Variables in social science research and its measurement ppt

variables in social science research and its measurement describes the various types of variables in social sciences with examples and the measurement of variables.

1. Presented by : ABHIJEET SATPATHY 1 st Year Ph.D. Department of Extension Education, College of Agriculture, OUAT, Bhubaneswar DEPARTMENTAL SEMINAR ON “USE OF VARIABLES IN SOCIAL SCIENCE RESEARCH”

2. Concept Construct

3. Variable: “ Attribute or quality that could differ in magnitude” Or “A symbol to which numerals or values are assigned.” Or “A variable is something which varies and can have more than one value.” Example: age, intelligence, income, land holding, risk orientation, innovation proneness, extent of adoption, etc. Constant: Property of objects or things which cannot vary in any situation. Example: Gender is constant in a study related to women empowerment

4. The variables selected for study should be – According to the objectives of the study. Mutually exclusive and not overlapping According to the level of understanding of the researcher. Measurable by the available techniques or suitable technique could be developed for the same. Could be classified into some categories. Limited in number so as to avoid confusion and could be studied with that resource and time available. Variables selected for study should be labelled, such as y for dependent variables and x1, x2, x3….. Xn for the independent variables

5. Academic performance Attitude towards study Study habits Learning style Family background Self efficacy and motivation

6. Independent variable : It is a variable hypothesized to cause or explain variation in another variable (i.e., “ Influencer ”). Or The variable from which predictions are made is known as independent variable. It is the presumed cause of the dependent variable. Example : Age(years), land holding (number of acres of land owned), income (rupees earned per year) are the independent variable.

7. Dependent variable: it is the condition which the researcher tries to explain, the dependent variable is the “consequent”. Or It is a variable hypothesized to vary depending on the influence of another variable (i.e., “ Consequence ”). It is the variable that is measured. Example : Time and extent of adoption, attitude towards new farm practices, etc.

8. 1. Promotion affects employees’ motivation Independent variable: promotion Dependent variable: employees’ motivation 2. A researcher is interested in knowing “how stress affects mental state of human beings?” Independent variable : stress Dependent variable: mental state of human beings the researcher can directly manipulate the stress levels and can measure how those stress levels change the mental state.

9. Determining the effect of use of audio visual aids on learning ability of farmers of a village. The association between audio visual aids and learning ability needs to be explained Other variables intervene : such as anxiety, motivation, etc. Higher education typically leads to higher income . Higher education : independent variable Higher income : dependent income Better occupation : intervening variable It is casually affected by education and itself affects income.

10. Stimulus variable Response variable

11. Moderator variable Control variable

12. Dichotomous variable: A dichotomous variable is one which may have only two values . Example: female-male, agriculturist-non-agriculturist. Polytomous variable: Polytomous variables have more than two values and have got many dimensions. Example: The influencing skill of extension worker may be high, somewhat high, medium, low, etc.

13. Organismic variables  are the internal forces that influence an organism’s behavior. Or Any characteristic of the research participant/ individual under study that can be used for classification. Example: mental age, I.Q., Personality. Characteristics, past education, etc., are examples of organismic variables .

14. Contextual variable: It is an outcome variable that describes a property of a group , it is constant within the groups and computed from the values of some other variables that may vary within the group. Example: the average age in a suburb where a person lives is a contextual variable. Confounding variables: It is an extra variable that interferes the existing activities .

15. Measurement of variables is quantifying the variables by giving numbers. Measurement is defined as assignment of numerals to objects (or) events according to rules. Example : measuring the height, weight of man Types/levels of measurement: quantification of variables according to mathematical properties, is known as level of measurement. Levels of measurement: Nominal scale 3. Interval scale Ordinal scale 4. Ratio scale

16. Numbers are assigned to objects or events which can be placed into mutually exclusive categories. Fundamental property is of “equivalence” (=). Example: 1. Variable sex has two nominals of classes i.e., Male and female 2. Classification of farmers like big, small and marginal Statistical methods used : number of cases, mode, contingency co-efficient, chi square test, etc.

17. Numbers are assigned to objects or events which can be placed into mutually exclusive categories and be ordered into greater and less than scale. It has no absolute zero point and is also called as ranking measurement. Example: Observations may be classified into categories such as taller and shorter, greater and lesser, faster and slower, harder and easier, and so forth. Statistical measures used: co-efficient of correlation, etc.

18. Numbers are assigned to objects or events which can be categorized, ordered and assumed to have an equal distance between scale values . Intervals between categories are equal but they originate from some arbitrary point of origin. No meaningful zero point exists . Determination of equality of intervals / differences . Example: “standard scores” on cognitive & affective scales, température: fahrenheit & centigrade scales, calendar dates. Statistical measures used: mean, standard deviation, f-test, t-test

19. The ratio level is the same as the interval level with the addition of a meaningful and non-arbitrary zero point . Variables measured at a higher level can always be converted to a lower level but not vice versa. Examples: Years of experience, weight, height, adoption quotient, area, speed, velocity, temperature: kelvin scale, length, force, etc.