23 Data Management, Analysis and Interpretation

Ms. Tongbram Rubyrani Devi

Contents:

1 Introduction

2 Data Management

2.1 Concepts of Data Management

2.2 Data Management Planning

3 Data Analysis

3.1 Elements or Types of Analysis

3.2 Consideration/Issues in Data Analysis

4 Interpretation

4.1 Technique of Interpretation

4.2 Precautions in Interpretation

Summary

Learning Objectives:

•  To introduce the basic concept, meaning and scope of data management, analysis and interpretation and
•  To understand the general rules of appropriate data management, analysis and interpretation in accordance with responsible conduct of research.

1. Introduction

Research is search for knowledge. One can also define research as a scientific and systematic search for pertinent information on a specific topic. In fact, research is an art of scientific investigation. The Advanced Learner’s Dictionary of Current English lays down the meaning of research as “a careful investigation or inquiry especially through search for new facts in any branch of knowledge.” Redman and Mory (1923) define research as a “systematized effort to gain new knowledge.” Some people consider research as a movement, a movement from the known to the unknown. It is actually a voyage of discovery. We all possess the vital instinct of inquisitiveness for, when the unknown confronts us, we wonder and our inquisitiveness makes us probe and attain full and fuller understanding of the unknown. This inquisitiveness is the mother of all knowledge and the method, which man employs for obtaining the knowledge of whatever the unknown, can be termed as research (Kothari, 2004).

There are two basic approaches to research; quantitative approach and qualitative approach. After confirming the concerned approach, a research plan is need to be processed under which data management, analysis and interpretation comes to fulfil the prescribed research work. But before reviewing the plan, the term “data” should be defined. As per Merriam Webster Dictionary, data is a “factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation.” In other words data are any information or observations that are associated with a particular project including experimental specimens, technologies and products related to the inquiry.

2. Data Management

“Research data management concerns the organization of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. It aims to ensure reliable verification of results, and permits new and innovative research built on existing information.” (http://www2.le.ac.uk/services/researchdata/rdm/whatisrdm). Data management in research encompasses all aspects of looking after, handling, organizing and enhancing research data. Managing data well enhances the scientific process, ensures high quality data and also increases the longevity of data and opportunities for data to be shared and re-used.

According to the den Eynden et al. (2010), for each type of investment evaluated data management practices are organized into relevant topical areas:

– data management planning

– ethics, consent and confidentiality when managing and sharing research data

– data copyright and rights management

– contextualising, describing and documenting data

– data formats and software data storage, back-up and security

– roles and responsibilities of data management

Data management is a general term that covers a broad range of data applications. It may refer to basic data management concepts or to specific technologies. Some notable applications of data management includes –

(i) Data design (or data architecture)

It refers to the way data is structured. For example, when creating a file format for an application, the developer must decide how to organize the data in the file. For some applications, it may make sense to store data in a text format, while other programs may benefit from a binary file format. Regardless of what format the developer uses, the data must be organized within the file in a structure that can be recognized by the associated program.

(ii) Data storage

It refers to the many different ways of storing data. This includes hard drives, flash memory, optical media, and temporary RAM storage. When selecting an appropriate data storage medium, concepts such as data access and data integrity are important to consider. For example, data that is accessed and modified on a regular basis should be stored on a hard drive or flash media. This is because these types of media provide quick access and allow the data to be moved or changed. Archived data, on the other hand, may be stored on optical media, such as CDs and DVDs, since the data does not need to be changed. Optical discs also maintain data integrity longer than hard drives, which makes them a good choice for archival purposes.

(iii) Data security

It involves protecting computer data. Many individuals and businesses store valuable data on computer systems. In order to secure the data, it is wise to take steps to protect the privacy and integrity of the data. It can be achieved by some steps which include installing a firewall to prevent unauthorized access to computer and encrypting personal data that is submitted online or shared with other users. It is also important to backup the data regularly so that it may help in recovering files in case of primary storage device fails (https://techterms.com/definition/data_management).

2.1 Concepts of Data Management

The concept of research data management is based on the planning, collecting, organizing, managing, storage, security, backing up, preserving and sharing of data. The key concepts of data management which are related to the conduct of research are –

2.2 Data Management Planning

Data managing planning is the structured way of thinking about the research data such as what type of data to be collected, format of the data, ways of data storage, methods of assessing data etc. A systematic diagram representing data management planning is shown in Figure 1.

