1. Meaning of Data Analysis
What is data?
Before defining data analysis, lets first understand what data means. Data refers to raw facts, observations, or information collected by a researcher for the purpose of answering research questions or achieving research objectives. It is raw because it has not been processed or given context. A researcher normally does not collect data merely to present it. The purpose is to use the data to answer questions.
Data can also be defined as the quantitative or qualitative information of a variable that is obtained from the subjects of the research. Data can be in numbers, images, words, figures, pieces of information, facts or ideas.
There are various methods of collecting data.
a) Administration of Questionnaires
b) Interviews
c) Focus group discussions
d) Observations
e) Document Analysis
2. What is data analysis?
Data analysis is the process of reducing research data to manageable summaries. In addition, we can say that data analysis is the process of turning collected data into meaningful findings. We reduce data by use of statistical tools for quantitative data and thematic induction for qualitative data.
NOTE: For quantitative data, the selection of the statistical tool is dependent on the on the scale of measurement i.e. you must be SURE of the scale of measurement for each question of research. Next week we shall discuss Scales of Measurement
Data analysis involves several activities
Quantitative data analysis may involve:
1. Editing – checking data for errors, omissions and inconsistencies.
2. Coding – assigning codes or labels to responses.
3. Data entry – entering data into a spreadsheet or statistical software.
4. Data cleaning – identifying and correcting errors, duplicates, missing values and inconsistencies.
5. Classification – grouping data into meaningful categories.
6. Tabulation – presenting data in tables.
7. Summarization – reducing large amounts of data into manageable summaries.
8. Statistical analysis – applying appropriate statistical procedures to quantitative data.
9. Thematic analysis – identifying themes and patterns in qualitative data.
10. Presentation – presenting findings using tables, graphs, charts, narratives or other appropriate formats.
Qualitative Data Analysis involves:
1. Processing Condensed Data – this step is also referred to as pre-analysis of data. It involves transcribing interviews (changing from audio to print) and translating observed events and behaviours into words;
2. Condensing Data –This is done by:
a) Editing data – editing data ensures removal of any grammatical error so as to ensure precise explanation in a concise form and also increase clarity. It is important to note that while editing, the critical meaning of data in not changed
b) Removing Ambiguity – this involves clarifying the meanings presented by the data. Where a phrase is repeated and monotonously used by a participant, it is referred to as ambiguity. In this case the repetitive phrase/statement needs to be edited.
c) Creating Data Categories – A data category is a theme or class of data established to represent related or similar forms of data. This is a complex process and requires that the researcher be very familiar with the data. He/she must be able to detect various categories in data, which should be distinct from each other. The researcher should then establish the relationship among these categories. Themes are identified from literature review and theories;
d) Selecting and assigning data to established categories using codes
e) Summarizing the data in each category – the researcher should condense and report in his/her word and should not keep on repeating the same quote if said by two or more people.
3. Presentation of Findings – This is used to display analysed data. This is done using strategies such as narratives, direct quotes, matrices, tables and diagrams. The researcher should think critically about the techniques s/he should use to present data and justify the selected strategy. In qualitative research, tables are not statistical but are interpretive frames / analytic frame. An interpretive frame has a question (Theme), Response and Remarks
4. Making Sense of the Findings – Making sense of the findings involves interpreting the results and drawing parallels and disparities from existing theories e.g. these results concur with the study done in … which … The researcher quotes verbatim when all the given responses or a section appears to summarize a wide range of aspects being investigated. Finally, the researcher should make conclusions and recommendations of the study.
3. Data Analysis Versus Data Interpretation
These two concepts are related, but they are not the same.
Data Analysis – this is the process of reducing research data to manageable summaries; it focuses primarily on what the data shows.
Data Interpretation – Data interpretation focuses on what those findings mean in the context of the research problem. Interpretation means searching for meaning and implication of research results, in order to make inferences, draw conclusions and relate to the theory;
A useful way to remember the difference is:
Analysis asks: “What does the data show?”
Interpretation asks: “What does this finding mean?”
Example 1:
Suppose a researcher studies employee training. The analysis produces the following result:
Ø Employees who received more training hours had higher average performance scores. This is an analytical finding.
The researcher may then interpret this finding as:
Ø The finding suggests that investment in employee training may contribute to improved employee performance. That is interpretation.
Example 2:
Suppose a researcher in a study on academic performance finds that:
Ø 70.0% of students reported that they have difficulties learning online. This is analysis
Interpretation:
Ø This finding indicates that poor or lack of access to online materials could be a barrier to effective online learning.
NOTE: interpretation must remain grounded in the evidence. The researcher should avoid making claims that the data cannot support.
The following gives a simple distinction between data analysis and data interpretation.
Data Analysis Data Interpretation
Ø Examines the data Explains the meaning of the findings
Ø Identifies patterns Explains why the patterns may matter
Ø Calculates or organizes results Relates findings to the research problem
Ø Answers “What does the data show?” Answers “What does it mean?”
Ø May involve statistical tests Involves reasoning and contextual
explanation
Ø Produces findings Helps develop conclusions and
implications
Important point for students
Analysis and interpretation should not be confused. A researcher may have excellent statistical analysis but poor interpretation. Conversely, a researcher may provide interesting interpretations without adequate analytical evidence. Good research requires both. Other terms that students confuse are:
Data Presentation – refers to ways of arranging data to make it clear;
Discussion of Findings – this means tying the finding to the literature review.
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