Common statistical methods used in a research design
One of the courses I dreaded above all others in graduate school was research statistics. I have never been a strong student in math and my limited exposure to statistics only struck confusion in my mind and fear in my heart. Years later, I have found some statistical measures are not complicated once you understand their role and their definitions.
The ability to understand the mathematics of statistics requires one first understands the terminology associated with statistics.
Central tendency – The center of a distribution or set of scores or values
Dependent Variable (DV) – The DV is the variable measured following the introduction of the IV.
Independent Variable (IV) – The IV is the focus variable of the research.
Mean – The average of a set of scores divided by the number of scores in the set
Median – The score found at the middle of a set of scores or values
Mode – The number that appears most often in a set of scores or values
Null hypothesis (NH) – The NH is the prediction that no effect will result from introduction of the independent variable on the dependent variable.
Pearson r – The degree of relationship that exists between two variables ranging from -1.00 to +1.00. A direct relationship is a high positive near one. An inverse relationship is a high negative value close to negative one. A value of 0 indicates not relationship exists between the two variables.
Significance – The determination that an effect resulted from the introduction of the IV
Standard deviation (SD) – The measure of the variance from the mean found within a set of scores
t-test – The measurement of difference between the means from two samples.
Type 1 Errors – The null hypothesis was rejected but should not have been.
Type 2 Errors – The null hypothesis was not rejected but should have been.
Z score – A conversion of a score to a SD ranging from -1.00 to +1.00 in order to know the variance from the mean up to one SD
Types of Statistical Methods
The basic statistical terms and methods are either descriptive or inferential. Descriptive statistics simply describe the relationship that was found between two variables. Inferential statistics infer a relationship between two variables. An example of inferential statistics includes t-tests, ANOVA, MANOVA, and scatter analysis.
Statistics need not be a frightful experience and numerous resources are available to perform statistical calculations such as SPSS. Numerous resources are available online to help in determining which statistical methods would be appropriate depending upon the type of study.