More specifically, a research design refers to the type of study that will be conducted, whether it will be pre-experimental, quasi-experimental, or true experimental.
A cross-sectional designs provides a snapshot of the variables included in the study, at one particular point in time. It may reveal how those variables are represented in a cross-section of a population. Cross-sectional designs generally use survey techniques to gather data, for example, the U.S. Census.
Advantages and Disadvantages of Cross-Sectional Designs
|data on many variables||increased chances of error|
|data from a large number of subjects||increased cost with more subjects|
|data from dispersed subjects||increased cost with each location|
|data on attitudes and behaviors||cannot measure change|
|answers questions on who, what, when, where||cannot establish cause and effect|
|good for exploratory research||no control of independent variable|
|generates hypotheses for future research||difficult to rule out rival hypotheses|
|data useful to many different researchers||static, time bound|
A longitudinal design collects data over long periods of time. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. There are two different types of longitudinal designs: time series and panel.
A Time Series Design collects data on the same variable at regular intervals (weeks, months, years, etc.) in the form of aggregate measures of a population. For example, the Consumer Price Index (CPI), the FBI Uniform Crime Rate, unemployment rates, poverty rates, etc.
|data easy to collect||data collection method may change over time|
|easy to present in graphs||difficult to show more than one variable at a time|
|easy to interpret||needs qualitative research to explain fluctuations|
|can forecast short term trends||assumes present trends will continue unchanged|
Panel Designs collect repeated measurements from the same people or subjects over time. Panel studies reveal changes at the individual level, for example, when a particular person was employed or unemployed, or when they were on or off of welfare.
Panel data can show different patterns from time series data.
For example, about 5% of the elderly are institutionalized at any one time,
but it is not always the same people. So elderly people have a 20%
chance of being institutionalized at some point.
Advantages and Disadvantages of Panel Designs
|reveals individual level changes||difficult to obtain initial sample of subjects|
|establishes time order of variables||difficult to keep the same subjects over time|
|can show how relationships emerge||repeated measures may influence subjects behavior|
|includes data from multiple perspectives||limited to contemporary phenomena|
|combines data from different sources||need direct access to subjects|
|need diverse sources of information|
|need skills in many techniques|
|can be an intense experience|
|difficult to replicate findings|
|insiders can be biased|
|outsiders can be naive|
|difficult to draw boundaries|
A meta-analysis is a quantitative analysis of a sample of existing research studies on a particular topic. It is used to draw conclusions about the topic from a range of studies, for example, identify aspects of a program associated with program success. A meta-analysis may also generate new hypotheses for future research.
2. One-group Pre-Test/Post-Test Design
3. Non-randomized comparison group Pre-Test/Post-Test Design
4. One-group Time Series Design
1. Case study design
2. Randomized Control Group Post-Test only design
3. Randomized Control Group Pre-Test/Post-test design
4. Control group Time Series Design