Fred S. Switzer

A Meta-Analytic Review of Predictors of Job Performance for Salespeople

This meta-analysis evaluated predictors of both objective and subjective sales performance. Biodata measures and sales ability inventories were good predictors of the ratings criterion, with corrected rs of .52 and .45, respectively. Potency (a subdimension of the Big 5 personality dimension Extraversion) predicted supervisor ratings of performance (r = .28) and objective measures of sales (r = .26). Achievement (a component of the Conscientiousness dimension) predicted ratings (r = .25) and objective sales (r = .41). General cognitive ability showed a correlation of.40 with ratings but only .04 with objective sales. Similarly, age predicted ratings (r = .26) but not objective sales (r = -.06). On the basis of a small number of studies, interest appears to be a promising predictor of sales success.

Missing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques:

Researchers in many fields use multiple item scales to measure important variables such as attitudes and personality traits, but find that some respondents failed to complete certain items. Past missing data research focuses on missing entire instruments, and is of limited help because there are few variables to help impute missing scores and the variables are often not highly related to each other. Multiple item scales offer the unique opportunity to impute missing values from other correlated items designed to measure the same construct. A Monte Carlo analysis was conducted to compare several missing data techniques. The techniques included listwise deletion, regression imputation, hot-deck imputation, and two forms of mean substitution. Results suggest that regression imputation and substituting the mean response of a person to other items on a scale are very promising approaches. Furthermore, the imputation techniques often outperformed listwise deletion.

Meta-analyzing the relationship between grades and job performance.

Employers and academics have differing views on the value of grades for predicting job performance. Employers often believe grades are useful predictors, and they make hiring decisions that are based on them. Many academics believe that grades have little predictive validity. Past meta-analyses of the grades-performance relationship have suffered either from small sample sizes or the inability to correct observed correlations for research artifacts. This study demonstrated the observed correlation between grades and job performance was.16. Correction for research artifacts increased the correlation to the.30s. Several factors were found to moderate the relationship. The most powerful factors were the year of research publication and the time between graduation and performance measurement.

A Monte Carlo Analysis of Missing Data Techniques in a HRM Setting

Researchers have examined various techniques to solve the problem of missing data. Simple techniques have included listwise deletion, pairwise deletion, mean substitution, regression imputation and hot-deck imputation. Past research suggests that regression imputation and pairwise deletion generally result in less dispersion around true score values while listwise deletion results in more dispersion around true scores. Unfortunately, this research spent much less time examining whether the various techniques lead to overestimation or underestimation of the true values of various statistics. The present study utilized a Monte Carlo Analysis to simulate an HRM research setting to evaluate missing data techniques. Pairwise deletion resulted in the least dispersion around true scores and least average error of any missing data technique for calculating correlations. Implications for use of these techniques and future missing data research were explored.

Systematic Data Loss in HRM Settings: A Monte Carlo Analysis

The accuracy of eight missing data techniques (MDTs) under conditions of systematically missing data was tested using a Monte Carlo analysis. Data were generated from a population correlation matrix, then deleted using several patterns that might be found in a human resource management (HRM) selection validation study. The results indicated that listwise and pairwise deletion were the most accurate methods, followed closely by imputation methods such as regression and hot-deck. Mean substitution was substantially inferior to the other methods tested. Future research that examines different missing data patterns is recommended.

Opioid Analgesia in Persons at Risk for Hypertension

Objective: Acute pain sensitivity is reduced in clinical hypertension, but the precise relationship between pain perception and altered blood pressure control is not well-characterized. A negative correlation between resting blood pressure and pain sensitivity is observed throughout the normotensive range, suggesting links between basic mechanisms of blood pressure control and pain regulation. The opioid peptides are important endogenous analgesic mechanisms, but their role in the hypoalgesia of blood pressure elevations has not been well-established. The current study sought to examine the effects of endogenous opioids on blood pressure-associated hypoalgesia in young adults at risk for hypertension development. Methods: The effects of the opioid receptor antagonist, naltrexone, on cold pressor pain sensitivity were assessed in young adult men (n = 49) and women (n = 76) with mildly elevated casual blood pressure. Results: Results indicate interactions between hypertension risk and the effects of opioid blockade on pain sensitivity. Conclusions: These findings suggest exaggerated opioid analgesia in persons at enhanced risk for hypertension and point to important links between altered neuropeptide regulation of pain and altered blood pressure control mechanisms in the early stages of hypertension. HR = heart rate; SBP = systolic blood pressure; DBP = diastolic blood pressure; MAP = mean arterial pressure; HPA = hypothalamic pituitary adrenocortical; CRF = corticotropin-releasing factor.

