Module 3: Strategies for Data Collection

• Observation - is one of the means of collecting data in qualitative research.
• Method - data can be obtained by watching and listening purposively to people’s behavior (Almeida et al. 2016, 96).
• Observation - is a primary tool that helps you document what is happening in a particular research setting.
• The two major types of observation are: Participant Observation and Direct Observation.
• Participant Observation - type of observation is characterized by the researcher’s interaction or participation with participants and become part of their community.
• Direct Observation - the researcher does not participate with the activities of the group under study. S/He acts as a passive observer (Almeida et al. 2016, 96) and records the participants’ behavior.
• • Observation Guide It helps to maintain the observer’s focus while also giving the observer scope to reflect on the context associated with each site.

  A) observation guide

  
• it is a example of observation guide opposite of structed observation guide

  A) Unstrucred observation

  
• Observation Grid - The grid is like the guide in that, it helps remind the observer of the events and issues of most import; however, unlike the guide, the observation grid is a spreadsheet or log of sorts that enables the observer to record (and record his reflections of) observable events in relationship to the constructs of interest.
• The grid might show, for instance, the relevant constructs or research issues as column headings and the specific foci of observation as rows.

  
• Complete Participant - As the term denotes, the researcher is part of the group under study and participates in the group members’ activities. However, the participants are not aware that they are being observed and studied.
• Participant as Observer - Here, the researcher is still part of the group under study and participates in the group members’ activities. The participants are fully aware that they are being observed and studied.
• Observer as Participant - The researcher is not part of the group under study but still participates in the group members’ activities. The participants under study are aware of the research and its objectives
• Complete Observer - Here, the researcher is not part of the group under study and does not take part of their activities. The participants are not also aware that they are being observed and studied
• Research Interview - defined as ‘a two-person conversation initiated by the interviewer for the specific purpose of obtaining research-relevant information and content
• Structured Interview - one in which the content and procedures are organized in advance.
• Semi-structured Interview - the interviewer asks a predetermined set of questions. There is no list of options. The interviewee can answer the questions in his/her own words.
• Unstructured Interview - an open situation, having greater flexibility and freedom. The research purposes govern the questions asked, their content, sequence and wording are entirely in the hands of the interviewer.
• Interview protocol - is more than a list of interview questions; it also extends to the procedural level of interviewing and includes a script of what you will say before the interview
• Coding - is the process of analyzing the data and searching for essential information that answers the research questions. Another definition says that it is a process of filtering the data.
• PRESET - are codes that have been identified prior to analysis. It is also called the deductive approach wherein the codes/themes were determined beforehand, either from related theories or existing knowledge
• EMERGENT CODES - are those that show up during analysis. It is also referred to as inductive approach where the data is the one that determine the codes/themes.
• Collating - your way of bringing together the coded data. Giving the data an orderly appearance is putting them in a graph, specifically a table of responses
• Patterns and themes - are words or phrases that collectively describe the experience or thoughts of every participant.
• Word Repetitions - The researcher should understand what people are talking about by looking at the words they use. Word repetitions can be analyzed by informal mode by simply reading the text and note words or synonyms that people use a lot.
• Indigenous categories – It is a way of finding themes by looking for a local term that sounds unfamiliar or used in unfamiliar ways. It also refers as "indigenous categories."
• Key-words-in-context (KWIC) - is a technique that is based on a simple observation by understanding a concept and looking at how it is used.
• Compare and Contrast - Compare approach are based on the idea that themes represent the ways in which texts are either similar or different from each other from the words "constant comparison method.”
• Social Science Queries – It refers to the interests in understanding how textual data illuminate questions of importance to social science.
• Phase 1. Familiarization with the data: It involves deeper understanding of the data gathered by reading it multiple times. In doing so will help you to gain familiarity of the content.
• Phase 2. Coding: It involves generating concise labels or codes. You have to code the whole dataset and make sure that the codes are relevant to your topic then collate them.
• Phase 3. Searching for themes: This is now the phase where you will be generating themes from the codes. You may want to go through your codes multiple times just to be sure that you are collating relative codes.
• Phase 4. Reviewing themes: From the initial generation of themes, you may still see some irrelevant themes when you try to connect it with your topic. To prevent this, you have to review the generated themes and then compare it to the dataset and then to your topic.
• Phase 5. Defining and naming themes: there should be a “story” behind those themes since it will be further discussed in the results and discussion part. this phase also involves deciding on an informative name for each theme.
• Phase 6. Writing up: This final stage involves knitting together the analytic narrative explanation from each generated theme, data extracts, and contextualizing the analysis in relation to existing literature.