✰ The data has to be processed and analysed after collecting it.
✰ Technically speaking, processing suggest. editing, coding, classification and tabulation of collected data so that they are amenable to analysis.
✰ The process of cleaning data is called editing
✰ The purpose of editing is to identify and minimize errors, miscalculations, misclassification or any gap in information provided by the respondent.
✰ Editing is the procedure that improves the quality of the data for coding.
✰ Depending upon how a variable has been measured in your research instrument. For coding, raw data can be categorized into either qualitative or quantitative.
3.Classification of data
Classification of data is a process of arranging data in groups or classes on the basis of important characteristics.
Classification According to Attributes.
Classification According to class intervals.
1.Classification According to Attributes.
✰ The data can be descriptive (Example: Gender, Literacy, religion, etc.) or numerical (Example: Weight, height, income, etc.).
2.Classification According to class intervals.
✰ Classification is done with data relating to income, age, weight, tariff, production, occupancy, etc. Such quantitative data are classified on the basis of class intervals. For example, people whose income is between 10,000 and 30,000 can form one group or class, those with income within 30,001 and 50,000 can form another group or class and so on.
✰ In Tabulation, the classified data are put in the form of tables.
✰ Tabulation is the process of summarising raw data and displaying the same in compact form (i.e., in the form of tables) for further analysis.
✰ In a broader sense, tabulation is an orderly arrangement of data in columns and rows.
Analysis of collected data in research:
✰ After tabulation, analysis is done with the help of different mathematical and statistical techniques, such as percentages, averages, coefficients of correlation, regression, and so on. It largely depends upon whether the data is qualitative or quantitative.
✰ The technique of analysis of variance can help us in analyzing whether three or more varieties of seeds grown on certain fields yield significantly different results or not. In brief, the researcher can analyze the collected data with the help of various statistical measures.
What is Parameter and statistics hypothesis ?
Characteristic of the population is called parameter.
Characteristic of the sample is called statistics.
✰ In statistical hypothesis, a parameter is a description of a population, while a statistics is a description of a sample.
If you ask every student in your class (population) about their marks, you get a parameter, because it is a true description about the population since every students was asked. Now, if you want to guess the average marks of students in your class (population) using the marks of few student (or sample), then you get statistics.