The first step in designing any quantitative study is to identify the independent (i.e., cause or predictor) and dependent (i.e., effect or criterion) variables. The second step is to define the variables concretely (guided by prior research). Variables can be defined using one of four measurement scales: nominal (i.e., numbers signify categories), ordinal (i.e., numbers signify ranks), interval (i.e., signifies quantity and units are equal), and ratio (i.e., same as interval but with an absolute zero). It is important to know the variables’ measurement scale because this is what determines which statistical test you will need to conduct to test your hypothesis.

Consider for example, a study assessing the relationship between BMI and hypertension. The independent variable would be BMI and the dependent variable would be hypertension. BMI (the independent variable) can be defined in many different ways:

1. Nominal Not Obese vs. Obese

2. Ordinal Underweight, Normal, Overweight, vs. Obese

3. Interval/ratio Actual BMI score

Hypertension (the dependent variable) can be defined in different ways:

1. Nominal Not Hypertensive vs. Hypertensive

2. Ordinal Not Hypertensive, Borderline Hypertensive, vs. Hypertensive

3. Interval/ratio Actual systolic and diastolic numbers

If, for example, a researcher decided to measure BMI and hypertension using a nominal scale of measurement, the researcher would conduct one of two statistical tests:

1. Cross-tabulation with chi-square

2. Logistic regression

If the researcher decided to measure both variables using an ordinal scale, the researcher would conduct one of two statistical tests:

1. Multinomial logistic regression

2. Ordinal regression

Lastly, if the researcher decided to measure both variables using an interval/ratio scale, the researcher would conduct one of two tests:

1. Pearson correlation

2. Linear regression

A researcher can, of course, have various combinations, where the independent variable is measured using a nominal scale and the dependent variable is measured using an interval/ratio scale. In these circumstances, the researcher would conduct one of two tests:

1. Independent t-test (if the independent variable has two categories)

2. One-way ANOVA (if the independent variable has three or more categories)

3. Linear regression

One advantage of measuring variables using an interval or ratio scale is that you can always code the variables (later, once you are analyzing the data) into variables measured using a nominal or ordinal scale. It is thus very important to do an extensive literature review and spend time defining and operationalizing the study variables.

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