# Develop your own definition fo

Develop your own definition for cross-sectional statistics andlongitudinal statistical measurements. After you have done thatthen compare and contrast them. Along that line discuss thestrengths and weaknesses of both. Which do you favor? Why?

Let us first of all try to understand as to what a crosssectional statistics is and what exactly is a longitudinalstatistical measurements.

To begin with, a cross sectional statistics is a statisticalanalysis or study of a dataset from a given population or itssubset in/at a given period of time. For example – time series ofobservational data in a given experiment. Another example of it canbe a capturing of means of long jump distances of two groups ofathletes from different universities over a given period of timeand then to compare their means to check if their differences aresignificant or not.

A longitudinal study on the other hand refers to the analysiswhere the outcomes of output from the sampler /participant andpossibly the treatments or exposures are collected over variousfollow-up times.A longitudinal study generally yields multiple or\repeated” measurements on each subject.

For example,Key bosy parameters of Cancer patients of a givenhospital may be followed over time and monthly measures arecollected to characterize immune status and disease burdenrespectively.

These body parameters are then referenced and the data arecorrelated within subjects and thus require special statisticaltechniques for valid analysis and inference.

Differences

The key to understanding the difference between cross-sectionalstatisitcs and longitudinal studies is

1. time and

2. the amount of measurements required.

Usually since a cross sectional statistics involves real timecapturing of time based data ,it is much easier and quicker toperform in case one needs to solve a research question here andnow.

For example , let us assume that a given population has aspecific outcome (i.e. a disease) and now we want to find out if acertain exposure (i.e. drinking) could cause that outcome. So toascertian this, a cross-sectional statisitcal analysis will onlyrequire one contact to the patients/persons with the outcome tofind out if the person is exposed or not.

Now look at the longitudinal studies. The longitudinal studywill require a constant follow-up for the same validation ofoutcome owing ot certain factor (i.e drinking). A number ofexposures are measured at baseline and from there we need toobserve the population for the outcome.

Strengths andweaknesses

The cause and effect analysis /validation is much more reliablein a longitudinal study. However, it is marred by lack of real timedata over a long time and are therefore reported lessfrequently.

A cross-sectional statistics on the other hand have a very longsampling period. That means that it can take a long time to samplethe amount of people required to make statistical analyses. This isthe case when the outcome of interest is infrequent in thepopulation. But the people included in the study are not followedover time.

Usually the use of cross-sectional statistics refers to theresearcher decision based on SHORTAGE of TIME.

A longitudinal statistical design is stronger and effective thanthe cross sectional design because it incorporates a longersamplimg over long period of time and hence it has stronger inestablishing changes over time, relationships, casuality..etc.

Thus between the two, the longitudinal statistical measurmeentsshould be chosen since it gives us a better understanding of thecausal relationship of the data points over time .

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