Every type of research has to be critically evaluated. Patton (2001) states that reliability and validity are important parts of every qualitative research. Thus, the following sections will analyse the master’s thesis reliability, validity, and objectivity. 4.15.1 Reliability
The extent to which results are consistent over time and an accurate representation of the total population under study is referred to as reliability and if the results of a study can be reproduced under a similar methodology, then the research instrument is considered to be reliable.
Reliability describes if a study conducted under the same circumstances would give the same results. According to Yin (2008), the goal of reliability is to minimize errors and keep the study unbiased. Even though the term reliability is commonly used to test or evaluate quantitative research, it is most often likewise used for qualitative methods (Golafshani, 2003). Some authors are more specific about the term reliability and use ‘dependability’ as the corresponding term for qualitative research (Lincoln & Guba, 1985). When referring to the reliability of an interview study, it refers to the degree of consistency of the executed interviews (Kvale, 1996). Cronbach’s alpha, α (or coefficient alpha), developed by Lee Cronbach in 1951, was used to measures reliability. Cronbach’s alpha tests to see if multiple-question Likert scale surveys are reliable.
These questions measure latent variables — hidden or unobservable variables like a person’s conscientiousness, neurosis or openness. These are very difficult to measure in real life. Cronbach’s alpha will tell if the test designed is accurately measuring the variable of interest. Cronbach’s alpha was computed by correlating the score for each scale item with the total score for each observation (usually individual survey respondents) and then comparing that to the variance for all individual item scores. The minimum acceptable value for Cronbach's alpha ca 0.70; below this value the internal consistency of the common range is low. Meanwhile, the maximum expected value is 0.90; above this value is perceived as redundancy or duplication. Cronbach's alpha coefficient is both an inherent property of the response pattern of the population studied, without a characteristic ladder itself; it is feasible, the alpha value changes depending on the population in the scale was applied.
4.15.2 Validity
According to Bryman and Bell (2007), there are two types of validity in qualitative research: internal validity and external validity. Internal validity refers to the match between the researchers’ observations and the theoretical ideas they propose. External validity describes how findings can be generalized for other cases (Bryman & Bell, 2007). Although some qualitative researchers have argued that the term validity is not applicable to qualitative research, but at the same time, they have realised the need for some kind of qualifying check or measure for their research. For example, Creswell & Miller (2000) suggest that validity is affected by the researcher’s perception of validity in the study and his/her choice of paradigm assumption.
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As a result, many researchers have developed their own concepts of validity and have often generated or adopted what they consider to be more appropriate terms, such as, quality, rigor, and trustworthiness (Davies & Dodd, 2002; Lincoln & Guba, 1985; Mishler, 2000; Seale, 1999; Stenbacka, 2001). Wainer and Braun (1998) describe the validity in quantitative research as “construct validity”.
In this study, the construct was the initial concept, notion, question or hypothesis that determined which data was to be gathered and how it was to be gathered. They also assert that quantitative researchers actively cause or affect the interplay between construct and data in order to validate their investigation, usually by the application of a test or other process. In this sense, the involvement of the researchers (preliminary investigation) in the research process was used to increase the validity of a test. 4.15.3. Cronbach’s reliability analysis
According to SPSS – Cronbach's Alpha is not a statistical test and is technically a coefficient of reliability (or consistency). Alpha therefore measures internal consistency and indicates how closely related a set of items are grouped. SPSS results further indicate that a high value of Alpha is often used along with substantive arguments and possibly other statistical measures as evidence that the items measure an underlying (or latent) construct (Giliem, 2003). In summary Cronbach's Alpha can be written as a function of the number of test items and the average inter-correlation among the items.
SPSS further highlighted that when the number of items increases so will also Cronbach's Alpha increase. Also when the average inter-item correlation is low, Alpha will be low and that when the average inter-item correlation increases Cronbach's Alpha increases holding the number of items constant. Of importance is that Cronbach’s Alpha reliability coefficient normally ranges between 0 and 1. The closer Cronbach’s Alpha coefficient is to 1.0 the greater the internal consistency of the items in the scale, (Cerny & Kaiser 1997). As a rule of thumb for Cronbach’s Alpha as provided by George and Mallery (2003) and cited by Gliem, J.A, and Giliem, R.R (2003) - Midwest Research to Practice Conference for Likert-Type Scales is when Cronbach’s Alpha is regarded as:
> .9 – Excellent, > .8 – Good, > .7 – Acceptable, > .6 – Questionable, > .5 – Poor, and < .5 – Unacceptable”.
In summary Cronbach's Alpha is used to rate the internal consistency (homogeneity) or the correlation of the items in a test. It is agreed by experts that test showing a strong internal consistency measurement should show only moderate correlation among items (.70 to 0.90). However, the following guidelines exist (1) when correlations between items are too low, then it is likely that they are measuring different traits and therefore should not all be included in a test, (2) If item correlation are too high, it is likely that some items are redundant and should be removed from the test, (Giliem,2003).
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4.16. Chapter Summary
This chapter outlined the research methods that were adopted for the collection of relevant data required. This Chapter addressed the research methodology used for gathering and analysis of data. The use of specific research methodology and design as well as the design and structure of the questionnaire and the attributes of the target sample was explained. The utilisation of item and factor analysis for data interpretation was also discussed. It described the research approach, data collection instruments, sampling design and goal achievement matrix of the study required for the achievement of the research objectives outlined in chapter one. The chapter that follows discusses the literature review pertaining to this study. This chapter presented and justified the selection of the research methods used in this study. The qualitative methodology was used in this study. A description of the research sites and an explanation of the participants used in this study was given. The next chapter presents the finding.
In Chapter 3 the literature of the research process was discussed whilst Chapter 4 described the application and interpretation of the data analysis as applied in the research. Chapter 5 further will display an in-depth analysis and coverage of the item and factor analysis performed.
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CHAPTER FIVE FINDINGS AND ANALYSIS
5.1. Introduction
This chapter presents the results of the research findings relevant to the problem. The key research questions raised in Chapter 1 shall be guiding the analysis and presentation of data. This chapter presents the findings from the investigation into the Intelligent Transport System capabilities and Business agility within the Bus Rapid Transit system. The research findings in this chapter emanate from questionnaires to selected stakeholders of the BRT. The findings presented below to elucidate the views of the different stakeholders as well as the social and economic effect that Intelligent Transport System has had on the BRT. There were a hundred participants in the first segment of the data collection and as previously mentioned these participants were required to complete the questionnaire related to the BRT (see Appendix A). Ninety- eight completed questionnaires were returned completed.