1. Elements of Research

2. Concept A concept is a term that expresses an abstract idea formed by generalizing from particulars and summarizing related observations. Note that people must share an understanding of a concept. Example Terms and Jargons used by professionals 2

3. Construct A construct is a concept that has three distinct characteristics: 1.It is an abstract idea broken down into lower- level concepts 2.It cannot be observed directly 3.Its precise meaning depends on the research context 3

4. Example Construct: Authoritarianism May consist of different concepts 1.Conventionalism 2.Submission 3.Superstition 4.Cynicism 4

5. Variable The empirical counterpart of a construct or concept is called a variable. 1.Variables are important because they link the empirical world with the theoretical; 2.They are the phenomena and events that are measured or manipulated in research; 3.Variables can have more than one value along a continuum. For example, satisfaction level (a lot, a little, not- at-all). 5

6. Marker Variables Researchers try to test a number of associated variables to develop an underlying meaning or relationship among them. After suitable analysis, the most important variables are kept and the others are discarded. These identified important variables, which are used to measure a construct, are called marker variables. 6

7. Types of Research Experimental Research – This is the type of research in which the researcher control the settings of some characteristic to see its effect on some other variable of interest Non-experimental Research – In this type of research, there is no active manipulation of variables by the researcher 7

8. Experimental Research - Types of Variables Independent Variables Dependent Variables 8

9. Research Definitions An experiment is a process in which an investigator devises two or more different experiences (treatments) for subjects or participants. Involves a control group and one or more treatment groups

10. Research Variables Independent Variable (IV): the controlled variable in a study; hypothesized to have an effect on the dependent variable – In a true experiment, this is an experimental (manipulated) variable. – In a quasi-experiment, this is a subject variable (not subject to manipulation). Experimental Subject

11. Research Variables Regardless of type, there will always be two or more levels of the independent variable. – Variable: a characteristic or phenomenon that may take on different values; variables must vary! The levels of the independent variable can be: – Independent of one another: between-subjects design – Dependent on one another: within-subjects design

12. Research Variables Dependent Variable (DV): an outcome of interest that is observed and measured by the researcher; hypothesized to be affected by the independent variable

13. Defining Research Variables Operational Definition: a definition of a variable in terms of the operations used to manipulate it or measure it Must be precise enough that anyone reading a review of your research could replicate your experiment exactly.

14. Problem Variables Extraneous (Nuisance) Variables: uncontrolled variables which can affect the experimental outcome Extraneous variables become confounding variables when their values change systematically along with the independent variable in an experiment.

15. Independent variables Independent variables are systematically varied by the researcher Generally, a group of study units is randomly allocated to each level of an independent variable 15

16. Dependent Variables Dependent variables are observed and their values are presumed to depend on the effects of independent variables. The values of dependent variables are not manipulated; they are simply observed or measured. 16

17. Example Effect of camera angle on the audiences’ perception of the credibility of a TV newscaster In this example, various camera angles (low- level, eye-level, high-level) form an independent variable while credibility is a dependent variable 17

18. Discrete and Continuous Variables Discrete variable can take on only a finite set of values (usually integers) e.g. population, number of TV channels Continuous variable can take on any value, including fractions, and can be meaningfully broken into smaller subsections e.g. height, amount of time spent in watching TV 18

19. Non-experimental Research – Types of Variables Predictor variable (Antecedent variable) – Used for prediction and is assumed to be causal Criterion variable – Predicted as it is assumed to be affected by the predictors Control variables – Used to eliminate unwanted influences Noise variables – Variables that potentially may have some relation but are not measured in your research 19

20. Variables Operational Definitions It is important to provide operational definition of all the research variables in a project. Kerlinger (2010) identifies two types of operational definitions – Measured – Experimental 20

21. Importance of Operational Definition Forces researcher to express abstract concepts in concrete terms Helps to identify inappropriate research concepts thus need to be redefined No operational definition satisfies everybody Researcher must choose the operational definition which is best suited for the research problem at hand 21

22. Qualitative Research Several methods of data collection can be used e.g. focus groups, field observation, in- depth interviews, and case-studies. The researcher enters the project with a specific set of questions but follow-up questions are developed as needed. The variables in qualitative research may or may not be measured or quantified. 22

23. Advantages of Qualitative Research Allows researcher to view behavior in a natural setting without the artificiality that may surrounds experimental or survey research Increase depth of understanding of the phenomenon under investigation especially working on a new topic of research 23

