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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
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Selecting a sample size for studies with repeated measures.

Yi Guo, Henrietta L Logan, Deborah H Glueck

    BMC Medical Research Methodology
    |August 2, 2013
    PubMed
    Summary
    This summary is machine-generated.

    Selecting sample size for repeated measures designs is complex. This guide offers practical steps for calculating sample size, focusing on time by treatment interactions in dental pain research.

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    Area of Science:

    • Statistics
    • Biostatistics
    • Research Methodology

    Background:

    • Repeated measures designs offer higher statistical power and detect within-person changes over time compared to cross-sectional designs.
    • Sample size selection for repeated measures is challenging due to numerous input requirements.
    • This study provides guidance for sample size calculation in repeated measures designs, using a dental pain study as an example.

    Discussion:

    • The paper details how to gather necessary inputs for sample size calculations.
    • Guidance is provided on selecting appropriate statistical software for these calculations.
    • Practical issues like missing data, multiple study aims, and continuous covariates are addressed.

    Key Insights:

    • The study offers a structured approach to sample size determination for time by treatment interactions in repeated measures studies.
    • It simplifies the complex process of sample size selection for researchers.
    • The guidance is applicable to various fields employing longitudinal data analysis.

    Outlook:

    • Future research should explore advanced methods for sample size calculation considering complex data structures.
    • Developing user-friendly software tools for repeated measures sample size calculation is recommended.
    • Further studies can validate these methods across different therapeutic areas and research questions.