Basic
Concepts of Quantitative Research
Dr. R. Ouyang
Results
Data types and preparation for analysis
Different kinds of data results represent different scales of measurement. There are four types of measurement scales, that is, there are four types of data we usually deal with. They are nominal, ordinal, interval and ratio. It is important to know which type of scale or data you collect for the research and which statistics are appropriate for your data analysis.
Four scales of measurement (4 types of data)
Nominal (categories): A nominal scale represents the lowest level of measurement. Such a scale classifies persons or objects into two or more categories. In other words, the nominal data are those based on the classification and categorization. When a nominal scale is used, the data simply indicate how many subjects are in each category. Category 4 and category 1 are not different base on the number 4 and 1, but the categories 4 and 1. 4 is not higher than 1 or more than 1. Example: Categories for IQ, types of school.
Ordinal (ranks): An ordinal scale puts the subjects in order from the highest to lowest, form the most to least. Although ordinal scales indicate that some subjects are higher, or better, than other, they do not indicate how much higher or better. Subjects A, B, C, D are measured as 4'5", 5'1", 6'2", 5'6" in height. The rank order will be ranked 1 for C, 2 for D, 3 for B, and 4 for A.
Interval (scores): An interval scale has all the characteristics of a nominal and ordinal scale, in addition it is based upon predetermined equal intervals. Most of the tests used in educational research, such as achievement tests, aptitude tests, and intelligence tests, represent interval scales. Interval scale, however, do not have a true zero point. Such scales typically have an arbitrary maximum score and an arbitrary minimum score, or zero point. If an IQ test produces scores ranging from 0 to 200, a score of 0 does not indicate the absence of intelligence, nor does a score of 200 dedicate possession of the ultimate intelligence. A score of 0 only indicates the lowest level of performance possible on that particular test and a score of 200 represents the highest level. We can say that an achievement test score of 90 is 45 points higher than a score of 45, but we cannot say that a person scoring 90 knows twice as much as a person scoring 45. Similarly, a person with a measured IQ of 140 is not necessarily twice as smart or twice as intelligent as a person with a measured IQ of 70.
Ratio: A ratio scale represents the highest, most precise, level of measurement. A ratio scale has all the advantages of the other types of scales and in addition it has a meaningful, true zero point. Height, weight, time, distance, and speed are examples.
Procession of coding data
Scoring procedure: All instruments administered should be scored accurately and consistently; each subject's test should be scored using the same procedures and criteria.
For self-developed test, if other than objective-type items (such as multiple-choice questions) are to be scored, it is advised to have at least one other person score the tests as a reliability check.
For a standardized test, it is better to make sure all answer sheets are marked corrected and scored by the machine properly.
Coding data: Coding data consists of developing a system by which the data and identification information are specified and organized in preparation for the analysis.
If a large number of subjects are involved, coding of the data is especially important. Data for all variables and subjects are usually converted to numerical values when the data are entered into the database management program since long entries take considerable space and contribute to typographical and spelling errors that mess up subsequent manipulations.
Steps of coding data: 1) to give each subject an ID number, 2) to make decisions as to how nonnumerical or categorical data will be coded, 3) to prepare all data for analysis.
Statistical packages (SPSS), (SAS), (JUMP-IN) include programs for many statistics, from the most basic to the most sophisticated, frequently used in research studies.
Reference:
Gay, L. R. (1996). Educational research: Competencies for analysis and application. Upper Saddle River, NJ: Merrill.