Leniency Errors

(noun)

Performance appraisal bias in which a manager or rater rate an employee too positively.

Related Terms

Examples of Leniency Errors in the following topics:

  • Evaluating Performance: Who, What, and How

    • Detriments of the PA system include the possible hindrance of quality control, stress for both employees and management, errors in judgment, legal issues arising from improper evaluations, and the implementation of inappropriate performance goals.
    • The most common problems in this area are leniency errors, halo effect errors, and central tendency errors.
    • Employee comparison methods attempt to evade the leniency and central tendency errors.
    • These both carry positive leniency as a major risk.

  • Evaluating Employee Performance

    • Detriments of the PA system include the possible hindrance of quality control, stress for both employees and management, errors in judgment, legal issues arising from improper evaluations, and the implementation of inappropriate performance goals.
    • This method eliminates central-tendency and leniency errors but still allows for halo-effect errors to occur.

  • Structuring Employee Feedback

    • Employee Comparison Models: Two of the main culprits of subjectivity are leniency error and central-tendency error (judging to favorably and judging everyone the same respectively).
    • This does incur halo effect errors, however.

  • All Pairwise Comparisons Among Means

    • We will take as an example the case study "Smiles and Leniency. " In this study, the effect of different types of smiles on the leniency showed to a person was investigated.
    • Let's return to the leniency study to see how to compute the Tukey HSD test.
    • For example, the following shows the ANOVA summary table for the "Smiles and Leniency" data.
    • The column labeled MS stands for "Mean Square" and therefore the value 2.6489 in the "Error" row and the MS column is the "Mean Squared Error" or MSE.
    • Compute a Sum of Squares Error (SSE) using the following formula

  • Proportion of Variance Explained

    • Consider, for example, the "Smiles and Leniency" case study.
    • A histogram of the dependent variable "leniency" is shown in Figure 1.
    • It is clear that the leniency scores vary considerably.
    • "An alternative way to look at the variance explained is as the proportion reduction in error.
    • where MSE is the mean square error and k is the number of conditions.

  • One-Factor ANOVA (Between Subjects)

    • In the smiles and leniency study, k = 4 and the null hypothesis is
    • It is traditional to call unexplained variance error even though there is no implication that an error was made.
    • For the "Smiles and Leniency" study, SSQtotal = 377.19.
    • For the "Smiles and Leniency" study, the values are:
    • The rounding errors have been corrected.

  • Exercises

    • Which of these measures of central tendency will change when you correct the recording error?
    • The following question is from the Smiles and Leniency (SL) case study.
    • (SL#2) Find the mean, median, standard deviation, and interquartile range for the leniency scores of each of the four groups.

  • Analysis of Variance Designs

    • In the case study "Smiles and Leniency," the effect of different types of smiles on the leniency showed to a person was investigated.
    • Four different types of smiles (neutral, false, felt, miserable, on leniency) were shown.

  • Overview of Statement Changes and Errors

    • Despite best efforts, occasionally an error is made on the financial statement and must be corrected.
    • Despite best efforts, occasionally an error is made on the financial statement.
    • Please note: an error correction is the correction of an error in previously issued financial statement; it is not an accounting change.
    • A counterbalancing error has occurred when an error is made that cancels out another error.
    • If the error has not counterbalanced then an entry must be made to retained earnings.

  • Chance Error

    • Random, or chance, errors are errors that are a combination of results both higher and lower than the desired measurement.
    • While conducting measurements in experiments, there are generally two different types of errors: random (or chance) errors and systematic (or biased) errors.
    • A random error makes the measured value both smaller and larger than the true value; they are errors of precision.
    • In this case, there is more systematic error than random error.
    • In this case, there is more random error than systematic error.