The post Types of Measurement Scales in Research and Statistics appeared first on Reading Craze.

]]>The nominal or classificatory scale is the most simple scale that is used in statistics and research. This scale only identifies the variables under study into unique values. This scale does not provide numerical values to the variables. The variables are only compared on the basis of some unique characteristic descriptively. Kumar R. (2000) defines nominal scale as the classifications of individuals, objects or responses based on common or shared property or characteristics. These people, objects or responses are divided into a number of subgroups in such a way that each member of the sub-groups has a common characteristic. This scale is most commonly used to categorize ‘gender’ or other simple variables, such variables do not have numerical values. Gender can be either male or female, but you cannot assign some numerical values to gender like 100 percent male etc.

The ordinal scale has the properties of the nominal scale, but it is a little more advanced than nominal scale. It categorizes values assigned to the variables on the basis of their magnitude. Some values related to the variables will be ‘smaller’ while other will be ‘larger’ or in other cases, some values will be ‘greater’ than the other values which will be ‘lesser’. The values are arranged either in ascending or descending order, so there is an ordered relationship between the values in the scale. This scale, however, does not categorize the values on the scale in fixed intervals.

The interval scale has the properties of the ordinal and nominal scale, which means that it assigns unique values to the each value under study and it also categorizes the values in ascending or descending order. The unique property of the interval scale that makes it different from the previous two scales is that it also categorize the values in equal intervals. In this scale, any of the two values on the scale has an equal interval. Temperature can be categorized using the interval scale, for example, values in centigrade can be spaced at equal interval using this scale. It also happens that in this scale the values have a starting point and an ending point. The space between 0-degree centigrade to 100-degree centigrade is always equally spaced.

This scale has all the properties of the previous scales plus one of its own properties that it has a fixed zero point, and below that point, no value exists. Every value can be measured from an absolute zero point or starting point, due to this property this scale is called as an absolute scale and for most empirical and mathematical operations this scale is used. Weight and height are the best examples of variables that can be measured on the ratio scale, they have an absolute zero point and no value exists below that. Temperature, which can be measured on the interval scale, cannot be measured on the ratio scale because the temperature can be below zero so there are no absolute zero points.

The post Types of Measurement Scales in Research and Statistics appeared first on Reading Craze.

]]>The post The Variables in Research Studies appeared first on Reading Craze.

]]>This is an important question that students should know before attempting to formulate a research problem. In general terms, variables are concepts that can be measured and operationalized. In the research, there are concepts that the researcher deals with these concepts are highly subjective, for example, some concepts about a community health program. There will be different concepts about the program, say it will be successful or it will a total failure. The term success or failure can be very subjective and judgemental. You can ask the respondents what do they think about this program and they will say it will be *effective, successful, failure, useful. *These terms and other such judgemental terms cannot be treated scientifically in the research. The reason being that their meaning can be very different from one person to another. The context in which each person looks at *success *or *failure* can be very different. One person might say that this particular community program benefited 50% of the community members and so it is successful but another person might say that at least 75 % people should benefit from this program to call it a success. This subjectivity can bring bias in the research. To avoid this subjectivity, the concepts should be converted into variables in research. Variables are quantifiable and hence they can be easily dealt in the research process.

Very simple definitions of variables say that a variable is something that can change. Variables are concepts that can be measured on any one of the measuring scales. Numeral values can be assigned to the variables. There are several other definitions of variables in research like variables are those aspects of the research that vary and this variation can be tested using different scales. These variables are characteristics or values and hence they can change, their use is more common in psychology but in other studies to they can be used.

Here we take two examples of variables in research and try to understand how variables influence the research process. Suppose you want to study the effect of permanent-press finishes on the durability characteristics of cotton fabric. It is an experimental study and in experiments you have an independent variable, a dependent variable and a control variable. The dependent variable, therefore, will be the cotton fabric and the independent variables are the permanent-press finishes that are applied to cotton fabric. There can be several other variables that can effect this study, these variables need to be controlled. For example, the other finishes that are applied to the cotton fabric can be the extraneous variables and they must be controlled to see the effect of independent variable on the dependent variable in the study.

Take another example, suppose you want to study the impact of the tsunami on the religious and social lives of the people living on the west coast of Sumatra-Indonesia. It is again a cause and effect study and in this study you will take the lives of the people as a dependent variable and tsunami after effects as the independent variables. Any other extraneous variable should be controlled in order to avoid bias in the study. One thing to make sure in such researches or studies is that the control variable needs to be controlled in the best possible manner since it can ruin the whole study. Another way is to add the other control variables in the study and they can be taken as secondary independent variables. When more variables are introduced in the study the study becomes more empirical and valid but it becomes complicated. It takes more time to measure several variables than to study the impact of one variable. When extraneous variables are difficult to control they should be added in the study.

The post The Variables in Research Studies appeared first on Reading Craze.

]]>