By Asha Burks John Foster Dominique Peete Philip Tillman LaWanda Clayborn EDPR 2111 Cross Sectional This design is the most common type of study It is used to compare surveys Ex A survey of different groups So two groups take a survey then you compare the results ID: 267553
Download Presentation The PPT/PDF document "Experimental designs" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Experimental designs
By: Asha Burks, John Foster, Dominique Peete, Philip Tillman, LaWanda Clayborn
EDPR 2111Slide2
Cross Sectional
This design is the most common type of study. It is used to compare surveys.Ex: A survey of different groups. So two groups take a survey then you compare the results.Strengths: it’s cheap to do, you can receive results quickly, and you get comparisons of multiple cohorts
Limitations
: no track of individual change, impossible to detangle age effects from cohort effectsSlide3
Longitudinal
This one uses one group at two different times. Ex: When you take a class and you get a first of the year assessment and then a end of the year assessment to see where you are. Strengths: It gives an extended period of time ( you can track individual change), you can make comparisons of age effects, and you can track individual growth and change due to age
Limitations
: Takes longer, requires more resources, you have to deal with the “drop out” effect, and the study can become biasedSlide4
Cross- Sequential
It uses a cross sectional sample at two different times.Ex: Using the assessment at the beginning of the year and the end of year in two different classes. Then compare the results between the two classes. Strengths: It disentangles age/ cohort effects
Limitations
: It involves more time and effort, and it entails more complex data analysis