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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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...
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Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Reliability and Validity01:29

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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Measurement Model Quality, Sample Size, and Solution Propriety in Confirmatory Factor Models.

Phill Gagne, Gregory R Hancock

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    Sample size recommendations for confirmatory factor analysis (CFA) now depend on model quality, not just sample size. Simulations show construct reliability also impacts CFA model convergence and parameter estimation accuracy.

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    Area of Science:

    • Psychometrics
    • Statistical Modeling

    Background:

    • Traditional sample size recommendations for Confirmatory Factor Analysis (CFA) are increasingly viewed as insufficient.
    • Recent shifts emphasize model quality over simple observation counts per variable or parameter.

    Purpose of the Study:

    • To investigate the impact of sample size (n) and construct reliability on CFA model convergence and parameter estimation accuracy.
    • To extend previous research on sample size and CFA performance.

    Main Methods:

    • Conducted simulation studies to assess the effects of sample size and construct reliability.
    • Examined measurement model quality indicators: number of indicators per factor (p/f) and factor loading magnitude.

    Main Results:

    • Both sample size (n) and construct reliability significantly affect CFA model convergence.
    • Accuracy of parameter estimation is influenced by both n and construct reliability.
    • These effects were observed across different levels of sample size.

    Conclusions:

    • Applied researchers using CFA should consider both sample size and construct reliability for robust model results.
    • The study provides updated sample size recommendations for CFA based on design characteristics.