According to Watling as cited in Winter (2000), “validity and reliability are tools of an essentially positivist epistemology”. Reliability is the extent to which the same measurement technique or strategy produces the same result on different occasions, for example when used by different researchers (Hammersley, 2008:43). It is concerned with the stability and consistency of measurement (Cohen, Manion & Morrison, 2007:146). Validity is the extent to which the research findings accurately represent what is really happening in the situation (Leedy &
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Ormrod, 2005:92). It tests how well an instrument measures the particular concept it is intended to measure (Neuman, 2006:188). Generally, validity and reliability address issues concerning the quality of the data and appropriateness of the methods used in conducting research. The quality of the data and the appropriateness of the methods employed are particularly important in social sciences, because of the different philosophical and methodological approaches to the study of human activity (Cano, 2003). Validity and reliability are of primary concern for data quality control measures in research, since the quality of a study depends to a large extent on the accuracy of the data collection procedures (Mugenda & Mugenda, 2003:95). Thus, a study is considered to be valid when the conclusions are true or correct (McBurney & White, 2010:173), and reliable when the findings are repeatable (Bryman, 2004:28).
4.8.1 Validity in quantitative and qualitative research
Qualitative research views validity in a different way to quantitative research. Quantitative research tends to focus more on minimising threats to validity, because the objective of the study must be representative of what the researcher is investigating (Welman, Kruger & Mitchell, 2005:9). On the other hand, the focus of qualitative research is on authentically capturing the lived experiences of people (Neuman, 2006:196; Onwuegbuzie & Johnson, 2006:49).
In quantitative research, validity is more concerned with measurement validity. It determines whether or not the research truly measures what it was intended to measure, or how truthful the research results are. In other words, does the research instrument allow the researcher to hit "the bull’s eye" of the research object? (Joppe, 2000 cited in Golafshani, 2003). Validity in quantitative research can be minimised through careful sampling, appropriate instrumentation and appropriate statistical treatments of data (Cohen, Manion & Morrison, 2007:133).
In qualitative research, the term validity has generally been replaced by the term “trustworthiness” (Onwuegbuzie & Johnson, 2006:51) or “validation” (Creswell, 2007:207). Validity in qualitative research has something to do with the research being accurate, correct or true (Robson, 2002:171). Various authors (Lincoln & Guba, 1985; Cohen, Manion & Morrison, 2000; Onwuegbuzie & Leech, 2007; Silverman, 2006; Creswell, 2007) have proposed ways in which trustworthiness in qualitative research can be achieved. These include prolonged
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engagement, persistent observation, triangulation, peer debriefing, negative case analysis, referential adequacy, and member checks. Others include thick description, clarifying researcher bias and external audit.
4.8.2 Reliability in quantitative and qualitative research
In any study, reliability is a consequence of validity (Patton, 2002). There can be no validity without reliability. Therefore, demonstration of the former is sufficient to establish the latter (Lincoln & Guba, 1985:316). In quantitative research, reliability refers to dependability, consistency and replicability over time, instruments and groups of respondents (Cohen, Manion & Morrison, 2007:146; Leedy & Ormrod, 2005:31; Payne & Payne, 2004). It is mainly concerned with the extent to which similar results will be obtained if the study was to be repeated (Payne & Payne, 2004:195). There are four ways of achieving reliability in quantitative research: clearly conceptualising constructs; use of a precise level of measurement; use of multiple indicators; and use of pilot tests (Neuman, 2006:190). Thus, research findings are considered to be reliable if they are repeatable, and if the repeated measures yield constant results (Babbie & Mouton, 2001; Cohen, Manion & Morrison, 2000; Leedy & Ormrod, 2001; Payne & Payne, 2004).
In qualitative research, reliability includes fidelity to real life, context-and-situation-specificity, authenticity, comprehensiveness, detail, honesty, depth of response and meaningfulness to the respondents (Cohen, Manion & Morrison, 2007:149). Reliability in qualitative research can be achieved through a range of data sources and use of multiple measurement methods (Neuman, 2006:196). It can also be achieved by the use of standardised methods to write field notes and prepare transcripts, and by comparing the analysis of the same data by several researchers in the case of interviews and textual studies (Silverman, 2006).
4.8.3 Pre-testing the instruments
Pre-testing the data collection instruments is one of the tools that may be used for content validation (Ngulube, 2005:136). No matter how carefully a data collection instrument is designed, there is always a possibility of error. The surest protection against such errors is to pre-
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test the data collection instruments (Babbie & Mouton, 2001:244). Pre-testing gives the researcher an opportunity to identify items that tend to be misunderstood by the participants. It also gives respondents ample opportunity to highlight problem questions, poor instruction, and unnecessary or missing questions, and to give their general reactions to the instrument (Powell & Connaway, 2004:140).
In this study, the data collection instruments were developed by the researcher, in collaboration with her supervisor. The developed questionnaires, interview schedules and FGD guide were pretested. The pretesting was done in two phases, as advised by Dawson (2007). In the first phase, the researcher asked people who were not involved in the preparation of the tools to read the tools and see if there were any ambiguities. The tools were sent to practicing library and information science professionals, who were asked to provide their comments. They provided useful comments, which were then used to modify the tools. After incorporating all the comments, the research tools were translated into Swahili. In the second phase, the Swahili tools were pretested in a purposively selected village with similar characteristics to those of the study villages. The data collection instruments were pretested in Tungamalenga village, Iringa rural district. The instruments were modified to incorporate suggestions drawn from the pretesting. The complete data collection instruments in Swahili are presented in Appendices 3, 5 and 7.
4.8.4 Sampling adequacy and saturation
Saturation is defined as the process of adding cases until the full range of what there is to observe/cover has been included. Data saturation means that sampling continues to the point at which no new information is obtained and redundancy is achieved (Bryman, 2004:305; Teddlie & Tashakkori, 2009). When researchers have truly attained saturation, informational adequacy has been achieved. At this point, the actual number of cases is less important than the sense of having fully covered or saturated the topic of study (Bryman, 2004:305; Pickard, 2007:91). Saturating data ensures replication in categories - replication verifies and ensures the comprehension, adequacy and completeness of the data (Morse et al., 2002). In this study, efforts were made to ensure that the sample was appropriate, consisting of participants who best represented the research topic.
88 4.8.5 Verification
Verification is the process of checking, confirming, making sure and being certain. In qualitative research, verification refers to the mechanisms used during the process of research to incrementally contribute to ensuring reliability and validity (Morse et al., 2002). In this study, the researcher sought to identify and correct errors in every step of the research process. This allowed the researcher to judge the situation and decide whether to continue, stop or modify the research process, in order to achieve validity and reliability. For instance, checking data collected on a daily basis ensured that the data collected was relevant, and if not, the process was repeated to fill in the gaps and obtain relevant data.