47 Interpretation

Malarvizhi. V

INTRODUCTION

When we have information about the business or project saved and tracked, what do we do with it? That’s where interpretation of data comes in. It is designed to help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace. Interpretation refers to the task of showing inferences from the collected facts after a systematic and/or experimental study. In fact, it is seek out for broader meaning of research findings. The Task of Interpretation has two major aspects viz.,

a). the effort to establish link in research through connecting the results of a given study with those of another.

b). the establishment of some descriptive concepts In one sense, interpretation is concerned with relationships within the collected data, partially overlapping analysis. It also extends ahead of the data of the study to include the results of other research, theory and hypotheses. Hence, interpretation is the device through which the factors that seem to put in plain words what has been observed by researcher in the course of the study can be better understood and it also provides a theoretical idea which can serve as a guide for new researches.

According to C. William Emory, “Interpretation has two major aspects i.e. establishing stability in the research through linking the results of a given study with those of another and the establishment of some relationship with the collected data. Interpretation can be defined as the device through which the factors, which seem to explain what has been observed by the researcher in the course of the study, can be better understood. Interpretation provides a theoretical conception which can serve as a guide for the further research work”.

Why interpretation

Interpretation is important for the simple reason that the usefulness and utility of research result lie in suitable interpretation. It is being measured a fundamental module of research procedure because of the following reasons:

i). It is all the way through interpretation that the researcher can well understand the abstract principle that works beneath his findings. Through this he can tie up findings with those of other studies, having the same conceptual principle and thereby can forecast about the real world of events. Fresh investigation can test these predictions afterward. This way the link in research can be maintained.

ii). Interpretation leads to the concern on descriptive concepts that can provide as a guide for future research studies; it opens new avenues of intellectual venture and stimulates the hunt for more knowledge.

iii). Through interpretation, the investigator can better understand why his findings are what they are and can create others to understand the real significance of his research findings.

iv). The interpretation of the findings of exploratory research study often pave the way for the formulation of hypotheses for experimental research and as such facilitate the transition from exploratory to experimental research. Since an exploratory study does not have a hypothesis to start with, the findings of such a study have to be interpreted on a ‘post-factum’ basis in which case the interpretation is technically described as ‘post factum’ interpretation.

Technique of interpretation

The task of interpretation is not an easy job; rather it requires a great skill and dexterity on the part of researcher. Interpretation is an art that one learns through practice and experience. The researcher may, at times, seek the guidance from experts for accomplishing the task of interpretation.

The technique of interpretation often involves the following steps:

i). Researcher must give reasonable explanations of the relations which he has found and he must interpret the lines of relationship in terms of the underlying processes and must try to find out the thread of uniformity that lies under the surface layer of his diversified research findings. In fact, that is the technique of how generalization should be done and concepts be formulated.

ii). Extraneous information collected during the study, must be considered while interpreting the final results of research study, for it may prove to be a key factor in understanding the problem under consideration.

iii). It is advisable, before writing final interpretation, to consult someone having insight into the study and who is able to give honest opinion and will not hesitate to point out omissions and errors in logical argumentation. Such a consultation will result in correct interpretation and, thus, will enhance the utility of research results.

iv). Researcher must accomplish the task of interpretation after bearing in mind all relevant factors affecting the problem to avoid false generalization. One must not be in a hurry while interpreting results, for quite often the conclusions, which appear to be all right at the beginning, may not at all be accurate.

Precautions in Interpretation

One should always keep in mind that even if the data are properly collected and analyzed, wrong interpretation would lead to inaccurate conclusions. It is therefore, absolutely essential that the task of interpretation be accomplished with patience in an impartial manner and also in correct perspective. Researcher must pay attention to the following points for correct interpretation:

i). At the outset, researcher must invariably satisfy himself that

ii). The researcher must remain cautious about the errors that can possibly arise in the interpretation of the results. Errors can arise due to false generalization and/ or due to wrong interpretation of statistical measures, such as the application of findings beyond the range of observations, identification of correlation with causation and the like. Another major pitfall is the tendency to assert that definite relationships exist on the basis of confirmation of particular hypotheses. In fact, the positive test results accepting the hypothesis must be interpreted as “being in accord” with the hypothesis, rather than as “confirming the validity of the hypothesis”. The researcher must remain cautious about all such things so that false generalization may not take place. He should be well equipped with and must know the correct use of statistical measures for drawing inferences concerning his study.

