Processing of dataLecture 4

By

Professor Nasir Zamir Qureshi

Department of Commerce,

Aligarh Muslim University, Aligarh

Processing of data

Processing of data includes:

1. Editing of data.

2. Codification of data.

3. Classification of data.

4. Tabulation of data.

5. Presentation of data.

6. Analysis of data.

7. Interpretation of data.

Editing of data• After the data collection, there may be some possible

errors in survey data that hinders it to be presented asinformation.

• The process of analysis and evaluation of the data withthe objective of detecting the errors and outliers (aliensto rest of the data) and then rectifying the data.

• Data editing can be carried out by using a computer ormanually or the combination of the two.

Editing of data

Need for data editing:

1. Misunderstanding the question by the respondent.

2. Misunderstanding the response by the interviewer.

3. Inaccurate response by the respondent.

4. Checking the wrong response by the interviewer or respondent.

Editing of data

Objectives of data editing:

1. To verify whether the data are complete or not.

2. To detect the errors and outliers and fix them.

3. To ensure accuracy of the data.

4. To ensure the coherency of the data.

5. To validate the outputs to be presented as data.

Editing of data

Data editing types:

1. Data completeness and validity.

2. Data duplication edits.

3. Data range edits.

4. Consistency edits.

Codification of data

• The process of refining the collected data can be very hecticwhen it is in large numbers like the birth and death rates ofthe 29 states of India, the data of thousands of employeesin a firm, number of voters in India etc.

• Data coding has been very helpful to ease such a hecticprocess of Data analysis by converting the large quantitiesof information into a form that can be more easily handled,especially by computer programs.

• A code is a short word interpreting both the context andmeaning of the long responses of the respondents.

Codification of data

• The purpose of coding the data is not just to eliminatethe excessive data but to summarize it meaningfully.

• In softwares like SPSS etc, the data needs to be changed into the form of matrix with rows (containing data) and columns (containing variables)

• Example: worksheets of the employees in a company.

Classification of data

• Data classification, which is defined as “sorting or arrangingentries or data in a class or group based on their commoncharacteristics” helps to ease the process of statistical operation.

• After classification, we get groups containing data relevant toeach other in some respect.

• For example, in the process of admission in various departmentsin a college, The applications received are sorted according tothe faculties and then according to the departments in thatcollege e.g., sorting the applications into the faculties ofagricultural sciences, engineering sciences, science etc.

Classification of data

Objectives of the classification:

• To convert the large data into simplified and narrowed down data.

• To present the data easily in comprehensive and understandable form.

• To make the entries fairly comparable.

• To help to draw conclusions.

• To ease the process of data analysis.

• To make the tabulation feasible.

Classification of data

Guidelines for the data classification:

• Each and every class must contain the entries that are homogeneous.

• An entry must be present only in one class.

• There should not be any ambiguity in the definition of classes.

• The class intervals should be as far as of equal size

• The classification should be flexible enough to accept and accommodate new entries.

• Ease for data location.

Classification of data

Types of classification:

1. Chronological classification

2. Geographical classification

3. Quantitative classification

4. Qualitative classification

5. Simple and manifold classification

Tabulation of data

• After classification, the huge data needs to be presentedin an organized and sequential manner which is donewith the help of tabulation.

• Tabulation is defined as the process of systematicpresentation of the grouped or classified data in the formof a table so that it is clearly understood and analyzed.

• It helps in the process of comparison and also depictssome patterns contained in the data.

Tabulation of data

Objectives of tabulation:

1. Compare the data

2. Understand the data easily

3. Simplify the data

4. Use for future reference.

Tabulation of data

Parts of a table:

• Caption: It is the upper part of the table.

• Box head: this is the whole upper part of table.

• Stub: It defines each and every row of the table. It isthe left side of the table.

• Body: Body is the main part of the table containingthe substance for which the table is created.

Presentation of data• Conveying the data to the readers in an easy and

convenient form so that they can interpret it easily andquickly with least effort and time is called as presentationwhich is done by various methods.

• For time saving purpose, graphs and charts have beendeveloped for the data presentation.

• Graphs and charts are the effective visual tools thatfurnish the information at a glance and are alsoattractive. Moreover graphs depict trends andrelationships within the data e.g., time dependentchanges, correlation and frequency distribution.

Presentation of data

Data can be presented through:

1. Bar charts

2. Pie chart

3. Histogram

Analysis of data

• Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making.

• Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same.

• There are various approaches with different techniques for the data analysis.

Analysis of data

Phases of data analysis:

• Data cleaning: this is the first process of data analysis where recordmatching, duplication, and column segmentation are done to cleanthe raw data from different sources.

• Quality analysis: Using frequency counts, descriptive statistics suchas mean, median, standard deviation, normality histograms such asskewness, kurtosis etc.

• Analysis: There are many analyses which can be done during theinitial phase such as Univariate, Bivariate, Scatter plots,Associations, Hierarchical log linear analysis etc.

Analysis of data

• Quality of measurement: using confirmatory factor analysisand Analysis of homogeneity.

• Analysis with statistical techniques: T-test, ANOVA,ANCOVA, MANOVA, MANCOVA, Ordinary linear regression,f-test and multiple linear regressions.

• Data analysis approaches: Discourse analysis, contentanalysis, cross cultural analysis etc.

• Data analysis softwares: R software, Orange Data Mining,SPSS, MATLAB, Python etc.

Interpretation of data

• Interpretation of data refers to the task of 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:

• The effort to establish continuity in research through linking the results of a given study with those of another, and

• The establishment of some explanatory concepts.

Thank you