Edisons Statistics

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ANALYSIS AND INTERPRETATION OF DATA Analysis and Interpretation of Data https://my.visme.co/render/1454658672/www.erau.edu Slide 1 Transcript In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of interest will be the focus for this module. Data and Statistics Match the data with an appropriate statistic Approaches based on data characteristics Collected for single or multiple groups Involve continuous or discrete variables Data are nominal, ordinal, interval, or ratio Normal or non-normal distribution Statistics serve two functions Descriptive: Describe what data look like Inferential: Use samples to estimate population characteristics Slide 3 Transcript There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics: 1. Have data been collected for a single group or multiple groups 2. Do the data involve continuous or discrete variables 3. Are the data nominal, ordinal, interval, or ratio, and 4. Do the data represent a normal or non-normal distribution. We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics. The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures of central tendency. Descriptive Statistics and Central Measures Descriptive statistics organize and present data Mode The number occurring most frequently; nominal data Quickest or rough estimate Most typical value Measures of central tendenc.

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