Concept of Data Collection | CHAPTER-7 | Research Methodology

Concept of Data Collection- In its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal, like acquiring knowledge or verifying knowledge claims. This normally involves various steps, like choosing a sample, collecting data from this sample, and interpreting the data. The study of methods concerns a detailed description and analysis of these processes. It includes evaluative aspects by comparing different methods.

In this way, their benefits and drawbacks are evaluated, as well as the research goals for which they may be used. These descriptions and evaluations are predicated on philosophical background assumptions; examples include how to conceptualize the phenomena under study and what constitutes evidence in favor of or against them. In its broadest sense, methodology encompasses the discussion of these more abstract issues.

Concept of Data Collection

Definition of Data collection:

According to WHO (World Health Organization)

Data collection is defined as the ongoing systematic collection, analysis, and interpretation of health data necessary for designing, implementing, and evaluating public health prevention programs.

Or

Data collection is the systematic approach to gathering and measuring information from a variety of sources to get a complete and accurate picture of an area of interest.

Or

Data collection may be defined as systemic collection of data for a particular purpose from various sources including

• Questionnaires,
• Interviews,
• Observations,
• Existing records, and
• Electronics device

Different Types/Method of Data Collection:

There are several methods of data collection. Importance ones are:

Importance of Data Collection:

According to Carpenito (1993) data collection focuses on identifying the client’s 

1. Present and past health status.

2. Present and past coping patterns (strengths and limittions0).

3. Present and past functional status.

4. Response to therapy (nursing, medical)

5. Risk for potential problems.

6. Desire for a higher level of wellness.

Erickson et al (1983) explains the purposes of data collection as follows:

1. To develop an overview of the client’s situation from the client’s perspective.

2. To develop an understanding of the clients personal orientation in terms of the clients expectations for the present and future.

3. To determine the nature of the external support system.

4. To determine the client’s strengths and virtues.

5. To determine the client’s currently available internal sources.

6. To determine the current developmental status in order to understand the clients’ personal model and to utilize maximum communication skills.

[Ref-AM Rajinikant/1″ /20]

Data Collection Tool/ Instrument:

1. Box and whisker plot

2. Check sheet

3. Control chart

4. Design of experiments

5. Histogram

6. Scatter diagram

7. Stratification

8. Survey or Questionnaires

9. Case studies

10. Interviews

11. Observations

Distinctions between data collection methods & data collection tools:

Definition of 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) various 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”..

Or

“Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making

[Ref.www.wikipedia.com]

Data Interpretation:

Data interpretation is the process of assigning meaning to the collected information and determining the conclusions, significance, and implications of the findings.

Or

Data înterpretation is the process of making sense of numerical data that has been collected, analyzed and presented.

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

(Ref- Dr. Md. Zahid Hossain Sharif/1st)

Bias in Data/Information Collection:

Bias in data or information collection is a distortion that results in the information not being representative of the true situation.

Possible sources of bias during data collection are:

A. Defective instruments:

• Questionnaires with fixed or closed questions on topics about which too little is known.
• Open-ended questions without guidelines on how to ask (or to answer) the questions.
• Vaguely phrased questions or questions placed in an illogical order.
• Weighing scales that are not standardized.

These sources of bias can be prevented by carefully planning the data-collection process and by pretesting the data-collection tools.

B. Observer bias:

• Observer bias can occurs during observation or loosely structured group or individual interviews. There is a risk that the data collector will see only things in which he or she is interested or will miss information that is crucial to the research.
• Observation protocols and guidelines for conducting loosely structured, interviews should be prepared and training and practice should be provided to data Collectors in using these tools.

C. Effect of the interview on the respondent:

This is a possibility in all interview situations. The respondent may mistrust the intention of the interviewer and dodge certain questions or may give misleading answers.

[Ref- Dr. Md. Zahid Hossain Sharif/1/19, 20]

Ethical Issues in Collecting Data:

As we develop our data-collection techniques, we need to consider whether our research procedures are likely to cause any physical or emotional harm to research subjects. Harm may be caused by for example-

➤By violating research subject’s right to privacy by posing sensitive questions or by gaining access to records that may contain personal data.

➤Observing the behavior of the respondents without their being awareness or

➤Failing to observe or respect certain cultural values, traditions or taboos of the respondents.

Several methods for dealing with these ethical issues are as follows:

➤ Obtaining informed consent before the study or interview begins.

➤Not exploring sensitive issues before a good relationship has been established with the research participant.

➤Ensuring the confidentiality of the data obtained from research participants.

➤ If sensitive questions are asked, for example about sexually transmitted diseases, it may be advisable to omit names and addresses from the questionnaires.

➤ Respecting culture, customs and values of the society

Data Processing

Definition of Data Processing:

Data processing or information processing is the process of editing, organization and coding of data so that it becomes amenable for analysis and interpretation.

(Ref by- Nirmala V/Research Methodology in Nursing/1/152)

Data processing is, generally, “the collection and manipulation of items of data to produce meaningful information.”

Types of Data Processing;

1. Automatic or electronic data processing:

• The operations are performed by a computer.

2. Distributed data processing:

• Some or all the operations are performed in different locations at computer facilities connected by telecommunication sites.

3. Manual data processing:

• The operations are performed manually.