iii). He must always keep in view that the task of interpretation is very much tangled with analysis and cannot be noticeably separated. As such he must take the task of interpretation as a special aspect of analysis and accordingly must take all those precautions that one usually observes while going through the process of analysis viz., precautions concerning the reliability of data, computational checks, validation and comparison of results.

iv). The researcher must never lose sight of the fact that his task is not only to make sensitive observations of relevant occurrences, but also to identify and extricate the factors that are initially hidden to the eye. This will enable him to do his job of interpretation on proper lines. Broad generalization should be avoided as most research is not amenable to it because the coverage may be restricted to a particular time, a particular area and particular conditions. Such restrictions, if any, must invariably be specified and the results must be framed within their limits.

v). The researcher must remember that “ideally in the course of a research study, there should be constant interaction between initial hypothesis, empirical observation and theoretical conceptions. It is exactly in this area of interaction theoretical orientation and empirical observation that opportunities for originalities and creativity lie”. He must pay special attention to this aspect while engaged in the task of interpretation.

Types of Interpretation

Researchers use a similar but more comprehensive process together, analyze and interpret data. While Experimental scientists’ base their interpretations largely on objective data and statistical calculations, Social scientists interpret the results of written reports that are rich in descriptive detail but may be devoid of mathematical calculations. The following figure-1 depicts the types of interpretation.

Figure -I

Quantitative Interpretation

Scientists interpret the results of rigorous experiments that are performed under specific conditions. Data which can be quantified are entered into spreadsheets and statistical software programs, and then interpreted by researchers after determining if the results achieved are statistically significant or more likely due to chance or error. The results helps to prove or disprove hypotheses generated from an existing theory. By using scientific methods, researchers can generalize about how their results might apply to a larger population. For example, if data show that a small group of patients in a voluntary drug study went into remission after taking a new drug, other patients might also benefit from it.

The analysis of quantitative data is represented in mathematical terms. The most common statistical terms represented in the figure-2 this includes:

Figure-II

Mean – The mean value represents a numerical average for a set of responses. For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is termed the sample mean to distinguish it from the population mean.

Standard deviation – The standard deviation represents the distribution of the responses around the mean. It indicates the degree of consistency among the responses. The standard deviation, in conjunction with the mean, provides a better understanding of the data. For example, if the mean is 3.3 with a standard deviation (SD) of 0.4, then two-thirds of the responses lie between 2.9 (3.3– 0.4) and 3.7 (3.3 + 0.4).

Frequency distribution – Frequency distribution indicates the frequency of each response. For example, if respondents answer a question using an agree/disagree scale, the percentage of respondents who selected each response on the scale would be indicated. The frequency distribution provides extra information beyond the mean, since it allows for examining the level of consensus among the data.

Higher levels of statistical analysis (e.g., t-test, factor analysis, regression, ANOVA) can be conducted on the data, but these are not regularly used in most program/project assessments.

Qualitative Interpretation

Specific academic disciplines, such as sociology, anthropology and women’s studies, rely a lot on the collection and interpretation of qualitative data. Researchers hunt for new knowledge and approaching into phenomenon such as the stages of grief following a loss, for example. As an alternative of controlled experiments, data is composed through techniques such as field observations or personal interviews of research subjects that are recorded and transcribed. Social scientists study field notes or look for themes in transcriptions to make meaning out of the data.

The analysis of qualitative data is conducted by organizing the data into common themes or categories. It is often more difficult to interpret qualitative data since it lacks the built-in structure found in numerical data. Initially, such data appears to be a collection of random, unconnected statements. The assessment techniques and questions can help to direct the focus on data organization. The following strategies may also be helpful when analyzing qualitative data and also with the help of diagram.

Figure III

Focus groups and Interviews:

Read and organize the data from each question separately. This approach permits focusing on one question at a time (e.g., experiences with tutoring services, characteristics of tutor, student responsibility in the tutoring process). Group the comments by themes, topics, or categories. This approach allows for focusing on one area at a time (e.g., characteristics of tutor – level of preparation, knowledge of content area, availability).

