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14MBA23 – RM Notes

Research Methods

Module 5 – MBA – 2nd SemesterV T University Syllabus

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14MBA23 – Research Methods

Syllabus of Module 5:

Preparing the Data for Analysis: Editing, Coding, Classification, Tabulation, Validation Analysis and Interpretation.

(This module emphasizes on activities, for conversion of RAW DATA converted to a MEANINGFUL INFORMATION)

14MBA23 – RM – M5

Preparing the Data for Analysis: Editing, : Editing is the process of checking and

adjusting the data for omissions, legibility, and consistency. Editing may be differentiated from coding, which is the assignment of numerical scales or classifying symbols to previously edited data.

There are various variables, which need to be edited, they include:

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14MBA23 – RM – M5

Preparing the Data for Analysis: Editing, : Types of editing are:1. Validation Edits: The researcher checks its validity.,2. Logical Edits: The researcher satisfies, that two data

are not contradictory.,3. Consistency Edits: The researcher satisfies, that

‘Consistent or correct arithmetic relationship is found between data items.,

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14MBA23 – RM – M5

Preparing the Data for Analysis: Editing, : (Contd…)4. Range Edits: The researcher satisfies, that data values

fall in the acceptable range.,5. Variance Edits: The researcher satisfies, that that every

data has a uniform variances., (there exists no high variance between 2 variables).,

6. Micro-Editing and Macro-Editing: The researcher distinguish in order to calculate the range of edits, in his research.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Editing, : Hence editing is one of the most important

activity to eradicate, minimize, detecting and correcting errors in data. (Error Free Questionnaire and so on).

Editing helps the researcher to collect only those relevant data for his research and omit those which are irrelevant for his research.

Editing makes to deduct errors, fill the missing data and make the data complete for next step in research.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Coding, : Coding is translating answers into numerical

values or assigning numbers to the various categories of a variable to be used in data analysis.

Coding is done by using a ‘Code book’, ‘Code Sheet’, and a ‘Computer Card’.

Coding is done on the basis of the instructions given in the codebook. The code book gives a numerical code for each variable/ specification or description.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Coding, : Coding is usually done after examining the

each scale, and allocating the appropriate number stating the proper range between the different scales. In brief it can be said that ‘Coding’ is the conversion of verbal (Qualitative) values to numerical (Quantitative) values, therefore the study can be more specific with relevant values.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Time of Coding, :1.Before examining the data collected., (Applicable

in Deductive Hypothesis – Those research is based on Theory is the Deductive Hypothesis)

2.After examining the data collected. (Applicable in Inductive Hypothesis – Those study which are based on ased on Observation)

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14MBA23 – RM – M5

Preparing the Data for Analysis: Importance of Coding, :1.To analyze the data,2.To make the study meaningful,3.To obtain to quantitative results,4.To facilitate interpretation of the data collected,5.To arrive and make the conclusion more specific.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:Distribution of data as a form of classification of

scores obtained for the various categories or a particular variable.

There are four types of distributions, also four types of classifications stated as under:

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:In classification of the data, can be done in four categories

as follows:. 1.Geographical Classification,2.Qualitative Classification,3.Quantitative Classification,4.Chronological Classification

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:In classification of the data, can be done in four categories

as follows:. 1.Geographical Classification,: This classification can be

done in geographical manner, Ex: East Zone, West Zone, South Zone, North Zone and Central Zone (Best Example is the division of our Indian Railways)

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:In classification of the data, can be done in four categories

as follows:. 1.Qualitative Classification,: This classification is as per

the quality attributes like sense, sex, marital status, literate level and so on.

Qualitative classification can be single or multi-mode classification.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:In classification of the data, can be done in four

categories as follows:. 1.Quantitative Classification,: This classification is

based on quantitative attributes like weight, length, Meters, Age or any attributes which can be measured easily with numerical values.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Classification, : or The Data Classification:In classification of the data, can be done in four categories

as follows:. 1.Chronological Classification, This classification is based

on the arrangement of the data, which can be ascending or descending order.

In business operations, it is arranged or classified on year based, month or week based.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Data Classification, : These data can also be

distributed as follows: The distribution classification can be as follows:

1. Frequency distribution 2. Percentage distribution 3. Cumulative distribution 4. Statistical distribution

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14MBA23 – RM – M5

Preparing the Data for Analysis: Data Classification, : These data can also be distributed as

follows: The distribution classification can be as follows:

Frequency distribution, : Frequency wise distributing the group or ungroup.

Example for Grouped distribution can be All 1st MBA Students,

Example for Ungrouped distribution can be All MBA students.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Data Classification, : These data can also be

distributed as follows: The distribution classification can be as follows:

Percentage distribution, : This is classification of the population based on percentage.