(Ref by- Nirmala V/Research Methodology in Nursing/1/152)

Steps of Data Processing

Editing (Scrutinizing raw data)

↓

Organization (Grouping the data)

↓

Coding
(Assigning numerical values)
↓

Analysis
(Examination of tabulated material)

↓

Interpretation
(Drawing conclusion)

↓

Presentation of data

(Ref by- Nirmala V/Research Methodology in Nursing/1/152)

Data Presentation

Definition of Data presentation

Data presentation is the method by which people summarize, organize and communicate information using a variety of tools, such as diagrams, distribution charts, histograms and graphs.

Method of Data Presentation:

(Ref by- Handout)

Pre-Testing

A pretest is a test given to measure the outcome variable before the experimental manipulation is implemented.

Or

Pre-testing may be defined as –

“A preliminary test administered to determine a student’s baseline knowledge or prepare dens for an educational experience or course of study.”

Need of Pre-Testing:

• Pretesting is a very important part of the questionnaire construction process is its piloting. This involves testing the research instrument in conditions as similar as possible to the research, but not in order to report results but rather to check for glitches in wording of questions, lack of clarity of instructions etc. in fact, anything that could impede the instrument’s ability to collect data in an economical and systematic fashion.
• Pretests should be conducted systematically, with potential respondents and using the same method of administration. The temptation to hurry over them, using just a convenience sample, should be avoided.
• It is also beneficial to pretest the questionnaire with specialists in question construction, who may be able to pick up potential difficulties which might not be revealed in a pretest with respondents.
• If there are a variety of respondent types, all should be included in the pretest, and if the questionnaire is to be in several languages, it should be tested in each language.

Editing

Definition of Editing

Editing involves examining each completed questionnaire to ensure that the proper sequence of questions has been asked, the answers are clear and consistent, and they have been correctly marked.

Or

Editing prepares a written work for publication. An editor checks for completeness, accuracy, consistency, word choice, writing style and spelling errors. While a writer may accept, nego..ate or reject individual edits, the efforts of an editor always enhance the final product.

Data Editing

According to UNICEF (United Nations International Children’s Emergency Fund) Data editing is defined as the process involving the review and adjustment of collected survey data. The purpose is to control the quality of the collected data.

Or

According to OECD (Organization for Economic Co-operation and Development) Data editing is the activity aimed at detecting and correcting errors (logical inconsistencies) in data.

Data Cleaning

Data cleaning may be defined as correction or removal of erroneous (dirty) data caused by contradictions, disparities, keying mistakes, missing bits, etc. It also includes validation of the changes made, and may require normalization

Or

Data cleaning involves the detection and removal (or correction) of errors and inconsistencies in a data set or database due to the corruption or inaccurate entry of the data. Incomplete, inaccurate or irrelevant data is identified and then either replaced, modified or deleted.

Table

Definition of Table:

A table is an arrangement of data in rows and columns, or possivly in a more complex structure. Tables are widely used in communication, research, and data analysis.

Parts of Table:

In general, a statistical table consists of the following eight parts. They are as follows:

1. Table Number: Each table must be given a number. Table number helps in distinguishing one table from other tables. Usually tables are numbered according to the order of their appearance in a chapter. For example, the first table in the first chapter of a book should be given number 1.1 and second table of the same chapter be given 1.2 Table number should be given at its top or towards the left of the table.

2. Title of the Table: Every table should have a suitable title. It should be short & clear. Title should be such that one can know the nature of the data contained in the table as well as where and when such data were collected. It is either placed just below the table number or at its right.

3. Caption: Caption refers to the headings of the columns. It consists of one or more column heads. A caption should be brief, concise and self-explanatory, Column heading is written in the middle of a column in small letters.

4. Stub: Stub refers to the headings of rows.

5. Body: This is the most important part of a table. It contains a number of cells. Cells are formed due to the intersection of rows and column. Data are entered in these cells.

6. Head Note: The head-note (or prefactory note) contains the unit of measurement of data. It is usually placed just below the title or at the right hand top corner of the table

7. Foot Note: A foot note is given at the bottom of a table. It helps in clarifying the point which is not clear in the table. A foot note may be keyed to the title or to any column or to any row heading. It is identified by symbols such as *,+@,£ etc.

8. Source Note: The source note shows the source of the data presented in the table. Reliability and accuracy of data can be tested to some extent from the source note. It shows the name of the author, title, volume, page, publisher’s name, year and place of publication of the book or journal from which data are complied.

Different Uses of Table:

There are several specific situations in which tables are routinely used as a matter of custom or formal convention.

A. Publishing

• Cross-reference (Table of contents)

B. Mathematics

Main article: Mathematical table

• Arithmetic [(Multiplication table)]
• Logic [(Truth table)]

C. Natural sciences

• Chemistry (Periodic table)
• Oceanography (tide table)

D. Information technology

E. Software applications

Modern software applications give users the ability to generate, format, and edit tables and tabular data for a wide variety of uses, for example:

• word processing applications;
• spreadsheet applications;
• presentation software;
• tables specified in HTML or another markup language

General Rules of Tabulation

• A table should be simple and attractive. There should be no need of further explanation (details).
• Proper and clear headings for columns and rows are necessary.
• Suitable approximation may be adopted and figures may be rounded off.
• The unit of measurement should be well defined.
• If the observations are large in numbers they can be broken into two or three tables.
• Thick lines should be used to separate the data under big classes and thin lines to separate the sub classes of data.

Read more:

• Research Methodology | Diploma in Nursing Science and Midwifery