Documents

Code content and characteristics of documents into various categories, (e.g), training manual–policies and procedures, communication, responsibilities. This approach keeps your information organized and easily accessible.

Observations

Code patterns from the focus of the observation, for (e.g), behavioral patterns – amount of time engaged/not engaged in activity, type of engagement, communication, interpersonal skills).

Data Interpretation and Analysis Techniques

The analysis of the data via statistical measures and/or narrative themes should provide answers to your assessment questions. Interpreting the analyzed data from the appropriate perspective allows for determination of the significance and implications of the assessment.

Some Data Interpretation and Analysis Tips

• Consider the data from various perspectives. Whatever your project may be or whatever data you have collected from your business it’s always best to ask what that data means for various actors or participants.
• Think beyond the data but do not stray too far from the data. Be mindful that you are not making too much of your data or too little. Make the link between the data and your interpretations clear. Base your interpretations in your research.
• Make noticeable the assumptions and beliefs, or mental models, that influence your interpretation. We each carry images, assumptions, and stories in our minds about ourselves, others, the organizations we work in, etc. As a composite, they represent our view of our world. Because these models are generally tacit, i.e., below our level of our awareness, if left unexamined, these assumptions and beliefs can lead to incorrect interpretations. Reflect on your own thinking and reasoning. Individually and/or collectively list your assumptions about the inquiry focus. Take care not to disregard outlying data or data that seems to be the exception.
• Data that is surprising, contradictory or puzzling can lead to useful insights (insites.org)

Basic analysis of “quantitative” information

• Make copies of your data and store the master copy away. Use the copy for making edits, cutting and pasting, etc.
• Tabulate the information, i.e., add up the number of ratings, rankings, yes’s, no’s for each question.
• For ratings and rankings, consider computing a mean, or average, for each question. For example, “For question 1, the average ranking was 2.4”. This is more meaningful than indicating, e.g., how many respondents ranked 1, 2, or 3.
• Consider conveying the range of answers, e.g., 20 people ranked “1”, 30 ranked “2”, and 20 people ranked “3”.

Basic analysis of “qualitative” information

• Read through all the data.
• Organize comments into similar groups, e.g., concerns, suggestions, strengths, weaknesses, similar experiences, program inputs, recommendations, outputs, outcome indicators, etc.
• Label the groups or themes, e.g., concerns, suggestions, etc.
• Attempt to identify patterns, or associations and causal relationships in the themes, e.g., all people who attended programs in the evening had similar concerns, most people came from the same geographic area, most people were in the same salary range, what processes or events respondents experience during the program, etc.
• Keep all commentary for several years after completion in case needed for future reference.

Now Let us summarize

Interpretation is an integral part of any research. Attempts should be made to put the information in proper perspective, e.g., compare results to the expected results, original goals, especially if you are conducting a program evaluation; indications or measures of accomplishing outcomes or results, especially if you are conducting an outcomes or performance evaluation; description of the program’s experiences, strengths, weaknesses, etc. especially if you are conducting a process evaluation. One should record conclusions and recommendations in a report and associate interpretations to justify your conclusions or recommendations.

Web links

• https://research-methodology.net/research-methods/data-analysis/
• http://dspace.nwu.ac.za/bitstream/handle/10394/12269/Vosloo_JJ_Chapter_6.pdf?seque nce=7
• http://14.139.60.114:8080/jspui/bitstream/123456789/16776/1/047_Data%20Interpretati on%20and%20Report%20Writing%20(772-784).pdf
• www.kish.in/interpretation_and_report_writing/
• www.kish.in/interpretation_and_report_writing/
• http://study.com/academy/lesson/research-methodologies-quantitative-qualitative-mixed-method.html
• https://www.psychologynoteshq.com/qualitative-research-quantitative-research/
• http://managementstudyguide.com/qualitative-and-quantitative-research.htm
• http://dspace.tiss.edu/jspui/bitstream/1/7047/1/Research-MethodologyMethods-and-Techniques-by-CR-Kothari.pdf
• https://www.slideshare.net/annakittystefen/research-methodology-methodsandtechniquesbycrkothari