Quitosano y sistema inmune

Case study researchers collect data from a variety of sources. The most common being interviews, electronic or paper documents, archival records, direct observations, participant observations, and physical artifacts (Yin, 2009). Several data collection tools are frequently used during a case study investigation as this can increase the trustworthiness of the data through triangulation, enhancing the potential generalizability of the research findings, and improving the robustness of the final report (Babbie, 2007; Creswell, 2008; Patton, 2002). The main data collection tools used in this research were semi-structured interviews and document analysis.

3.5.1 Semi-structured interviews

Semi-structured interviews are a valuable data collection tool as they allow researchers to facilitate a guided non-threatening conversation through the use of a flexible set of topic-specific questions (Yin, 2009). Most of the questions used in this situation tend to be open-ended in nature so that interviewees are left to decide how they will answer questions, both in relation to the words they use and the length of response they give (Babbie, 2007; Denscombe, 2003; Scott & Usher, 1999). Asking open-ended questions tends to result in less structured responses being collected than when closed questions are used. While this can make analysis more difficult and time consuming, open-ended questions generally elicit richer and more insightful responses (Gall et al., 2007; Patton, 2002). Researchers therefore gain a more in- depth understanding of their participants‘ thoughts and feelings, and their focus case or phenomenon, than when closed questions or a highly structured interview are used (Bell, 2005; Gall et al., 2007; Yin, 2009). The flexible nature of semi-structured interviews also allows researchers to respond to important, but unanticipated, points or issues that arise during an interview or the wider research process itself (Cohen et al., 2003).

The types of questions used in semi-structured interviews need to be linked to the focus of the research, well supported by suitable prompts and probes, and worded in a way that encourage respondents to articulate rich and meaningful ideas.

Researchers need to guard against using vague, ambiguous, or ‗doublebarrelled‘ questions, as these types of questions can diminish the quality of the data. Researches also need to consider what type of analysis they are going to do to ensure that their questions and all other aspects of their inquiry are constructively aligned. Creating good open-ended interview questions therefore takes time, creativity, knowledge, insight, and practice (Bassey, 2003; Bell, 2005; Denscombe, 2003; Maxwell, 2005; Patton, 2002).

Effective semi-structured interviews therefore require:

getting interviewees‘ permission to tape interviews to increase the accuracy and analysis of the data that is being collected;

well crafted open-ended questions that ensure that discussions remain focused;

a location where an interviewee will feel comfortable and where there will be minimal interruptions;

ice-breaker type questions that help participants become comfortable with the interview situation;

scaffolding a conversational tone through actively listening to the participants‘ responses and providing verbal prompts and probes when necessary to gain further insight or clarity;

flexible, reactive and sensitive responses to the information that is being shared;

an awareness of bias, as the very presence of a researcher can potentially skew answers through interviewer effect22 _{as what interviewees say they do} versus what they actually do can differ (Denscombe, 2003; Gall et al., 2007; Kvale, 2007; Maxwell, 2005; Patton, 2002; Yin, 2009).

22 _{Interviewer effect is where respondents make assumptions about what they think the} interviewer wants to hear and alter their answers accordingly or fail to answer questions honestly because the question is too sensitive or they are afraid that their response might be used against them (Gall et al., 2007).

3.5.2 Document analysis

Gathering a range of documents during the data collection phase of case study research can aid research validity through triangulation, as documentary evidence can corroborate and enhance evidence that researchers have collected from other sources. Written reports, policy documents, unit plans, institutional records, and assessment documents are stable forms of data that can be repeatedly reviewed. These sorts of documents will have often been constructed outside of the research so they should not be biased by the research itself. Researchers must, however, be aware that documents may contain errors or be influenced by the author‘s perspective. Sometimes, it can be difficult for researchers to gain a clear understanding of the contextual factors that may have influenced the way in which a document was created. There may also be times when participants have reasons for either openly or surreptitiously denying researchers access to certain documents. The important thing for researchers to remember is that when documentary evidence contradicts information gained from other sources, or introduces new ideas; it should prompt further investigation to ensure the clarity and accuracy of the research data (Babbie, 2007; Gall et al., 2007; Yin, 2009).

Ultimately, the decisions that researchers make about the data collection tools they use have the potential to affect the validity of their data. It is therefore vital that researchers actively reflect on the potential consequences of the decisions they make about the type of case study approach they are using, the cases they have selected, the data collection tools they are using, and the analysis they are doing (Denscombe, 2003). The next section looks at data analysis.

3.6

Data analysis framework

Regardless of which analysis tools case study researchers use, there is a well established analysis pathway that is generally used to interpret qualitative data. The first step involves organising data into formats that aid analysis. Interviews and fieldnotes may be transcribed, written documents scanned to create portable document files (PDFs), and artefacts photographed. The next step is to read through

this data to gain a general sense of the material that has been gathered. The third step involves coding or categorising the data. Whether this is done manually or with the aid of some sort of software data analysis programme, the purpose is the same. The purpose is to draw out patterns or unique instances and find keys themes that are contained within the data. At this point researchers will generally pause and reflect on what they have found to consider how their findings mirror, or differ from, previous research that has been reported in the literature review. A decision is then made as to whether there is a need to go back and conduct further data collection or analysis, or whether the evidence is robust enough to move on to constructing the final research report (Babbie, 2007; Creswell, 2008). Figure 3.1 illustrates the non- linear iterative nature of this process.

The Researcher…

collects data 

prepares the data for analysis 

reviews the data 

codes or categorizes the data 

identifies patterns, themes or unique instances within their data



cross checks their data against previous research in preparation for writing their

research report

(Adapted from Creswell, 2008)

Figure 3.1: A qualitative process of data analysis in case study research

While most multiple case study researchers work within this framework, they also need to decide how to synthesize the data they have collected from their multiple sites/cases to make cross case assertions. Stake (2006) suggests three possible approaches: Itera tiv e Si_m ultan eou s

Track I: Emphasize Case Findings - Assertions emphasize the situational factors of individual cases;

Track II: Merge Case Findings - Assertions are more closely related to the research questions (themes) through merging individual case findings;

Track III: Provide Factors for Analysis - Assertions are based on conceptual factors that are derived from individual cases. The situationality of cases is more blurred.

Regardless of which track researchers select they must consider the ―Case-Quintain dilemma‖ when writing up their reports by asking: ―Does the Quintain need to be thought about more in terms of what is happening in the individual Cases, or more in terms of what is common across the Cases?‖ (Stake, 2006, p. 71). The response to this question will undoubtedly be influenced by the purpose and scope of the study. As the focus of this research was to investigate a phenomenon—assessment practices in year 9 and 10 social studies courses—the analysis phase has been aligned to Stake‘s Track II approach.