Chapter 14: Quantitative Analysis, descriptive statistics
Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constricts. Inferential analysis refers to the statistical testing of hypotheses (Theory Testing). Basically, it refers to using statistical data to understand the results of a study. This chapter addresses each step that one must take when doing Quantitative Analysis.
Data Preparation
In this section, we see the first step, data preparation which is what it sounds like, collecting and organizing data. There are many things that go into this step such as coding. They define coding as the process of converting data into numeric format. Coding requires the use of codebooks which contains detailed description of each variable in a research study, the response scale for each item whether on a nominal, ordinal, interval, or ratio scale and how to code each value into numeric format. This coded data is then entered into a spreadsheet, database, text file, or directly into a statistical program like SPSS. During this process one must check if there is missing data within a data set and address this before entering it into a database. Once everything is entered, Data transformation is needed to help create a more clearly interpreted data set.
Univariate Analysis
In this section, Univariate analysis is the analysis of a single variable which they say refers to a set of statistical techniques that can describe the general properties of one variable including frequency distribution, central tendency and dispersion. Frequency distribution is a number of times an individual value or range of values appear. Central tendency is an estimate of the center of a distribution which is found using mean, median and mode. As I understood, dispersion is how far apart a variable is from the central tendencies like mean. Range and standard deviation measure the dispersion. Standard deviation is said to correct for outliers, the formula is a shown below.
Bivariate Analysis
In this section, we look at Bivariate analysis, which is the analysis of two variables and how they relate to one another. A Bivariate correlation is the most common, between -1 and +1 showing how strong of a relationship the two variables have. The formula for bivariate correlation is as shown below.
Thank you for the summary of chapter 14, and the biggest takeaway from this chapter is organization! Researchers focus a lot on organizing the data they receive, which makes it easier for readers to visualize and process. The coding portion is interesting to read about, and I know there are many factors and techniques that researchers have at their disposal. For me, the best way for me to really grasp the organizational process is to do it myself. Especially with coding, learning and doing individually makes it a lot easier to understand what the author is trying to say. Great post!
Thank you for the summary! This chapter could be a lot to take in if you don’t enjoy statistics and data. There is a lot of information, formulas and definitions throughout the chapter. Coding is one of the most important parts of the chapter as it is how it is organized and can be deciphered. I have to think that statistical programs like SPSS have allowed for easier breakdown of the data and ability to incorporate it on a larger scale. It also allows for correlations to be computed in an easier manner so we can see if the pattern is real or “statistically significant,”