Quantitative Research & Data Analysis

Quantitative Research and Data Analysis

Quantitative research and data analysis is all about quantifying relationships between variables. You measure variables and express the relationship between variable using effect statistics, such as correlations, relative frequencies, or differences between means.

ValueScope can help you:

• Determine the variables you need to measure
• Design a quantitative analysis approach
• Work within the size of the sample
• Understand the relationships

Statistical Significance

Statistical significance is the standard but somewhat complicated approach. Your sample size has to be big enough for you to be sure you will detect the smallest worthwhile effect or relationship between your variables. To be sure means detecting the effect 80% of the time. Detect means getting a statistically significant effect, which means that more than 95% of the time you'd expect to see a value for the effect numerically smaller than what you observed, if there was no effect at all in the population (in other words, the p value for the effect has to be less than 0.05). Smallest worthwhile effect means the smallest effect that would make a difference to the lives of your subjects or to your interpretation of whatever you are studying. If you have too few subjects in your study and you get a statistically significant effect, most people regard your finding as publishable.

Confidence Intervals

Using confidence intervals or confidence limits is a more accessible approach to sample-size estimation and interpretation of outcomes. You simply want enough subjects to give acceptable precision for the effect you are studying. Precision refers usually to a 95% confidence interval for the true value of the effect: the range within which the true (population) value for the effect is 95% likely to fall. Acceptable means it won't matter to your subjects (or to your interpretation of whatever you are studying) if the true value of the effect is as large as the upper limit or as small as the lower limit. A bonus of using confidence intervals to justify your choice of sample size is that the sample size is about half what you need if you use statistical significance.

Dependent and Independent Variables

Usually you have a good idea of the question you want to answer. That question defines the main variables to measure. Next, identify all the things that could affect the dependent variable. These things are the independent variables.