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- INTERPRETATION OF DATA
What is data interpretation?
Data interpretation refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion. Data is very likely to arrive from multiple sources and has a tendency to enter the analysis process with haphazard ordering (Silverman, 2015).
Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed.
Data interpretation has two main categories
- Quantitative Analysis
- Qualitative Analysis
Quantitative data interpretation has mainly four different types of scale
Nominal Scale:
Non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
Ordinal Scale:
Exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales.
Interval:
A measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
Ratio:
Contains features of all three.
Qualitative data
Qualitative analysis, data is not described through numerical values or patterns, but through the use of descriptive context.
The following are the techniques that can be used to gather Qualitative data:
- Observations: Detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity and the method of communication employed.
- Documents: Much like how patterns of behavior can be observed, different types of documentation resources can be coded and divided based on the type of material they contain.
- Interviews: One of the best collection methods for narrative data. Enquiry responses can be grouped by theme, topic or category. The interview approach allows for highly-focused data segmentation.
Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. As person-to-person data collection techniques can often result in disputes pertaining to proper analysis.
Quantitative
Quantitative analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.
The following are some of the ways in which quantitative data is presented
- Mean: A mean represents a numerical average for a set of responses. It can also be defined as sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average and mathematical expectation.
- Standard deviation: Standard deviation reveals the distribution of the responses around the mean. It describes the degree of consistency within the responses.
- Frequency distribution: This is a measurement gauging the rate of a response appearance within a data set. When using a survey, for example, frequency distribution has the capability of determining the number of times a specific ordinal scale response appears.
Common challenges with data interpretation
Correlation mistaken for causation: This refers to the tendency of data analysts to mix the cause of a phenomenon with correlation.
Confirmation bias: This problem occurs when you have a theory or hypothesis in mind, but are intent on only discovering data patterns that provide support, while rejecting those that do not.
Irrelevant data: This happens because it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.
INTEGRATING OF FINDINGS
This refers to the way in which the researcher uses the results got from the field and analyzes it while using the already existing research of a specific author to either agree with the findings or disagree with the already existing knowledge.
There are two different ways of integrating findings with existing knowledge
Qualitative Integration
Qualitative research integration incorporates two techniques; the traditional literature review, and the box count or voting method of summarizing findings.
The traditional literature review provides a narrative overview of prior findings accommodating smaller bodies of literature than are typically encountered today.
Narrative overviews of prior findings, while offering a certain contextual richness, generally do not provide the systematic information a researcher needs to design more powerful future investigations (Saunders, 2011).
The box count or voting method is a more formal method which determines for each study only whether or not a statistically significant difference was found and if so in what direction. A counting of significant positive, significant negative, and non-significant findings and hence the declaring of a winner is reported. An extension of this approach, the sign approach, tabulates the direction of effects without regard to statistical significance and computes the probability of the results obtained under the assumption that the two methods studied in an experimental setting are equally effective (Cooper and Rosenthal 1980).
The voting method is biased in that it disregards sample size. For example several small studies showing not quite significant results would outvote one large sample study showing just significant results generating a conclusion quite at odds with one’s best instincts (Glass, McGaw, and Smith 1981).
However the main criticism with qualitative integration is that;
Qualitative reviews can result in information overload if many studies are involved in such a narrative review.
The use of the box count or voting method technique results in not using information that may be available in the primary studies.
Qualitative reviews have difficulty in handling a large number of studies.
The diversity of study characteristics is not easily incorporated into such a review and it is difficult to estimate the impact- of these different study characteristics on findings.
Finally, narrative overviews do not provide systematic information and may appear quite disjointed especially when addressing a large number of studies.
However apart from the above criticism findings the strength of the qualitative integration approach rests in dealing with the integration of smaller sets of studies.
Quantitative Integration
The following are the benefits of quantitative of Quantitative Integration
(1) Increasing power due to increased sample size which may result from the pooling of smaller sample sized studies showing concordant but non-significant results.
(2) Obtaining a more precise average effect size measure.
(3) Describing the form of the relationship between two variables over a wider range as individual studies may cover this wider range.
(4) Harnessing the benefits of contradictions and determining the explanations for these.
(5) The ability to effectively handle a broader conceptual scope.
- II) COMPARING
This refers to a situation where the researcher measures the influence of specific variable against the other.
