Variables in social science research and its measurement ppt
- 1. ODISHA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY Presented by : ABHIJEET SATPATHY 1st Year Ph.D. Department of Extension Education, College of Agriculture, OUAT, Bhubaneswar DEPARTMENTAL SEMINAR ON “USE OF VARIABLES IN SOCIAL SCIENCE RESEARCH”
- 2. Topics covered: Common terms used in social science research Variables: meaning, definition Criteria for selection of variables Types of variables Measurement of variables Different scales of measurement
- 3. Introduction to some common terms used in social science research Concept: a concept is a thought, an opinion, a mental image of a thing formed by generalization from particular. Example: weight, Light, temperature, sound, age, etc. are concepts. Construct: it is a combination of concepts. A construct can not be observed directly. Example: intelligence, learning, hostility, anxiety Concept Construct
- 4. Fact: logical construct of concepts. Fact is an observation which is based on experience. Attributes : sub-values of a variable, such as 'male' and 'female’. Mutually exclusive attributes are those that cannot occur at the same time. Units are the ways that variables are classified. These include: individuals, groups, social interactions and objects.
- 5. What is a variable? • 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
- 6. Criteria for selection of variables: The variables selected for study should be – i. According to the objectives of the study. ii. Mutually exclusive and not overlapping iii.According to the level of understanding of the researcher. iv.Measurable by the available techniques or suitable technique could be developed for the same. v. Could be classified into some categories. vi.Limited in number so as to avoid confusion and could be studied with that resource and time available. • Variables selected for study shouldbe labelled, such as y for dependent variables and x1, x2, x3….. Xn for the independent variables
- 7. Identifying the variables: Academic performance Attitude towards study Study habits Learning style Family background Self efficacy and motivation
- 8. Types of variables in social science research 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.
- 9. Dependent variable: 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.
- 10. Examples 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.
- 11. Terminological varieties of dependent and independent variables DEPENDENT VARIABLE Explained Predictand Response Outcome controlled Explanatory Predictor Stimulus Covariate Control INDEPENDENT VARIABLE
- 12. Mediating/intervening variable/Hidden variable: According to Kerlinger, the constructs, which are non observable , have been called intervening variables. It can neither be seen nor heard, nor felt. it is inferred from the behavior. Example: Hostility, Anxiety, Fatigue, Motivation, Learning.
- 13. Examples: • 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.
- 14. Quantitative and Qualitative variables Quantitative variables: The variables which can be measured in numeric terms. These variables vary in amount. Those characteristics which may be taken on various magnitudes i.e., may exist in greater (or) smaller amounts. • Example: Age, income, size of land holding, size of group, etc. • There are 16 students enrolled in P.G 1st year • These variables are also called as ordered variables because the difference in the possible values are in ordered degrees.
- 15. Qualitative Variable Qualitative variable: this variable is also called attribute (or) organismic (or) unordered (or) categorical variable. Any variable that cannot be manipulated (or) at least is difficult to manipulate is called qualitative variable. They vary in kind, type. • Example: education, sex. • As a student you are a junior or senior. • Are you a male or female
- 16. Continuous and Discrete variables Continuous variable: • Example: age, income of farmers, adoption quotients. values fall along a continuum. Continuous variables are theoretically, infinitely divisible into smaller and smaller fractional units. A continuous variable is capable of taking an ordered set of values within a certain range.
- 17. Discrete variable: • Example: • a. the number of inhabitants in each village. • b. the number of members of a university students union. Discrete variable is one which involves counting the number of events. It consists of only whole numbers, fractional values cannot occur.
- 18. Stimulus and Response variables: Stimulus variable: Stimulus variable is the condition (or) manipulation created by the researcher so as to evoke a response in an organism. • Example: method demonstration, a slide show about a new crop variety, etc. Response variable (or) Behavioral variable: any kind of behavior of the respondent is called behavioral variable. This refers to some action (or) response of an individual. Example: Yes or No, True or False Stimulus variable Response variable
- 19. Moderator and control variables Moderator variables: These are special type of independent variables which are hypothesized to modify the relationship between the dependent and independent variables. • Example: age, intelligence, etc. Control variables: Those variables which may effect the relationship between the independent and dependent variables and which are ‘controlled’ (effects cancelled out). Moderator variable Control variable
- 20. Extraneous variables Extraneous variable/ nuisance variable: these are the independent variables which are not related to the purpose of the study, but which may have a significant influence upon the dependent variable. • Example: Intelligence may be one extraneous variable in the training process through various teaching methods, and the dependent variable achievement score may be affected by intelligence.
- 21. Types of extraneous variables: Subject variables: These are the characteristics of the individuals being studied that might affect their actions. These variables include age, gender, health status, mood, background, etc. Blocking variables or experimental variables: These are characteristics of the persons conducting the experiment which might influence how a person behaves. Example: Gender, language. Situational variables: These are features of the environment in which the study or research was conducted, which have a bearing on the outcome of the experiment in a negative way. Example: air temperature, level of activity, lighting, and the time of day.
- 22. Antecedent variables • Example: The training, education, experience of a extension worker are antecedent variables. Role of extension worker is independent variable, and performance of extension worker is dependent variable. Antecedent variable: These variables occur in terms of time, prior to independent variables and are related to both independent and dependent variables.
- 23. Active and Attribute variables Active variables: a variable that is manipulated is called active variable. • Example: Award of prizes, giving punishment, creating anxiety Attribute variable: variable that cannot be manipulated is called attribute variable. • Example: Sex, race
- 24. Dichotomous and Polytomous variables 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.
- 25. Demographic variables: “Demographic variables are characteristics or attributes of subjects that are collected to describe the sample”. They are also called sample characteristics. Demographic variables cannot be manipulated. Example: Some common demographic variables are age, gender, occupation, marital status, income etc.
- 26. Organismic variable • 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.
- 27. Other important variables: • 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.
- 28. MEASUREMENT OF VARIABLES • 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: 1. Nominal scale 3. Interval scale 2. Ordinal scale 4. Ratio scale
- 29. NOMINAL SCALE • 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.
- 30. Ordinal scale • 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.
- 31. Interval scale • 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
- 32. RATIO SCALE • 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.