A research design is the set of methods and procedures used in collecting and analyzing measures of the variables specified in the research problem research. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal case study), research problem, hypotheses, independent and dependent variables, experimental design, and, if applicable, data collection methods and a statistical analysis plan. Research design is the framework that has been created to find answers to research questions.
- 6.1: Scientific Method
- To be a proper scientific investigation the data must be collected systematically. However, scientific investigation does not necessarily require experimentation in the sense of manipulating variables and observing the results. Observational studies in the fields of astronomy, developmental psychology, and ethology are common and provide valuable scientific information.
- 6.2: Measurement
- The collection of data involves measurement. Measurement of some characteristics such as height and weight are relatively straightforward. The measurement of psychological attributes such as self esteem can be complex. A good measurement scale should be both reliable and valid. These concepts will be discussed in turn.
- 6.3: Data Collection
- Most statistical analyses require that your data be in numerical rather than verbal form (you can’t punch letters into your calculator). Therefore, data collected in verbal form must be coded so that it is represented by numbers.
- 6.4: Sampling Bias
- Descriptions of various types of sampling such as simple random sampling and stratified random sampling are covered in another section. This section discusses various types of sampling biases including self-selection bias and survivorship bias. Examples of other sampling biases that are not easily categorized will also be given. It is important to keep in mind that sampling bias refers to the method of sampling, not the sample itself.
- 6.5: Experimental Designs
- There are many ways an experiment can be designed. For example, subjects can all be tested under each of the treatment conditions or a different group of subjects can be used for each treatment. An experiment might have just one independent variable or it might have several. This section describes basic experimental designs and their advantages and disadvantages.
- 6.6: Causation
- The concept of causation is a complex one in the philosophy of science. Since a full coverage of this topic is well beyond the scope of this text, we focus on two specific topics: (1) the establishment of causation in experiments and (2) the establishment of causation in non-experimental designs.
- 6.7: Statistical Literacy
- A low level of HDL have long been known to be a risk factor for heart disease. Taking niacin has been shown to increase HDL levels and has been recommended for patients with low levels of HDL. The assumption of this recommendation is that niacin causes HDL to increase thus causing a lower risk for heart disease. What experimental design involving niacin would test whether the relationship between HDL and heart disease is causal?
Flowchart of four phases (enrollment, intervention allocation, follow-up, and data analysis) of a parallel randomized trial of two groups. Image use with permission (CC BYT-SA 3.0; PrevMedFellow).
Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.