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researcher to obtain information on trends in climate change and variability over the past years. The focus group, through group discussion and brain-storming, is expected to provide clarification and understanding of factors affecting access and use of information on adaptation to climate change and variability and knowledge generation, use and sharing of climate change and variability knowledge among farmers. The FGD is one of the best methods for seeking clarification on issues perceived by people.
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Plano-Clark 2007:128). When mixed method analysis is used, there are different ways of reporting the findings. Greene, Caracelli and Graham (1989) describe the five approaches used when reporting mixed methods analysis is done. These include (a) no integration, analysing and interpreting qualitative and quantitative data separately; (b) analysing separate but some integration during interpretation; (c) integration during both analyses and interpretation; and (d) analysis procedures not reported. This study employed approach (b).
4.12.1 Qualitative Data Analysis
Qualitative data analysis involves organising, accounting for and explaining the data through participants, defining the study situation, noting patterns, themes, categories and regularities (Cohen, Manion and Morrison 2011:537).
In the present study, in order to manage the qualitative data collected from interviews effectively, the data was analyzed thematically using content analysis (Patton 1990). Data from the interview schedules was collected and systemically arranged into themes. The themes were based on the study research questions. Thematic analysis is a type of content analysis which is a detailed and systematic examination of the contents of a particular body of material for the purpose of identifying patterns, themes or biases (Leedy and Ormrod 2005:142).
4.12.2 Quantitative Data Analysis
Cohen, Manion and Morrison (2011:604) describe quantitative data analysis as a powerful research tool, in most cases associated with large-scale research emanating partly from a positivist traditional approach. Numerical data analysis in social sciences mostly employs software such as the SPSS, Minitab and Excel which ease the computation of data.
In this study, the SPSS programme version 20 was used to analyse the quantitative data from semi-structured interviews. For quantitative data, descriptive statistics such as the means, frequencies, standard deviations, regression analyses and cross tabulation were generated using SPSS. SPSS was used because it offers powerful and easy ways to extract data, reduces time required to analyse data, reduces errors involved in coding data, analysing data with in-depth statistics and producing charts (Pickard 2007:278).
119 4.13 Validity and Reliability
Validity and reliability are of concern in qualitative and quantitative measurements as they are concerned with how substantial measures are related to constructs so as to establish the truthfulness, credibility and believability of findings (Neuman 2003:178; 2006:188). In a broader sense, the reliability of a measure indicates the extent to which it is without bias (error free) and hence its application ensures consistent measurement across time and across various items in the instrument (Sekaran 2003:203). Ashatu (2009) points out that scientific knowledge’s credibility can be enhanced by improving both internal consistency and generalisability, through combining both qualitative and quantitative methods in the same study.
4.13.1 Reliability in Qualitative and Quantitative Research
Reliability is concerned with whether or not the results of a conducted study are consistent, stable and repeatable (Neuman 2003:178; Sekaran 2003:203; Bryman 2008:31-32). Thus, dependability, consistency and replicability should be over time, over instruments and over a group of respondents (Cohen, Manion and Morrison 2011:199). In qualitative methodologies, reliability includes fidelity to real life, context-and-situation-specificity, authenticity, comprehensiveness, detail, honesty, depth of response and meaningfulness to the respondents (Cohen, Manion and Morrison 2007:149). Reliability in qualitative approach is ensured through properly designed and structured research to balance objectivity and subjectivity (Borland 2001:8). In quantitative research, reliability refers to the extent to which similar, consistent and stable results will be obtained if the study is repeated over time (Sekaran 2003:203; Payne and Payne 2004:195; Cohen, Manion and Morrison 2011:200). In this study, reliability was ensured through the use of proper transcription of data and pretesting of research instruments to ensure the proper use of correct terminologies familiar to respondents to avoid misinterpretation of constructed concepts.
4.13.2 Validity in Qualitative and Quantitative Research
Validity is concerned with whether or not an indicator devised to measure a concept really measures that concept in a research study (Babbie and Mouton 2001:122; Bryman 2008:32).
Validity acts as a bridge between a construct and the data to establish the truthfulness of the data (Neuman 2003:185; 2006:196). There are diverse types of validity in both qualitative and quantitative research. Common types include internal and external validity. Internal validity is concerned with the extent to which a research study design and the data yielded
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allow a researcher to produce accurate conclusions about constructs relationships (Leedy and Ormrod 2005:97). External validity is the extent to which the findings of a research study can be generalised in a wider context (Leedy and Ormrod 2005:97). In order to test the worthiness of instrument measures in research, validity tests are conducted (Sekaran 2003:206).
Qualitative research is more interested in authenticity referring to fairness and honest and balanced accounts of social life, rather than validity (Neuman 2003:185). Validity in qualitative research depends on credibility, skill, competence and the rigor of the qualitative inquiry, as the researcher is the instrument (Patton 1990; 2002:14). To minimise threats to validity and increase trustworthiness (Creswell 2003:196; Leedy and Ormrod 2005:100;
Creswell 2007:204), a researcher should use triangulation, extensive time in the field, peer debriefing, negative information, feedback from other members in the field and use of rich thick descriptions, so that readers can make conclusions, validate a respondent, clarify bias and use an external auditor.
In quantitative research, validity must be faithful to its foundations of positivism and positivist principles by adhering to controllability, replicability and predictability (Cohen, Manion and Morrison 2011:180). In quantitative inquiry, validity pivots on careful instrument construction, to ensure the designed instrument measures what it is supposed to measure (Patton 2002:14). Of major concern in quantitative research is the measurement of validity in the research. Measurement of validity refers to how well the conceptual and operational definitions are interconnected (Neuman 2003:182; 2006:192). Threats to the measurement of validity include inadequate procedures and participants’ experience, which might influence the real problem under observation. Other threats include the researcher drawing incorrect inferences from sample data to other settings, lack of knowledge on statistics and the use of inadequate definitions and incorrect use of variables (Creswell 2003:171). Threats to validity in quantitative research can be minimised through careful sampling, appropriate instrumentation and the appropriate statistical treatment of data (Cohen, Manion and Morrison 2007: 133; 2011:179). In the present study, validity was attained and ensured by pretesting the interview guides and having a truly representative sample of study elements at village level from the study population in Iramba and Bahi districts, as well as careful analysis of the data.
121 4.13.3 Pretesting of Data Collection Instruments
A pilot study and the pre-testing of the data collection instruments were done in Ulemo village, which is close to the village of Maluga. A total of eight trained farmers was selected and interviewed. This village was chosen for the pretesting because some of the farmers from Ulemo village had also received training by CCAA experts on adaptation to climate change and variability. The validity and reliability of the pretested data analysis was done through running regression and correlation tests on the data from the interview tools. The regression and correlation test indicated a Cronbach’s alpha coefficient value of 0.742, at the 0.05 significance level. The study adapted and modified questions from similar studies on agriculture, knowledge and information systems and climate change and variability, such as those by Lwoga (2009), Gundu (2009), Pelum (2010) and Munyua (2011) and Baide (2005), to inform the instrument questions. The researcher selected eight untrained farmers to be interviewed in the pretesting. After the pretesting, interview questions in the interview schedules were modified clarify the meaning of concepts.