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Monday, August 15, 2011

research variable

RESEARCH VARIABLES

The research variables, of any scientific experiment or research process, are factors that can be manipulated and measured.an entity that takes on different values a property of individuals,groups communities or any object that vary from one unit to another.

Class,income,occupation,intelligence,age etc arew variable,some that changes is variable and some that never changes is constant

These property not only vary from individual to individual or from group to group,but also influence and thus creates variation in peoples attitudes,opinions,behaviors


Any factor that can take on different values is a scientific variable and influences the outcome of
experimental research.

Gender, color and country are all perfectly acceptable variables, because they are inherently changeable.

Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable.

The key to designing any experiment is to look at what research variables could affect the outcome.

  1. A variable is a label or name that represents a concept or characteristic that varies (e.g., gender, weight, achievement, attitudes toward inclusion, etc.)
  2. Conceptual and operational definitions of variables
    1. Conceptual (i.e., constitutive) definition uses words or concepts to define a variable
      1. Achievement: what one has learned from formal instruction
      2. Aptitude: one's capability for performing a particular task or skill
    2. Operational definition is an indication of the meaning of a variable through the specification of the manner by which it is measured, categorized, or controlled
      1. Weschler IQ score
      2. Income levels below and above $45,000 per year
      3. Use of holistic or phonetic language instruction
  3. Types of variables
    1. Three variable labels defined by the context within which the variable is discussed
      1. Independent and dependent variables
      2. Extraneous and confounding variables
      3. Continuous and categorical variables
    2. Independent and dependent (i.e., cause and effect)
      1. Independent variables act as the "cause" in that they precede, influence, and predict the dependent variable. There are two types of independent variables: Active and attribute. If the independent variable is an active variable then we manipulate the values of the variable to study its affect on another variable. In the above example, we alter anxiety level to see if responsiveness to pain reduction medication is enhanced. Anxiety level is the active independent variable. An attribute variable is a variable where we do not alter the variable during the study. For example, we might want to study the effect of age on weight. We cannot change a person's age, but we can study people of different ages and weights.
      2. Dependent variables act as the effect in that they change as a result of being influenced by an independent variable. This is the variable that is affected by the independent variable. Responsiveness to pain reduction medication is the dependent variable in the above example. The dependent variable is dependent on the independent variable. Another example: If I praise you, you will probably feel good, but if I am critical of you, you will probably feel angry. My response to you is the independent variable, and your response to me is the dependent variable, because what I say influences how you respond.
      3. Control variable: A control variable is a variable that effects the dependent variable. When we "control a variable" we wish to balance its effect across subjects and groups so that we can ignore it, and just study the relationship between the independent and the dependent variables. You control for a variable by holding it constant, e.g., keep humidity the same, and vary temperature, to study comfort levels.
      4. Examples
        1. The effect of two instructional approaches (independent variable) on student achievement (dependent variable)
        2. The use of SAT scores (independent variable) to predict freshman grade point averages (dependent variable)
      5. Some situations do not lend themselves to the use of the terms independent or dependent because it is difficult to discuss them in causal terms
        1. The relationship between attitude and achievement, that is, do positive attitudes cause high achievement or does high achievement cause positive attitudes?
        2. The relationship between creativity and critical thinking, that is, do high levels of creativity cause higher levels of critical thinking or do higher levels of critical thinking cause greater creativity?
    3. Extraneous and confounding variables
      1. Extraneous variables are those that affect the dependent variable but are not controlled adequately by the researcher. This is a variable that probably does influence the relationship between the independent and dependent variables, but it is one that we do not control or manipulate. For example, barometric pressure may effect pain thresholds in some clients but we do not know how this operates or how to control for it. Thus, we note that this variable might effect our results, and then ignore it. Often research studies do not find evidence to support the hypotheses because of unnoticed extraneous variables that influenced the results. Extraneous variables which influence the study in a negative manner are often called confounding variables.
        1. Not controlling for the socio-economic status of students involved in a study of the effects of instructional technologies
        2. Not controlling for the key-boarding skills of students in a study of computer-assisted instruction
      2. Confounding variables are those that vary systematically with the independent variable and exert influence of the dependent variable
        1. Not using counselors with similar levels of experience in a study comparing the effectiveness of two counseling approaches
        2. Not using the same test to measure the effectiveness of two instructional approaches
    4. Continuous and categorical
      1. Continuous variables are measured on a scale that theoretically can take on an infinite number of values
        1. Test scores range from a low of 0 to a high of 100
        2. Attitude scales that range from very negative at 0 to very positive at 5
        3. Students' ages
      2. Categorical variables are measured and assigned to groups on the basis of specific characteristics
        1. Examples
          1. Gender: male and female
          2. Grade level: K-12
          3. Socio-economic status: low, middle, and high
        2. The term level is used to discuss the groups or categories

1. Gender has two levels - male and female

2. Socio-economic status has three levels - low, middle, and high

Continuous variables can be converted to categorical variables, but categorical variables cannot be converted to continuous variables

IQ is a continuous variable, but the researcher can choose to group students into three levels based on IQ scores - low is below a score of 84, middle is between 85 and 115, and high is above 116

Test scores are continuous, but teachers typically assign letter grades on a ten point scale (i.e., at or below 59 is an F, 60 to 69 is a D, 70 to 79 is a C, 80-89 is a B, and 90 to 100 is an A


Variables must be defined in terms of measurable behaviors. The operational definition of a variable describes the variable. There are two ways by which we can operationally define a variable; by how it is measured and by how it is used to classify subjects. Later we will use specialized terms for how variables are defined (continuous or categorical) and the nature of the data obtained (nominal, ordinal, or interval). These terms are discussed in Chapters 6 and 7.

The first way of defining a variable is to describe how we measure it. We cannot just say we will "reduce anxiety." We must define how anxiety will be measured and just what is a reduction in anxiety. In our example we define anxiety as a change in galvanic skin response generated by the discussion of potentially emotional content (i.e., one's pending death). Another example would be range of motion. In general range of motion deals with the amount of mobility one has of their limbs. Actually, we define ROM as the movement of a specific limb through so many degrees as measured by a goniometer with the limb held in a specific way and moved in a prescribed arc.

The second way of defining a variable is to describe how you have classified subjects (people) into groups or categories. This is important since two researchers could be studying the same variable but if they each classify their subjects differently they may get different results. For example, suppose we wanted to study the income levels of single adults. If one researcher classified his single adult subjects into these three categories (17 through 22, 23 through 27, and 28 through 33), he would get different results than this second researcher who used three different categories (20 through 40, 41 through 60, and 61 and over). The first researcher is interested in young adults and the second in all ages. Thus, without operational definitions we could think that they both were studying the same variable.

When we use behavioral (operational) definitions for variables, we define exactly what we are studying and enable others to understand our work. This is called operationalism.

Lectur notes prepared by Biju P R,Assistant professor in Political science,GBC TLY

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