24. Disadvantages of Qualitative Research Sample sizes are too small to generalize the results beyond the study sample Data reliability is questionable If not properly planned, the results may produce nothing of value Researcher close involvement may lead to biased results It is preliminary step which needs further investigation 24

25. Quantitative Research Several methods of data collection such as telephone surveys, mail surveys, and Internet surveys. All respondents are asked the same set of questions and there is no opportunity for follow-up questions It requires that the variables under consideration be measured. 25

26. Advantages of Quantitative Research Larger sample sizes are studied Reliability can be determined Use of numbers allows greater precision in reporting results e.g. Violence Index (Gerbner et al. 1980) Researcher involvement is limited so there is no or lesser amount of bias in results 26

27. Disadvantages of Quantitative Research No provision of subjectivity in recording data Quite hard for a new or less investigated area of research 27

28. Important Remark The only difference between qualitative and quantitative research is the style of questioning. Qualitative research uses flexible questioning; quantitative uses standardized questions. Assuming that the sample sizes are large enough and that the samples are properly selected, the results from both methods can be generalized to the population from which the sample was drawn. 28

29. The Nature of Measurement “The progress and maturity of a science are often judged by the extent to which it has succeeded in the use of mathematics….” “Mathematics is a universal language that any science or technology may use with great power and convenience…” (Guilford, 1945) 29

30. Measurement: Numerals and Numbers The idea behind measurement is simple A researcher assigns numerals to objects, events, or properties according to certain rules. A numeral has no implicit quantitative meaning. When it is given a quantitative meaning, it becomes a number and can be used in mathematical and statistical computations. 30

31. Measurement: Assignment Rules Rules specify the way that numerals or numbers are to be assigned. Rules are at the heart of any measurement system; if they are faulty, the system will be flawed. In some situations, rules are straightforward e.g. a stop watch can be used to measure reading speed In other instances, the rules are not so apparent e.g. measuring attitude towards violence 31

32. Measurement System: Isomorphism Isomorphism means identity or similarity of form or structure. In physical sciences, the measurement systems are isomorphic. In social sciences, the measurement systems do not have obvious isomorphism. This is the reason, you will find different measurement systems to quantify a concept. These measurement systems are unable to show one- to-one correspondence. 32

33. Measurements There are four different levels of measurement. 1.Nominal 2.Ordinal 3.Interval 4.Ratio The mathematical operations that can be performed with a given set of scores depend on the level of measurement achieved. 33

34. Scales of Measurement Nominal: classification Ordinal: ranking Interval: equal intervals Ratio: absolute zero

35. Nominal Measurement Nominal: observations are put into categories based on some criterion Classifies; categorizes Dichotomous Variable: has two values; e.g. male/female, yes/no Multichotomous: has more than two values; e.g. ethnicity, marital status No numerical value (even when observations are numbers) Permissible arithmetic operations: counting

36. Ordinal Measurement Ordinal: a basic form of quantitative measurement that indicates a numerical order; the intervals between adjacent scale values are undetermined or unequal. Examples: team/individual standing, socioeconomic status, level of education, Likert scales, any type of rating or ranking Permissible arithmetic operations: greater than/less than

37. Interval Measurement Interval: intervals between adjacent scale values are equal; scale has an arbitrary zero – Hint: If score can go below zero, or if no true zero exists, measurement is interval. Examples: Celsius and Fahrenheit temperature scales, IQ scores, most psychological measures Permissible arithmetic operations: addition, subtraction, multiplication, division; cannot make ratio statements

38. Ratio Measurement Ratio: a measurement scale that has equal units of measurement and a rational zero point for the scale (absolute zero) – Hint: An absolute zero indicates a complete absence of the attribute being measured. Examples: Kelvin temperature scale, income in dollars, length, area or volume, height, weight Permissible arithmetic operations: any, including ratios

39. Continuous and Discontinuous Scales Discontinuous (Discrete) Measurement: variables for which values can only be whole numbers (integers) Continuous Measurement: variables that can assume any value (real numbers) Hint: If it can be broken down into decimal points, it is continuous!!! Discontinuous: 1, 2, 3, -1, -2, -3 Continuous: 1.5, 2.25, 3.775, 4.0135, 5 ½, 6 ¾

40. Choosing Graphs Graphs (as well as statistics) are selected based on the scale and continuity of measurement. Scale of Measurement Continuity of MeasurementType of Graph ContinuousLine Graph Interval/RatioDiscontinuousHistogram Ordinal Bar Chart Nominal Bar Chart