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Related Concept Videos

Types of Skewness01:09

Types of Skewness

If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
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.
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
Self-Discrepancy and Its Effects01:29

Self-Discrepancy and Its Effects

Self-discrepancy theory explains how people compare their actual self to their ideal and ought selves and how mismatches between these self-guides can lead to emotional distress. Developed by E. Tory Higgins, the theory distinguishes among three components of self-concept: the actual self, the ideal self, and the ought self. These refer respectively to how individuals perceive themselves, how they aspire to be, and how they believe they are obligated to be. Emotional well-being, self-esteem,...
Self-Discrepancy Theory02:45

Self-Discrepancy Theory

One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.

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Related Experiment Video

Updated: Jul 18, 2026

Assessing the Coherence of Parents' Short Narratives Regarding their Child Using the Five-Minute Speech Sample Procedure
07:56

Assessing the Coherence of Parents' Short Narratives Regarding their Child Using the Five-Minute Speech Sample Procedure

Published on: September 19, 2019

Skew and internal consistency.

Tammy Greer1, William P Dunlap, Samuel T Hunter

  • 1Department of Psychology, The University of Southern Mississippi, Hattiesburg, MS, USA. tammy.greer@usm.edu

The Journal of Applied Psychology
|November 15, 2006
PubMed
Summary

Skewed data can slightly decrease reliability estimates like standardized item alpha. This effect is more pronounced with greater skew, fewer items, and low correlations between items.

Related Experiment Videos

Last Updated: Jul 18, 2026

Assessing the Coherence of Parents' Short Narratives Regarding their Child Using the Five-Minute Speech Sample Procedure
07:56

Assessing the Coherence of Parents' Short Narratives Regarding their Child Using the Five-Minute Speech Sample Procedure

Published on: September 19, 2019

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Survey Design

Background:

  • Standardized item alpha is a widely used reliability coefficient.
  • Its accuracy may be influenced by the distributional properties of the data.
  • Skewness is a common characteristic of real-world data that can affect statistical assumptions.

Purpose of the Study:

  • To investigate the impact of data skewness on standardized item alpha.
  • To compare alpha values derived from normal versus skewed distributions.
  • To examine how skewness interacts with other factors like interitem correlation and the number of items.

Main Methods:

  • Monte Carlo simulation techniques were employed.
  • Comparisons were made between alpha coefficients calculated from normal, lognormal, ranked, and skewed Likert-type variables.
  • Variations included the degree and direction of skew, population interitem correlation (rho), number of items, and number of Likert categories.

Main Results:

  • Data skewness was found to decrease the average interitem correlation.
  • Skewness produced small decreases in standardized item alpha.
  • The largest decreases in alpha occurred under conditions of high skew, low rho, opposite skew directions across items, and a small number of items.

Conclusions:

  • Skewed data can attenuate standardized item alpha, potentially underestimating scale reliability.
  • Researchers should consider data distribution when interpreting alpha coefficients, especially in non-normally distributed datasets.
  • The findings highlight the importance of assessing distributional assumptions in psychometric analyses.