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Rather than studying particular individuals across that whole period of time (e.g. 20–60 years) as in a longitudinal design, or multiple individuals of different ages at one time (e.g. 20, 25, 30, 35, 40, 45, 50, 55, and 60 years) as in a cross-sectional design, the researcher chooses a smaller time window (e.g. 20 years) to study multiple ...
In medical research, epidemiology, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data. [definition needed]
Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
The pour plate technique is the typical technique used to prepare plate count agars. Here, the inoculum is added to the molten agar before pouring the plate. The molten agar is cooled to about 45 degrees Celsius and is poured using a sterile method into a petri dish containing a specific diluted sample.
Colony-forming units are used to quantify results in many microbiological plating and counting methods, including: The pour plate method wherein the sample is suspended in a Petri dish using molten agar cooled to approximately 40–45 °C (just above the point of solidification to minimize heat-induced cell death).
In medicine, a crossover study or crossover trial is a longitudinal study in which subjects receive a sequence of different treatments (or exposures). While crossover studies can be observational studies, many important crossover studies are controlled experiments, which are discussed in this article.
In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are all associated with the same single period or point in time. This type of cross-sectional analysis is in contrast to a time-series regression or longitudinal regression in which the variables are considered ...
Cross-sectional data differs from time series data, in which the same small-scale or aggregate entity is observed at various points in time. Another type of data, panel data (or longitudinal data ), combines both cross-sectional and time series data aspects and looks at how the subjects (firms, individuals, etc.) change over a time series.