RM-Data Preparation

Data Preparation:

Data Preparation

Data Preparation: Introduction:

Data Preparation: Introduction It is the activity that ensures the accuracy of the data and their conversion from raw form to reduced and classified forms that are more appropriate for analysis. It is during this step that data entry errors may be revealed and corrected 2

PROCESSING OPERATIONS:

PROCESSING OPERATIONS 3

Data Editing:

Data Editing The first step in analysis is to edit the raw data. Editing detects errors and omissions, corrects them when possible, and certifies that maximum data quality standards are achieved. 4

Data Editing:

Data Editing The editor’s purpose is to guarantee that data is: Accurate; Consistent with the intent of the question and their information in the survey; Uniformly entered; Complete; and Arranged to simplify coding and tabulation. 5

Types of Editing:

Types of Editing Two types of editing are : FIELD EDITING CENTRAL EDITING. Field Editing : In large projects, field editing review is the responsibility of the field supervisor; Central Editing : For a small study, the use of a single editor produces maximum consistency. In large studies, editing tasks should be allocated so that each editor deals with one entire section. . 6

Data Coding:

Data Coding Data Coding means assigning a code to each possible response of each question. Usually a code is a number or other symbols so that the responses can be grouped into a limited number of categories. In coding, categories are the partitions of a data set of a given variable.. Both closed and free-response questions must be coded. 7

Importance of Coding:

Importance of Coding The categorization of data sacrifices some data detail but is necessary for efficient analysis . Most software programs work more efficiently in the numeric mode; Instead of entering the word male or female in response to a question that asks for the identification of one’s gender, we would use numeric codes, e.g., 0 for male and 1 for female Numeric coding simplifies the researcher’s task in converting a nominal variable, like gender, to a “dummy variable” 8

Data Classification:

Data Classification Data classification is the categorization of raw data into homogeneous groups having common characterstics for its most effective and efficient use. Classification can be of two types: Classification according to attributes Classification according to class intervals 9

Missing Data:

Missing Data In survey studies, missing data typically occur when participants accidentally skip, refuse to answer, or do not know the answer to an item on the questionnaire. In longitudinal studies, missing data may result from participants dropping out of the study, or being absent for one or more data collection periods. Missing data also occur due to researcher error, corrupted data files, and changes in the research or instrument design after data were collected from some participants, such as when variables are dropped or added. 10

Treatment of Missing Data:

Treatment of Missing Data The strategy for handling missing data consists of two-step process: the researcher first explores the pattern of missing data to determine the mechanism for missingness (the probability that a value is missing rather than observed), and then selects a missing-data technique. The three basic types of techniques which can be used to salvage data sets with missing values are: Listwise deletion Pairwise deletion Replacement of missing values with estimated scores 11

Tabulation of Data:

Tabulation of Data The process of placing classified data into tabular form is known as tabulation. A table is a symmetric arrangement of statistical data in rows and columns. Rows are horizontal arrangements whereas columns are vertical arrangements. is the process of summarizing raw data and displaying the same in compact form (i.e., in the form of statistical table) for further analysis 12

Importance of Tabulation:

Importance of Tabulation When mass data has been assembled, it becomes necessary for the researcher to arrange the same in some kind of concise logical order. Tabulation is essential because: It conserves space and reduces explanatory and descriptive statement to a minimum It facilitates the process of comparison It facilitates the summation of items and the detection of errors and omissions It provides a basis for various statistical computations. 13