity therefore concerns itself with the quality of research, showing how well the ideas correspond with actual reality (Neuman, 2006:188; Mugenda & Mugenda, 1999). Johnson and Christensen (2008:275) observed that minimising bias was a sure way to achieve high validity in research.
A closer examination of validity threats in quantitative and qualitative research revealed three key types of validity, including internal, external and construct validity (Easterby-Smith, Thorpe & Lowe, 2002:53). According to Leedy and Ormond (2005:97), internal validity is concerned with the extent to which extraneous variables are controlled to eliminate bias and thereby increase researcher confidence in the findings. Threats to internal validity include history, instrumentation, testing, statistical regression, selection, mortality and imitation of treatments (Tredoux & Smith, 175-177; Teddlie & Tashakkori 2009:299). External validity is concerned with generalizability of one study’s findings to a wider context (Leedy & Ormond, 2005:97; Johnson & Christensen, 2008:267). Mugenda and Mugenda (2003:99-104) explain that external validity shows how findings in a representative sample relate to the target population and tests the extent to which similar results can be obtained at other times with
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different settings. Construct validity is concerned with the accuracy of instruments used in data collection and how well results measured fit the theories underpinning the study (Cohen, Manion & Morrison, 2007:138). To address threats to construct validity, Johnson and Christensen (2008:272) and Neuman (2006:194) suggested that a study needs to clearly spell out definitions for constructs of the theories adopted to avoid any ambiguities in understanding.
The significance of validity in research is that accurate data will lead to accurate interpretation of the phenomenon under study. In the present study, validity was ensured through methodological triangulation for collection and analysis of data. The use of both qualitative and quantitative approaches ensured that the appropriate data required for the study were collected.
Further validity was ascertained through careful development of questionnaires and interview guides (Easterby-Smith, Thorpe and Lowe 2002:86). These instruments were pre-tested to ensure that the questions and interview guides were clear and well understood. Any ambiguities resulted in re-wording or reconstruction of the instruments. Validity was further achieved by ensuring careful sampling and use of appropriate statistical measurements (Tedlie &
Tashakkori, 2009:178-178). Clear definitions and discussions of key constructs of the Seven Faces of Information Literacy as the theory underpinning this study were provided to address the danger posed by threats from construct validity (Christensen, 2008:272; Neuman, 2006:194).