DECÁLOGO PARA ELABORAR UN PLAN DE COMUNICACIÓN El decálogo para elaborar un plan de comunicación que permita la

1.0 Introduction 2.0 Objectives 3.0 Main Body

3.1 Learning the Language of Variable and Hypothesis 3.1.1 What is Variable?

3.1.2 Causal Relationship and Hypotheses 3.1.3 Five Characteristics of Causal Hypothesis 3.1.4 Future Research Test

3.1.5 New Hypotheses Developed 4.0 Conclusion

5.0 Summary

6.0 Tutor Marked Assignment 7.0 References/Further Readings

1.0 IINTRODUCTION

Quantitative research relies primarily on assumptions from the positivist approach to science. Qualitative research uses a language of variables, hypotheses, units of analysis, and causal explanation. The logical errors that may arise when developing a causal explanation illustrate why it is essential to understand the components of research design and how they relate to one another.

2.0 OBJECTIVES

At the end of this unit you should be able to:

Discuss how to design a quantitative research project;

Explain the language of quantitative research.

3.0 MAIN BODY

3.1 Learning the Language of Variables and Hypothesis 3.1.1 What is Variable?

Variation and Variable: the variable is a central idea in quantitative research. Simply defined, a variable is a concept that varies. The language

of quantitative research is a language of variables and relationships among variables.

You learned about two types of concepts, t that refer to a fixed phenomenon (e.g., the ideal type of bureaucracy) and those that vary in quantity, intensity, or amount (e.g. amount of education). The second type of concept and measures of the concepts are variables. Variables take on two or more values. Once you begin to look for them, you will see variables in this order.

For example, gender is a variable; it can take on two values, male or female.

Marital status is a variable; it can tame on the values of never married single, married, divorced, or widowed. Type of crime committed is a variable; it can take on values of robbery, burglary, theft, murder, and so forth. Family income is a variable; it can take on values from zero to billions of dollars. A person’s attitude toward abortion is a variable; it can range from strongly in favour of legal abortion to strongly anti-abortion.

The values or the categories of a variable are its attribute.. It is easy to confuse variables with attributes. Variables and attributes are related, but they have distinct purposes. The confusion arises because the attribute of one variable can itself become a separate variable with a slight change in definition. The distinction is between concepts themselves that vary and conditions within concepts that vary. For example, “male” is not a variable;

it describes a category of gender and is an attribute of the variable “gender”.

Yet, a related idea, “degree of masculinity”, is a variable. It describes the intensity or strength of attachment to attitudes, belief, and behaviours associated with the concept of “masculine” within a culture.

It is not always easy to determine whether a variable is independent or dependent. Two questions help you identify the independent variable. First, does it come before other variables in time? Independent variables come before any other type. Second, if the variables occur at the same time, does the author suggest that one variable has an impact on another variable?

Independent variables affect or have an impact on other variables. Research topics are often phrased in terms of the dependent variables because dependent variables are the phenomenon if explained. For example, suppose a researcher examines the reasons for an increase in the crime rate in Dallas.

Texas; the dependent variable is the crime rate.

A basic causal relationship requires only an independent and a dependent variable. A third type of variable, the intervening variable, appears in more complex chains of causal relations. It comes between the independent and dependent variables and shows the link or mechanism between them.

Advances in knowledge depend not only on documenting cause-and-effect

relationships but also on specifying the mechanisms that account for the causal relation. In a sense, the intervening variable acts as a dependent variable with respect to the independent variable and acts as an independent variable toward the dependent variable.

For example, the French sociologist Emile Durkheim developed a theory of suicide that specified a causal relationship between manual status and suicide rates. Durkheim found evidence that married people are less likely to commit suicide than single people. He believed that married people have greater social integration (i.e. feelings of belong to a group or family) and thought that a major cause of one type of suicide was that people lacked a sense of belonging to a group. Thus, his theory can be restated as a three- variable relationship: marital status (independent variable) causes the degree of social integration (intervening variable), which affects suicide (dependent variable). Specifying the chain of casualty makes the linkages in a theory clearer and helps a researcher test complex explanations.

Simple theories have one dependent and one independent variable, whereas complex theories can contain dozens of variables with multiple independent, intervening, and dependent variables. For example, a theory of criminal behaviour (dependent variable) identifies four independent variables: an individual’s economic hardship, opportunities to commit crime easily, membership in a deviant subgroup of society that does not disapprove of crime, and lack of punishment for criminal acts. A multicause explanation usually specifies the independent variable that has the greatest causal effect.

A complex theoretical explanation contains a string of multiple intervening variables that are linked together. For example, family disruption causes lower self-esteem among children, which causes depression, causes poor grades in school, reduced prospects for a good job, a lower adult income.

The chain of variables is: family disruption (independent), childhood self- esteem (intervening), depression (intervening), grades in school (intervening), job prospects (intervening), adult income (dependent).

Two theories on the same topic may have different independent variables or predict different independent variables to be important. In addition, two theories may agree about the independent and dependent variables but differ on the intervening variable or causal mechanism. For example, two theories say that family disruption causes lower adult income, but for different reasons. One theory holds that disruption encourages children to join deviant peer groups that are not socialized to norms of work and thrift.

Another emphasizes the impact of the disruption on childhood depression and poor academic performance, which directly affect job performance.

3.1.2 Causal Relationships and Hypothesis

The Hypothesis and Causality. A hypothesis is a proposition to be tested or a tentative statement of a relationship between two variables. Hypotheses are guesses about how the social world works stated in a value-neutral form.

As Kerlinger (1979:25) notes:

Hypotheses are much more important in scientific research than they would appear to be just by knowing what they are and how they are constructed. They have a deep and highly significant purpose of taking man out of himself, so to speak… hypothesis are powerful tools for the advancement of knowledge, because, although formulated by man, they can be tested and shown to be correct or incorrect apart from man’s values and beliefs

A causal hypothesis has five characteristics. The first two characteristics define the minimum elements of a hypothesis. The third restates hypotheses.

For example, the hypothesis that attending religious services reduces the probability of divorce and can be restated as a prediction: Couples who attending religious services frequently have a lower divorce rate than do couples who rarely attend religious services. The prediction can be tested against empirical evidence. The fourth characteristic states that hypotheses should not be viewed in isolation. They should be logically tied to a research question and ultimately to a theory. Researchers test hypotheses to answer the research question or find empirical support for a theory. The last characteristic requires that a researcher use empirical data to test hypotheses.

3.1.3 Five Characteristics of Causal Hypotheses

2. It expresses a causal or cause-effect relationship

3. It can be expressed as a prediction or expected future outcome 4. It is logically linked to a research question and a theory

5. it is probable falsifiable; that is, it is capably of being tested against empirical evidence and shown to be true or false.

Statements that are necessarily true as logic or questions that are impossible are through scientific observation (What is a life? Is there a God?) cannot be scientific cases.

The result of testing a hypothesis is three outcomes:

TIME 1: Which of the eight potential hypotheses in competition to