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Data Analysis

A.J. Boggs offers a variety of techniques to analyze data into useful information. Descriptive statistics such as rates, ratios, percentages, cross-tabulations, and measures of central tendency can address many outcome monitoring and quality assurance requirements. For example:

• Has a performance goal or benchmark been met?
• What statistic best represents a particular variable?
• What is the percentage point difference between two time periods?
• Are there differences between subgroups on key variables?
• How does the current rate of change compare to the historical trend?

Samples are often used to make inferences about populations. Sample size, power analysis, margin of error, levels of measurement, and research questions are keys to selecting the best test statistics.

Examples of commonly used procedures include chi-square, ANOVA, and t-test. Significance testing within a well defined conceptual framework can address questions such as the following:

• Are two samples statistically different from each other on some variable of interest?
• What is the level of certainty when using the sample data to represent the population?
• Within what confidence interval is the real population value likely to actually exist?
• Is the observed difference between two time periods due to random chance?

Data collection and analysis techniques do not have to be complex but they should be methodologically sound to draw useful conclusions.

A.J. Boggs offers assistance in developing sound study processes with clear rationale and purpose, and appropriate methods of analysis.