4. Processing of data involves editing, coding, classifying and tabulating. Explain each of these steps by taking an appropriate example. Answer:- Explanation / Examples Data editing: Data editing is the process that involves detecting and correcting errors (logical inconsistencies) in data. After collection, the data is subjected to processing. Processing requires that the researcher must go over all the raw data forms and check them for errors. The significance of validation becomes more important in the following cases: • In case the form had been translated into another language, expert analysis is done to see whether the meaning of the questions in the two measures is the same or not. • The second case could be that the questionnaire survey has to be done at multiple locations and it has been outsourced to an outside research agency. • The respondent seems to have used the same response category for all the questions; for example, there is a tendency on a five point scale to give 3 as the answer for all questions. Field Editing: Usually, the preliminary editing of the information obtained is done by the field investigators or supervisors who review the filled forms for any inconsistencies, non-response, illegible responses or incomplete questionnaires. Thus the errors can be corrected immediately and if need be the respondent who filled in the form, can be contacted again. The other advantage is that regular field editing ensures that one can also check that the surveyor is able to handle the process of instructions and probing correctly or not. Thus, the researcher can advise and train the investigator on how to administer the questionnaire correctly. Centralized in-house Editing: The second level of editing takes place at the researcher’s end at this stage there are two kinds of typical problems that the researcher might encounter. Coding: The process of identifying and denoting a numeral to the responses given by a respondent is called coding. This is essentially done in order to help the researcher’s in recording the data in a tabular form later. It is advisable to assigna numeric code even for the categorical data (e.g., gender). In fact, even for open-ended questions, which are in a statement form, we will try to categorize them into numbers. The reason for doing this is that the graphic representation of data into charts and figures becomes easier. Usually, the codes that have been formulated are organized into fields, records and files. For example, the gender of a person is one field and the codes used could be 0 for males and 1 for females. All related fields, for example, all the demographic variables like age, gender, income, marital status and education could be one record. The records of the entire sample under study form a single file. The data that is entered in the spreadsheet, such as on EXCEL, is in the form of a data matrix, which is simply a rectangular arrangement of the data in rows and columns. For example, consider the following representation from a study on two-wheeler buyers: Here, the data matrix reveals that each field is denoted on the column head and each case record is to be read along the row. The data in the first column represents the unique identification given to a particular respondent also marked on his/her questionnaire). The second column has data entered on the basis of a coding scheme where every occupation is given a number value (for example, 1 stands for government service and 5 stands for student and so on). Column 3 has 1 representing a motorcycle and 2 representing a scooter. The next value is of the average number of kilometers a person travels per day. Classification and Tabulation of Data: the researcher might decide to reduce the information into homogenous categories. This method of arrangement is called classification of data. This can be done on the basis of class intervals. Classification by class intervals: Numerical data, like the ratio scale data, can be classified into class intervals. This is to assist the quantitative analysis of data. For example, the age data obtained from the sample could be reduced to homogenous grouped data, for example all those below 25 form one group, those 25–35 are another group and so on. Thus, each group will have class limits—an upper and a lower limit. The difference between the limits is termed as the class magnitude. One can have class intervals of both equal and unequal magnitude. The decision on how many classes and whether equal or unequal depends upon the judgments of the researcher. Generally, multiples of 2 or 5 are preferred. Some researchers adopt the following formula for determining the number of class intervals: I = R/(1 + 3.3 log N) where, I = size of class interval, R = Range (i.e., difference between the values of the largest item and smallest item among the given items), N = Number of items to be grouped.
Posted on: Sun, 05 Oct 2014 08:27:05 +0000
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