3. Data Analysis

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003), analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”.

While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye and Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, unobtrusive research) and the form of the data (field notes, documents, audiotape, videotape). An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to  analysis of non-statistical data as well. (https://ori.hhs.gov/education/products/n_illinois_ u/datamanagement/datopic.html).

Regarding qualitative and quantitative analysis of data, Kreuger and Neuman (2006) offer a useful outline of the differences and similarities between qualitative and quantitative methods of data analysis. According to them, qualitative and quantitative analyses are similar in four ways. Both of the methods involve:

Inference – the use of reasoning to reach a conclusion based on evidence Public method or process – revealing their study design in some way Comparison as a central process – identification of patterns or aspects that are similar or different

Striving to avoid errors, false conclusions and misleading inferences.

The core differences between qualitative and quantitative data analysis according to Kreuger & Neuman (2006) are as follows –

Qualitative data analysis is less standardised with the wide variety in approaches to qualitative research matched by the many approaches to data analysis, while quantitative researchers choose from a specialised, standard set of data analysis techniques

The results of qualitative data analysis guide subsequent data collection, and analysis is thus a less-distinct final stage of the research process than quantitative analysis, where data analysis does not begin until all data have been collected and condensed into numbers

Qualitative researchers create new concepts and theory by blending together empirical and abstract concepts, while quantitative researchers manipulate numbers in order to test a hypothesis with variable constructs

Qualitative data analysis is in the form of words, which are relatively imprecise, diffuse and context based, but quantitative researchers use the language of statistical relationships in analysis. (http://www.dspace.nwu.ac.za.)

3.1. Elements/Types of Analysis

Analysis is the computation of certain indices or measures along with searching for patterns of relationship that exist among the data groups. In survey or experimental data, analysis involves estimating the values of unknown parameters of the population and testing of hypotheses for drawing inferences. Analysis may, therefore, be categorised as descriptive analysis and inferential analysis. Descriptive analysis is largely the study of distributions of one variable. Data analysis may be in respect of one variable (uni-dimensional analysis), or in respect of two variables (bivariate analysis) or in respect of more than two variables (multivariate analysis). Data can also be analysed to see the correlation between two or more variables as well as to know how one or more variables affect changes in another variable. Such analyses are known as correlation analysis and causal analysis respectively. The causal analysis is considered relatively more important in experimental researches, whereas in general most of the social science researches correlation analysis as relatively more important. Regression analysis is also used to understand the functional relationships existing between two or more variables, if any

In modern times, with the availability of computer facilities, there has been a rapid development of multivariate analysis which may be defined as “all statistical methods which simultaneously analyse more than two variables on a sample of observations”. Usually the following analyses are involved when we make a reference of multivariate analysis:

(a) Multiple regression analysis

Multiple regression analysis is adopted when the researcher has one dependent variable which is presumed to be a function of two or more independent variables. The objective of this analysis is to make a prediction about the dependent variable based on its covariance with all the concerned independent variables.

(b) Multiple discriminant analysis

Multiple discriminant analysis is appropriate when the researcher has a single dependent variable that cannot be measured, but can be classified into two or more groups on the basis of some attribute. The

object of this analysis happens to be to predict an entity’s possibility of belonging to a particular group based on several predictor variables.

(c) Multivariate analysis of variance (or multi-ANOVA)

Multivariate analysis of variance (or multi-ANOVA) is an extension of two ways ANOVA, wherein the ratio of among group variance to within group variance is worked out on a set of variables.

(d) Canonical analysis

This analysis can be used in case of both measurable and non-measurable variables for the purpose of simultaneously predicting a set of dependent variables from their joint covariance with a set of independent variables (Kothari, 2004).

3.2 Considerations/Issues in Data Analysis

There are a number of issues that researchers should be cognizant of with respect to data analysis.

