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Related Concept Videos

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Reliability and Validity01:29

Reliability and Validity

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.
Calculating Standard Deviation01:08

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.       
Let us...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...

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Related Experiment Video

Updated: Jul 5, 2026

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
09:16

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

Published on: March 14, 2018

Statistical significance and reliability of single-subtest and cluster deviation scores.

Leonard S Feldt1, Richard A Charter

  • 1The University of Iowa, USA.

Psychological Reports
|May 17, 2008
PubMed
Summary

This study introduces deviation scores for analyzing individual subtest or cluster performance against a composite score. It provides statistical methods to test the significance and reliability of these deviation scores.

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Last Updated: Jul 5, 2026

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
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Area of Science:

  • Psychometrics
  • Statistical Analysis
  • Educational Measurement

Background:

  • Composite scores are common in standardized testing.
  • Understanding individual subtest or subtest cluster performance relative to a composite is crucial.
  • Existing methods may not adequately address the statistical significance and reliability of these specific deviation scores.

Purpose of the Study:

  • To define and introduce individual subtest deviation scores and cluster deviation scores.
  • To develop formulas for testing the statistical significance of these deviation scores.
  • To provide methods for assessing the internal consistency reliability of these deviation scores.

Main Methods:

  • Defining a composite score as the mean of K standardized subtest scores.
  • Calculating individual subtest deviation scores (subtest score - composite score).
  • Calculating cluster deviation scores (cluster average - composite score).
  • Deriving formulas for statistical significance testing.
  • Deriving formulas for internal consistency reliability.

Main Results:

  • Formulas for testing the statistical significance of individual subtest deviation scores are presented.
  • Formulas for testing the statistical significance of cluster deviation scores are presented.
  • Formulas for assessing the internal consistency reliability of these deviation scores are provided.

Conclusions:

  • The study provides novel statistical tools for analyzing specific score patterns within a test battery.
  • These methods allow for a more nuanced understanding of examinee performance beyond the overall composite.
  • The developed formulas enhance the psychometric rigor of interpreting deviation scores in standardized assessments.