Data Analysis

The Data Analysis Process

1. Understanding the nature of the problem. Effective data analysis requires an understanding of the research problem. We must know the goal of the research and what questions we hope to answer. It is important to have a clear direction before gathering data to ensure that we will be able to answer the questions of interest using the data collected. 2. Deciding what to measure and how to measure it. The next step in the pro- cess is deciding what information is needed to answer the questions of interest. In some cases, the choice is obvious. For example, in a study of the relationship between the weight of a Division I football player and position played, you would need to collect data on player weight and position. In other cases the choice of information is not as straightforward. For example, in a study of the relationship between preferred learning style and intelligence, how would you define learning style and measure it? What measure of intelligence would you use? It is important to carefully define the variables to be studied and to develop appropriate methods for determining their values. 3. Data collection. The data collection step is crucial. The researcher must first decide whether an existing data source is adequate or whether new data must be collected. If a decision is made to use existing data, it is important to understand how the data were collected and for what purpose, so that any resulting limitations are also fully understood. If new data are to be collected, a careful plan must be developed, because the type of analysis that is appropriate and the subsequent conclusions that can be drawn depend on how the data are collected. 4. Data summarization and preliminary analysis. After the data are collected, the next step is usually a preliminary analysis that includes summarizing the data graphically and numerically. This initial analysis provides insight into important characteristics of the data and can provide guidance in selecting appropriate methods for further analysis. 5. Formal data analysis. The data analysis step requires the researcher to select and apply statistical methods. Much of this textbook is devoted to methods that can be used to carry out this step. 6. Interpretation of results.Several questions should be addressed in this final step. Some examples are: What can we learn from the data? What conclusions can be drawn from the analysis? How can our results guide future research? The interpretation step often leads to the formulation of new research questions. These new questions lead back to the first step. In this way, good data analysis is often an iterative process.

Data Analysis Process 1

Throughout your personal and professional life, you will need to understand and use data to make decisions. To do this, you must be able to 1. decide whether existing data is adequate or whether additional information is required. 2. If necessary, collect more information in a reasonable and thoughtful way. 3. Summarize the available data in a useful and informative manner. 4. analyze the available data. 5. draw conclusions, make decisions, and assess the risk of an incorrect decision.