Ch 2 - Research Variables.pptx research variable
- 1. Chapter 2: Research Variables
- 2. Content • What is a Variable? • The Difference Between a Concept and a Variable • Converting Concepts into Variables • Types of Variables • Levels of Measurement
- 3. What is a Variable? • Whether we accept it or not, we all make value judgments constantly in our daily lives: ‘This food is excellent’; ‘I could not sleep well last night’; ‘I do not like this’; and ‘I think this is wonderful’. • These are all judgments based upon our own preferences, indicators or assessment. • Because these explain feelings or preferences, the basis on which they are made may vary markedly from person to person. • There is no uniform benchmark with which to measure them. A particular food may be judged ‘excellent’ by one person but ‘awful’ by another, and something else could be wonderful to one person but ugly to another.
- 4. • An image, perception or concept that is capable of measurement – hence capable of taking on different values – is called a variable. • In other words, a concept that can be measured is called a variable. • A variable is a property that takes on different values. • A concept that can be measured on any one of the four types of measurement scale is called a variable (measurement scales are discussed later in this chapter).
- 5. The Difference Between a Concept and a Variable • Measurability is the main difference between a concept and a variable. • Concepts are mental images or perceptions and therefore their meanings vary markedly from individual to individual, whereas variables are measurable, though, of course, with varying degrees of accuracy. • A concept cannot be measured whereas a variable can be subjected to measurement by crude/refined or subjective/objective units of measurement. • Concepts are subjective impressions which, if measured as such would cause problems in comparing responses obtained from different respondents.
- 6. The Difference Between a Concept and a Variable Concept • Subjective impression • No uniformity as to its understanding among different people • Cannot be measured • Examples: effectiveness, rich, self-esteem, excellent Variable • Measurable though the degree of precision varies from scale to scale and from variable to variable (e.g. attitude – subjective, income – objective) • Examples: age, income, height, weight
- 7. Converting Concepts into Variables • If you are using a concept in your study, you need to consider its operationalization – that is, how it will be measured. • In most cases, to operationalize a concept you first need to go through the process of identifying indicators – a set of criteria reflective of the concept – which can then be converted into variables. • The choice of indicators for a concept might vary with the researcher but those selected must have a logical link with the concept.
- 8. • Some concepts, such as ‘rich’ (in terms of wealth), can easily be converted into indicators and then variables. • For example, to decide objectively if a person is ‘rich’, one first needs to decide upon the indicators of wealth. • Assume that we decide upon income and assets as the indicators. • Income is also a variable since it can be measured in dollars; therefore, you do not need to convert this into a variable. • Although the assets owned by an individual are indicators of his/her ‘richness’, they still belong to the category of concepts.
- 9. • You need to look further at the indicators of assets. For example, house, boat, car and investments are indicators of assets. • Converting the value of each one into dollars will give the total value of the assets owned by a person. • Next, fix a level, based upon available information on income distribution and an average level of assets owned by members of a community, which acts as the basis for classification. • Then analyze the information on income and the total value of the assets to make a decision about whether the person should be classified as ‘rich’.
- 10. Types of Variables • There are sets of variables that could be involved in studies that attempt to investigate a causal relationship or association. • Those include independent, dependent, extraneous, and control variables.
- 11. Independent Variable • An independent (or a predictor) variable refers to the conditions that a researcher controls (or changes) in order to test its effect on some outcome. • It is the variable which the researcher chooses to study and manipulates in terms of amount or level, in order to assess its effect on the other variable. • It is the cause supposed to be responsible for bringing about change(s) in a phenomenon or situation.
- 12. Dependent Variable • An independent variable is usually presumed to affect another variable. • The other variable that the independent variable is presumed to affect is called the dependent or criterion or outcome variable. • Generally, the nature of a dependent variable depends on what an independent variable does to it. • Thus, a dependent variable manifests observable changes attributable to the influence of an independent variable. • It is the outcome or change(s) brought about by introduction of an independent variable.
- 13. Extraneous Variable • In an ideal study, only the independent variable should influence the dependent variable. • But this is not usually the case because there are always other variables that could also influence the dependent variable, and hence the outcome of a study. • However at any one time, a researcher can only study a few of these variables in one study. • Those other variables that can also influence the results of the study, but which the researcher does not wish to study at the moment are called extraneous or intervening variables.
- 14. Control Variable • Extraneous or covariate variables must be controlled or they will also influence the dependent variables and confound the results of the study. • An extraneous variable is said to be controlled when its effect on the dependent variable is reduced to a bare minimum so that it does not have a significant influence on the outcome of the study. • Once the effect of an undesirable independent variable on the dependent variable has been reduced to a bare minimum, it is then said to have been controlled, and it becomes a control variable.
- 15. Control of Variance • Variance in research refers to errors, or more specifically, to the difference between the expected results and the actual results. • The aim of any researcher is to get the expected (ideal) results. • But due to certain extraneous factors, there may be discrepancies between the actual and the expected results. • This discrepancy is referred to as variance. • The smaller the discrepancies (or variances) the more accurate the results. • In controlling variance, a researcher tries to reduce discrepancies to possible minimum.
- 16. Methods of Controlling Variance • There are several techniques for controlling variance. These include: Randomization. A random selection is picking cases without bias from a target or accessible population in a manner that each case has an equal and independent chance of being selected as a member of the sample. Eliminating Extraneous Variables. There are some extraneous variables that a researcher can remove completely from the study if they are suspected to influence the outcome of the study. Holding Factors Constant. In this technique, a researcher tries to keep the value of an extraneous variable the same for all cases or groups of cases.
- 17. Levels of Measurement • There are four levels of data measurement: nominal, ordinal, interval and ratio. • Nominal Level – This is the lowest level of measurement. In this level of measurement, the numbers in the variable are used only to classify the data. E.g., Gender (1: Male, 2: Female) • Ordinal Level – This level of measurement depicts some ordered relationship among the variable’s observations. Suppose a student scores the highest grade of 100 in the class. In this case, he would be assigned the first rank. Then, another classmate scores the second highest grade of an 92; he would be assigned the second rank. The ordinal level of measurement indicates an ordering of the measurements.
- 18. Levels of Measurement … • Interval Level – The interval level of measurement not only classifies and orders the measurements, but it also specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval. It has no absolute zero. An example of this level of measurement is temperature in centigrade, where, for example, the distance between 94 0 C and 96 0 C is the same as the distance between 100 0 C and 102 0 C. • Ratio Level – In this level of measurement, the observations, in addition to having equal intervals, can have a value of zero (absolute zero) as well. E.g., height, weight, academic performance of students.
- 19. Activity Identify the level of measurement for each of the following variables: • Student ID numbers • Ranks of participants in a race (1st, 2nd, 3rd, etc.) • Height of students in centimeters • Types of cars (Sedan, SUV, Truck, Coupe) • Scores on a mathematics test (out of 100) • Shoe sizes (6, 7, 8, 9, etc.) • Income level (Low, Medium, High) • Time spent studying in hours • Marital status (Single, Married, Divorced)
- 20. Levels of Measurement (Summary) • Nominal: Categories with no specific order (e.g., types of cars, marital status). • Ordinal: Categories with a specific order, but unequal intervals between categories (e.g., race rankings, income levels). • Interval: Ordered categories with equal intervals between them, but no absolute zero (e.g., temperature in Celsius). • Ratio: Ordered categories with equal intervals and a true zero point, making both differences and ratios meaningful (e.g., height, time spent studying).
- 21. Thank You!