There are five elements required to have better comparison of the study;
Frame of Reference. This is the context within which a researcher places two things they plan to compare and contrast; it is the umbrella under which one groups them. The frame of reference may consist of an idea, theme, question, problem, or theory; a group of similar things from which one extract two for special attention; biographical or historical information. The best frames of reference are constructed from specific sources rather than a researcher’s own thoughts or observations.
Grounds for Comparison; The researcher needs to indicate the reasons behind the comparison and also the choice of comparisons must be reasonable. For example a researcher may be comparing mountains lakes and rivers therefore there must be grounds.
Thesis. The grounds for comparison anticipate the comparative nature of the thesis. As in any argumentative paper, a researcher’s thesis statement will convey the gist of their argument, which necessarily follows from their frame of reference. But in a compare-and-contrast, the thesis depends on how the two things they chosen to compare actually relate to one another.
Organizational Scheme. A researcher’s introduction will include their frame of reference, grounds for comparison, and thesis. There are two basic ways to organize the body of a research paper.
- In text-by-text, were researcher discuses of A, then all of B.
- In point-by-point, were the researcher alternate points about A with comparable points about B.
Linking of A and B. All argumentative papers require the researcher to link each point in the argument back to the thesis. Without such links, the reader will be unable to see how new sections logically and systematically advance their argument. In a compare-and contrast, they also need to make links between A and B in the body of their essay if the researcher wants their paper to hold together.
Merits of comparing
This enables a reader of the research to be able to get in-depth understanding of the results in the study
This also enables the researcher to present information that is correct and accurate during the course of the study.
Comparing situations helps the researcher to present information that readers can understand the research study.
Through comparing in the research study the readers of a research report can be able to gain deep meaning of the situation and alternatives in the study.
Demerits of comparing
Through comparing some readers may be confused while reading the research report this therefore may be difficult for them to comprehend the meaning of given themes in a given report.
The sub themes may be difficult for the readers to clearly understand because using one object to compare with the other will change one reason with the other.
Grounds of comparison may be misunderstood by the readers.
CONTRASTING
Thematic analysis is used in qualitative research and focuses on examining themes within data, This method emphasizes organization and rich description of the data set. Thematic analysis goes beyond simply counting phrases or words in a text and moves on to identifying implicit and explicit ideas within the data. Coding is the primary process for developing themes within the raw data by recognizing important moments in the data and encoding it prior to interpretation. The interpretation of these codes can include comparing theme frequencies, identifying theme co-occurrence, and graphically displaying relationships between different themes. Most researchers consider thematic analysis to be a very useful method in capturing the intricacies of meaning within a data (Francis, Johnston, Robertson, Glidewell, Entwistle, Eccles, & Grimshaw, 2010).
Advantages
- Flexibility it allows researchers, in that multiple theories can be applied to this process across a variety of epistemologies.
- Well suited to large data sets.
- Allows researchers to expand range of study past individual experiences.
- Great for multiple researchers.
- Interpretation of themes supported by data.
Disadvantages
- Reliability is a concern due to wide variety of interpretations from multiple researchers.
- Thematic analysis may miss nuanced data.
- Flexibility makes it difficult to concentrate on what aspect of the data to focus on.
- Discovery and verification of themes and codes mesh together.
- Limited interpretive power if analysis excludes theoretical framework.
- Difficult to maintain sense of continuity of data in individual accounts.
- Does not allow researchers to make claims about language usage.
Speculation
Speculation is a notable response, or a set of responses, to dynamic and complex social phenomena that cannot be held, observed and acted upon without either the taking of risks or the experiencing of consequences (Batson, 2014). While it sometimes connotes an activity of anticipation and even exploitation of expectations, in other cases it denotes an investment in the real possibility of grasping alternate futures. Indeed, one of the threads that runs through the various engagements with the speculative is a renewed interest in the possibility of extracting from the present certain immanent potentialities that may be capable of opening up a transition into otherwise unlikely futures.
Theoretical consolidation
According to Hurley, Losh, Parlier, Reznick, & Piven, (2007). a theoretical consolidation definition is a proposed way of thinking about potentially related events. Indeed, theoretical definitions contain built-in theories; they cannot be simply reduced to describing a set of observations.
