# 美国论文代写-定量方法

Nominal variable: This type of a variable does not help in quantifying the amount or ascertain the degree. However, it is a measure of the qualitative aspect. These variables are not exhaustive or mutually exclusive.
Ordinal variables: These variables can be ranked on the basis of degree. These variables are mutually exclusive and exhaustive
Interval variables: These types of variables specify a certain interval. So, these are truly quantitative variables as these specify a certain interval or a comparative measure of the values. These have attributes of nominal as well as ordinal variables
Ratio variables: These are similar to interval variables which have a zero point or a neutral point.
Always from a researcher’s perspective, all concrete variables are measured at an interval level whereas the concept variables are measured at an ordinal level. A very important aspect of this type of analysis is that the sampling has to be correctly chosen to represent the majority of the population under the researcher’s purview. Additionally, the lowest unit of society should be measured for the required attributes to avoid generalization occurring as a repercussion of analyzing households instead of individuals. There are certain factors of measurement which must be satisfied before we can ascertain that the analysis has been done satisfactorily. The different types of validity are:
Face validity: This refers to a consensus among the researchers on the operational aspects of the research
Content validity: This refers to the availability of the proper content for testing the concept or assumption of the research
Construct validity: This refers to whether the variables chosen have sufficient correlation among each other to elucidate a meaningful output
Criterion validity: This refers to the instrument measure and the overall basis of the argument being tested in the research
The other factors in measure apart from validity are:
Reliability: This refers to getting the same answer from measurement of the variables more than once with the same instrument
Precision: This refers to how detailed is the scale of measurement of the variable
Accuracy: This refers to how close is the measurement to reality
The cause and effect relationship can be explained using different constructs. The first is co-variation or correlation. This refers to the fact that the variables selected for measurement are related directly with means of any impact on one variable due to a change in the other. Independent variables can be used to predict the outcome of dependent variables. Cognitive dissonance is a theory in the cause and effect realm which suggests if the outcome is outside of the set of expected outcomes. As mentioned earlier, there can be different ways of sampling the population so that the sample selected, portrays the measurable attributes accurately for the complete population.
Sampling with questionnaires allows us to take a random sample out of the population to administer the questionnaire to get some answers around the variables needed to be measured.
Systematic random sampling refers to defining a specific methodology to define a random sample, like selecting every odd numbered person out of a population of 100. This is very handy; however, there is a risk on missing out certain groups.

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