Example for percentage distribution, can be calculated x% of students from Mysuru, and y% of students are non-Mysuru Students.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Data Classification, : These data can also be

distributed as follows: The distribution classification can be as follows:

Cumulative distribution, : This classification states the cumulative distribution of the population.

Example for cumulative distribution, can the Age, Income, Qualification cumulatively taken and divide the population of Mysuru City.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Data Classification, : These data can also be distributed as

follows: The distribution classification can be as follows:

Statistical distribution, : This is the classification of the population based on some kind of averages or Mean, Median and Mode.

Example for statistical distribution in a business unit can be divided into several business heads and collect the statistical data of several years for each business head. (Ex of Business Heads in each district of the state)

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14MBA23 – RM – M5

Preparing the Data for Analysis: Tabulation, After editing, which ensures that the

information on the schedule is accurate and categorized in a suitable form, the data are put together in some kinds of tables and may also undergo some other forms of statistical analysis. Table can be prepared manually and/or by computers. For a small study of 100 to 200 persons, there may be little point in tabulating by computer since this necessitates putting the data on punched cards.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Tabulation, Tabulation is the systematic presentation

of numerical data in rows and columns, classified each with its characteristics, which displays a accurate, clear and easy to understand the data represented in the form of table.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Importance of Tabulation, Tabulation facilitates the

researcher to represent the data, in a concise form to enable others to understand the same easily.

These include,1.Tabulation simplifies the complex data,2.Tabulation facilitates to identify each variables,3.Tabulation facilitates to compare the variables.

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14MBA23 – RM – M5Preparing the Data for Analysis:

Essentials of Tabulation, The essentials of Tabulation include;

1. Title of the table, (Main Head)

2. Caption, (Rows Caption and Columns Caption)

3. Box Head, (Column Caption is called Box Head)

4. Stub, (Row Caption is called the stub)

5. Body of the table, (Data represented in the table)

6. Prefatory or Head notes, (Appears between title and body, Ex: in units, in Rs)

7. Foot Notes, (Appears below the note, end of the page)

8. Source note, (Below the foot note to furnish the source of information)

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14MBA23 – RM – M5Preparing the Data for Analysis:

Types of Tabulation, : The types include,1.One way Tabulation or Simple Tabulation, This

table is classified with one characteristics only, Ex: Religion.

2.Two Way Tabulation or Double Tabulation, This table has two characteristics furnished. Ex: Religion and Sex, and

3.Multi Tabulation, This table has multiple variances in one table. Ex: Religion, Sex, Age, Literacy, Income and so on. 26

14MBA23 – RM – M5

Preparing the Data for Analysis: Validation, : Data Validation is the process of

checking the data base collected is accurate, clean and specific to the objective of the study.

Validation requires that the data need to be checked to confirm that the data collected can be verified and confirmed on the genuine data collected.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Types of Data Validation Techniques, :1.Form Level Validation,: This states that all the fields

required are filled by the user/ respondent, before submission.

2.Field Level Validation,: This checks the validity of the fields, Ex: A name can’t be in numbers, or a email can’t be without @ symbol and so on.

3.Data Savings Validation,: This verifies that the (column and rows) fields filled are been saved.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Types of Data Validation Techniques, :4. Search Criteria Validation, : This confirms that the

search engines depended and gathered information are true, and

5. Range Validation, : This verifies and checks that the values, characters or numbers fall on the specific range only.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Analysis of Data ,: Analysis of data is the process of

inspection, cleaning and transforming and modeling data with the goal of highlighting useful information, suggestions, conclusions and supporting decision making.

Data Analysis is a multi-facet approach, encompassing diverse techniques under a variety of names, in different business, science and social science domain.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Types of Data Analysis ,: 1.Uni-variate Data Analysis, : (Description of single

Variable and its attributes)2.Bi-variate Data Analysis,: (Description of two

Variables and its attributes – Independent and Dependent Variables)

3.Multi-Variate Data Analysis,: (Description of multi Variables and its attributes).

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14MBA23 – RM – M5

Preparing the Data for Analysis: Process of Data Analysis ,: Stage 1 - Data Cleaning, Stage 2 – Initial Data Analysis, Stage 3 – Checking the Quality of Data,Stage 4 – Measurement of Quality, Stage 5 – Initial Transformation, Stage 6 – Characteristics of Data Sample, Stage 7 – Final Stage of Initial Data Analysis.

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14MBA23 – RM – M5

Preparing the Data for Analysis: Interpretation, : Data Interpretation is the furnishing

the data collected into a descriptive form. This interpretation is done when the table or graph is prepared and the same need to be expressed in a paragraph.

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14MBA23 – Research Methods

End of Module 5Sanjeev Kumar Singh.,

MBA DEPARTMENT, V T UNIVERSITY

Mob: +91 91640 76660Email: harsubhmys@yahoo.co.in