A theoretical consolidation can also be defined as an abstract concept that defines a term in an academic discipline, without a falsifiable operational definition, conceptual definitions assume both knowledge and acceptance of the theories that it depends on.
A theoretical definition is a proposed way of thinking about potentially related events. Indeed, theoretical definitions contain built-in theories; they cannot be simply reduced to describing a set of observations. The definition may contain implicit inductions and deductive consequences that are part of the theory (Denzin & Lincoln, 2011).
A theoretical definition of a term can change, over time, based on the methods in the field that created it.
Theoretical application
This refers to the use of a theory to explain a given phenomenon in research study. This happens when in research a researcher wants to prove the occurrence of a given phenomenon to help in the explanation of a given study.
The advantages with theoretical application include;
This helps the researcher to understand if the study he is carrying out is applicable to an already existing theory to explain a given phenomenon.
It helps the researcher to claim a originality of the research undertaken.
It helps to understand the strengths and weakness of a given theory in relation to a given study.
The study also enables the researcher to clearly gain better understanding of the study in relation to other studies.
Demerits include
It may make the research work not look original.
III) METAPHOR
Metaphor is a figure of speech that makes an implicit, implied, or hidden comparison between two things that are unrelated, but which share some common characteristics. In other words, a resemblance of two contradictory or different objects is made based on a single or some common characteristics.
Metaphors are a form of figurative language, which refers to words or expressions that mean something different from their literal definition. In the case of metaphors, the literal interpretation would often be pretty silly.
Metaphors show up in literature, poetry, music, and writing, but also in speech. If you hear someone say “metaphorically speaking,” it probably means that you shouldn’t take what they said as the truth, but as more of an idea. For example, it’s finals period and after exams, students are saying things like “That test was murder.” It’s a fair guess they’re still alive if they’re making comments about the test, so this is an example of speaking metaphorically or figuratively.
According to Kantrowitz-gordon & Vandermause, (2016), the use of metaphors in qualitative research provides an opportunity to examine phenomena from a unique and creative perspective. Metaphors can be used to provide structure to the data; to understand a familiar
Advantages of Metaphor
Metaphor makes the work easily understood by the read.
It helps the readers to relate the research to their usual real life situations.
Readers can easily attach great importance to the work through metaphors.
Disadvantages of metaphor
They can confuse the readers
It diverts the attention of the reader from the main topic of study.
Analogies
An analogy is a literary device that creates a relationship based on parallels or connections between two ideas. By establishing this relationship, the new idea is introduced through a familiar comparison, thus making the new concept easier to grasp (Watzlawick, Bavelas, & Jackson, 2011).
There are different types of analogies
A figurative analogy is a comparison about two things that are not alike but share only some common property.
Literal analogy is about two things that are nearly exactly alike. The two things compared in a figurative analogy are not obviously comparable in most respects.
Analogies play a significant role in problem solving as well as decision making, argumentation, emotion and prediction.
References
Francis, J. J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V., Eccles, M. P., & Grimshaw, J. M. (2010). What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychology and Health, 25, 1229–1245. doi:10.1080/08870440903194015.
Hurley, R. S., Losh, M., Parlier, M., Reznick, J. S., & Piven, J. (2007). The broad autism phenotype questionnaire. Journal of autism and developmental disorders, 37(9), 1679-1690.
Denzin, N. K., & Lincoln, Y. S. (2011). The SAGE Handbook of qualitative research ( 4th ed.). Los Angeles: Sage Publications.
Kantrowitz-Gordon, I., & Vandermause, R. (2016). Metaphors of distress: Photo-elicitation enhances a discourse analysis of parents’ accounts. Qualitative health research, 26(8), 1031-1043.
Silverman, D. (2015). Interpreting qualitative data. Sage.
Saunders, M. N. (2011). Research methods for business students, 5/e. Pearson Education India.
Cooper, H. M., & Rosenthal, R. (1980). Statistical versus traditional procedures for summarizing research findings. Psychological bulletin, 87(3), 442.
Watzlawick, P., Bavelas, J. B., & Jackson, D. D. (2011). Pragmatics of human communication: A study of interactional patterns, pathologies and paradoxes. WW Norton & Company.
Batson, C. D. (2014). The altruism question: Toward a social-psychological answer. Psychology Press.
