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Related Concept Videos

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Factorial Design02:01

Factorial Design

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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Assessing the impact of common method variance on higher order multidimensional constructs.

Russell E Johnson1, Christopher C Rosen, Emilija Djurdjevic

  • 1Department of Management, Michigan State University, N438 North Business Complex, East Lansing, MI 48824-1121, USA. johnsonr@bus.msu.edu

The Journal of Applied Psychology
|December 15, 2010
PubMed
Summary

Common method variance (CMV) can distort relationships within predictors, impacting higher-order constructs like core self-evaluation. Applying remedies altered the construct and its link to job satisfaction, highlighting the need for careful consideration of CMV in research.

Related Experiment Videos

Last Updated: Jun 6, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Psychology
  • Organizational Behavior
  • Research Methodology

Background:

  • Common method variance (CMV) is a concern for predictor-criterion relationships.
  • CMV may also bias relationships among predictors, especially for multidimensional constructs.
  • Higher-order constructs are increasingly popular in psychological research.

Purpose of the Study:

  • To investigate how common method variance (CMV) affects higher-order constructs.
  • To examine the inflation of interrelationships among indicators of higher-order constructs due to CMV.
  • To assess the impact of CMV on the relationship between higher-order constructs and criteria.

Main Methods:

  • Examined core self-evaluation, a higher-order construct composed of self-esteem, generalized self-efficacy, emotional stability, and locus of control.
  • Applied statistical and procedural common method variance (CMV) remedies across two studies.
  • Utilized data from multiple samples to ensure robustness of findings.

Main Results:

  • Common method variance (CMV) remedies altered the nature of the higher-order construct (core self-evaluation).
  • The relationship between core self-evaluation and job satisfaction was modified after applying CMV remedies.
  • Findings indicate that CMV can significantly influence the structure and predictive validity of higher-order constructs.

Conclusions:

  • Common method variance (CMV) can distort the measurement and relationships of higher-order constructs.
  • Researchers should carefully consider and address potential common method variance (CMV) when studying complex constructs.
  • The findings have significant implications for the conceptualization and empirical investigation of higher-order constructs in organizational psychology.