Cross-Sectional Design Vs. Longitudinal Designs
According to Berk (2014), longitudinal, cross-sectional, and sequential designs are forms of observational studies that research a particular variable and how it affects the participants over their lifespan. A longitudinal design measures a single individual or small group repeatedly over an extended period to provide information about changes. Regarding timeframe, longitudinal designs can relatively be short or long. On the contrary, a cross-sectional design can analyze people of diverse ages at a single moment in time. Since these studies have their pros and cons, we need a sequential study to rise above the conflicts of both cross-sectional and longitudinal design. This design will allow researchers to evaluate a cross-sectional or longitudinal
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Alternatively, a drawback to a longitudinal study is that it is time-consuming, costly, and age-related variations may be inaccurate. Moreover, member dropout, practice, and cohort outcomes may lead to the original sample no longer are representative of the population in which researchers would like to use. (Papalia, Feldman, and Martorell, 2014). Meanwhile, a cross-sectional design is more favorable economically; furthermore, it can indicate similarities and differences among age groups and minimizes the possibility of participant dropout and practice outcomes (Berk, 2014). As mentioned previously, a cross-sectional and longitudinal research has their strength and weakness; nevertheless, to overcome those faults a sequential design is required. The asset of this type of analysis is that we can compare both cross-sectional and longitudinal. Furthermore, it can show us the cohort effects as well as the age-related changes more competently than the longitudinal or cross-sectional design alone. Be that as it may, it can still be costly, and it will require a prolonged amount of time, determination, and a sophisticated analysis of the data (Berk,
Alternatively, a drawback to a longitudinal study is that it is time-consuming, costly, and age-related variations may be inaccurate. Moreover, member dropout, practice, and cohort outcomes may lead to the original sample no longer are representative of the population in which researchers would like to use. (Papalia, Feldman, and Martorell, 2014). Meanwhile, a cross-sectional design is more favorable economically; furthermore, it can indicate similarities and differences among age groups and minimizes the possibility of participant dropout and practice outcomes (Berk, 2014). As mentioned previously, a cross-sectional and longitudinal research has their strength and weakness; nevertheless, to overcome those faults a sequential design is required. The asset of this type of analysis is that we can compare both cross-sectional and longitudinal. Furthermore, it can show us the cohort effects as well as the age-related changes more competently than the longitudinal or cross-sectional design alone. Be that as it may, it can still be costly, and it will require a prolonged amount of time, determination, and a sophisticated analysis of the data (Berk,