These include:

– Having the necessary skills to analyze

– Concurrently selecting data collection methods and appropriate analysis

– Drawing unbiased inference

– Inappropriate subgroup analysis

– Following acceptable norms for disciplines

– Determining statistical significance

– Lack of clearly defined and objective outcome measurements

– Providing honest and accurate analysis

– Manner of presenting data

– Environmental/contextual issues

– Data recording method

– Partitioning ‘text’ when analyzing qualitative data

– Training of staff conducting analyses

– Reliability and Validity

– Extent of analysis

(Source: https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/datopic.html)

4. Interpretation

Interpretation is drawing inferences from the collected facts after an analytical and/or experimental study. In fact, it is a search for broader meaning of research findings. The task of interpretation has two major aspects viz.,

(i) the effort to establish continuity in research through linking the results of a given study with those of another and

(ii) the establishment of some explanatory concepts.

Interpretation also extends beyond the findings of the study to include the results of other related studies. Thus, interpretation helps in better understanding of the factors that explain the observed findings in the study. Moreover, it also provides a theoretical conception which can serve as a guide for further researches. The usefulness and utility of research findings lie in the proper interpretation of the findings. It is being considered a basic component of research process because of the following reasons:

•  Through interpretations, researchers can understand the abstract principle that works beneath his findings and can link the findings with other studies. It may also help in predicting the concrete events.
•  Interpretation helps in establishment of explanatory concepts and opens new avenues of intellectual adventure.
•  Interpretations can make others to understand the real significance of the research findings
•  Interpretation of exploratory research findings results into hypothesis for experimental research.

4.1. Technique of Interpretation

Interpretation in research requires a great skill and dexterity on the part of researcher. It is learned through practice and experience. According to Kothari (2004), the technique of interpretation often involves the following steps such as –

• (i) Reasonable explanations should be given for the findings to interpret relationship in the variables considered in the study, if any. In fact, interpretation is the technique of how generalization should be done and concepts be formulated.
• (ii) Extraneous information, if 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) Complete interpretation should be given only after considering all relevant factors affecting the problem to avoid false generalization.
• (iv) Consultation from subject experts are advisable before embarking upon final interpretation since it may result in correct interpretation and, thus, will enhance the utility of research results.

4.2. Precautions in Interpretation

One should always remember 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) Researcher must invariably self satisfy that the data are appropriate, trustworthy, adequate for drawing inferences, reflect good homogeneity and proper analysis has been done through appropriate statistical methods.
• (ii) The researcher must remain cautious about the errors that can possibly arise in the process of interpreting results. For example, 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”.
• (iii) Researchers should be well equipped with and must know the correct use of appropriate statistical measures for drawing inferences concerning the study.
• (iv) Researchers should always remember that analysis and interpretation of the data are correlated which cannot be distinctly separated. Interpretations of data are solely dependent on the outcome of data analysis. Therefore, precautions should be taken during the process of analysis viz precautions concerning the reliability of data, computational checks, validation and comparison of results.
• (v) Researchers should always identify the potential risk factors that are initially not visible besides observing the occurrences.
• (vi) 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 between theoretical orientation and empirical observation that opportunities for originality and creativity lie” (Kothari 2004).

Summary

Research refers to search of knowledge in a scientific and systematic manner. Redman and Mory (1923) define research as a “systematized effort to gain new knowledge.” There are two basic approaches to research viz, quantitative approach and qualitative approach. To fulfil the desired research work a research plan is needed, under its process data management, analysis and interpretation comes. Data is factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation as defined in Merriam Webster Dictionary. In other words data are any information or observations that are associated with a particular project including experimental specimens, technologies and products related to the inquiry. The management of data is a part of the research process which aims to make the process as efficient as possible to meet the expectations and requirements of the desired goal of the research work. It encompasses all aspects of looking after, handling and organizing research data. Moreover, it is an integral part of research work as it describes how research data are collected or created, used and stored during research as well as made accessible for others after the research has been completed. On the other hand, data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Analysis, particularly in case of survey or experimental data, involves estimating the values of unknown parameters of the population and testing of hypotheses for drawing inferences. Analysis may, therefore, be categorized as descriptive analysis and inferential analysis or statistical analysis. The last and most important part of the research is how to interpret the data. Data  interpretation is drawing inferences from the collected facts after an analytical or experimental study which literally means search for broader meaning for research findings. It is to establish continuity in research through comparing the results of the given study with that of secondary findings of the same.