Modeling the behavior of the 4/5ths rule for determining adverse impact: Reasons for caution.

The Equal Employment Opportunity Commissions 4/5ths rule has been used for over 20 years in applied psychology and employment law. The rule signals that there is adverse impact when the protected group selection ratio is less than 80% of the highest scoring groups selection ratio. We conducted several simulations and found, consistent with some previous management science literature, that the 4/5ths rule often resulted in false-positive readings of adverse impact even when there were no underlying (population) standardized group differences between subgroups. We then incorporated tests of statistical significance and found that adding such tests to the 4/5ths rule eliminated many false-positive indications of adverse impact. We also examined simulated selection systems based on meta-analytic values from the selection literature. The frequency of adverse impact signals from the 4/5ths rule increased markedly relative to simulations with no subgroup population differences. Adding statistical tests mitigated the number of indications of adverse impact to some extent.

Bootstrap Estimates of Standard Errors in Validity Generalization

Bootstrapping is introduced as a method for approximating the standard errors of validity generalization (VG) estimates. A Monte Carlo study was conducted to evaluate the accuracy of bootstrap validity-distribution parameter estimates, bootstrap standard error estimates, and nonparametric bootstrap confidence intervals. In the simulation study we manipulated the sample sizes per correlation coefficient, the number of coefficients per VG analysis, and the variance of the distribution of true correlation coefficients. The results indicate that the standard error estimates produced by the bootstrapping procedure were very accurate. It is recommended that the bootstrap standard-error estimates and confidence intervals be used in the interpretation of the results of VG analyses

Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves

Driving while sleepy is a serious contributor to automobile accidents. Previous research has shown that drowsy drivers produce systematic errors (variability) in vehicle behavior which are detectable using vehicle monitoring technology. The current study developed a new methodological approach using a vehicle heading difference metric to detect drowsy driving more effectively than other more commonly used methods. Twenty participants completed a driving scenario as well as several measures of fatigue in five testing sessions across a night of sleep deprivation. Each simulated highway driving session lasted 20 min, and was analyzed for lateral lane position variability and vehicle heading difference variability with two statistical methods. Fatigue measures monitored reaction time, attention, and oculomotor movement. The results showed that examining lane heading difference using the absolute value of the raw data detected driving variability better across the night than other statistical models. The results from the fatigue measures indicated an increase in reaction time and response lapses, as well as a decrease in oculomotor reactivity across the night. These results suggest that in fatigued drivers the statistical model using the absolute value of lane heading could be an improved metric for drowsy driving detection that could accurately detect detriments in driving ability at lower levels of fatigue.

Changes in nurses’ decision making during a 12-h day shift

BACKGROUND Although shift work is necessary in many health-care settings, research suggests that it can have detrimental effects on performance in health-care providers. AIMS To determine if a change in decision-making occurred across a 12-h day shift in a sample of registered nurses. METHODS The participants were nurses working a 12-h day shift (7 a.m.-7 p.m.) at a large hospital in the south-eastern USA. Participants completed a policy-capturing questionnaire, examining their likelihood of calling a physician in response to specific patient symptoms, at the beginning and end of the shift. They also completed self-report surveys on alertness, stress and sleepiness. RESULTS Sixty-five nurses completed the study, an overall response rate of 41%. Participants significantly changed their decision-making policies from the beginning to the end of the work shift and also became significantly less alert and more stressed. However, there was no correlation between decision-making and reported alertness and stress. CONCLUSIONS These results suggest that medical judgment in registered nurses changed from the beginning to the end of a 12-h day shift. One possible underlying mechanism responsible for the changes seen across the shift could be the ability to maintain attention, as suggested by the Controlled Attention Model. The current results expand upon previous research, indicating there are a variety of negative outcomes associated with shift work.