W. Lee Grubb

The Fakability of Bar-On's Emotional Quotient Inventory Short Form: Catch Me if You Can

This study investigated the fakability of the Emotional Quotient Inventory Short Form (EQ-i:S), a mixed-model emotional intelligence test developed by Bar-On (2002). A sample of 229 undergraduate students from a southeastern university completed a battery of selection and assessment measures in both an honest and faking good condition. When responded to honestly, the EQ-i:Sis predicted by The Big Five with a multiple correlation of .79. Therefore, the EQ-i:S can be viewed as an aggregation of The Big Five constructs. When faking, respondents were able to improve scores on the EQ-i:S, each of its subtests, and each of The Big Five measures. Respondents improved scores on the EQ-i:S by .83 SD. Faking on the EQ-i:S was primarily predicted by cognitive ability and agreeableness. The relative ease with which respondents can substantially raise their scores limits the value of the EQ-i:S as an applicant screening tool. The substantial extent to which the EQ-i:S is predicted by The Big Five casts doubt on the construct of emotional intelligence as operationalized in the EQ-i:S.

Critical Problems Of Rural Small Businesses: A Comparison Of African-American And White-Owned Formation And Early Growth Firms

This study investigated the critical problems encountered by African American and White-owned formation and early growth firms in rural businesses. Analyses were conducted to determine the most critical types of problems encountered by these businesses and the relationship between the types of problems reported and the owners race and firms developmental stage. Strategic problems were the most critical problems by business owners in the sample, regardless of race or developmental stage, followed by administrative and operations. Although no significant relationship was found between the type of problems and the owners race, a significant relationship was found between problem type and the firms developmental stage, such that businesses in the formation stage are more likely to encounter strategic problems and early growth firms more often experience administrative problems.

Affirmative action decisions: when ignorance is bliss

Purpose – The purpose of this paper is to show how various organizational justifications for hiring decisions influence the beneficiarys perceptions of fairness. Specifically, the paper investigates the relative impacts of no justification, affirmative action justification and justifications based on attempts to improve organizational creativity.Design/methodology/approach – Participants were asked to read several vignettes in which the justification for the hiring decisions was manipulated. Fairness perceptions were then assessed for each scenario. Paired‐sample t tests were used to test hypotheses.Findings – The paper finds that perceptions of both procedural and distributive justice appeared to follow a common theme across Hispanic and African American subgroups where the hiring decision was perceived to be fairer when no justification was provided. Hiring decisions based on affirmative action and diversity programs designed to promote creativity were perceived as less fair by both African Americans a...

Toward measurement of social exchange resources: reciprocal contributions and receipts.

This study investigates development of measures for the social resources which may be exchanged in working relationships, such as between supervisors and subordinates or between employees and work group peers. Items designed to measure social resource categories were administered to a heterogeneous sample of 260 employees from diverse organizations. This sample was fairly evenly divided by sex, age, job type, and employment tenure. Analysis showed both contribution and receipt of social resources can be distinctly measured for four different categories (information, respect, liking, and effort).

Deliberate Faking on Personality and Emotional Intelligence Measures

This study examined the extent the Big Five personality traits and emotional intelligence can be faked. Using a student sample, the equivalence of measurement and theoretical structure of models in a faking and honest condition was tested. Comparisons of the models for the honest and faking groups showed the data fit better in the faking condition. These results suggest that faking does change the rank orders of high scoring participants. The personality dimensions most affected by faking were emotional stability and conscientiousness within the Big Five and the general mood and stress management dimensions of Bar-Ons Emotional Quotient Inventory–Short Form (1997) measure of emotional intelligence.

Cart 4.0

CART 4.0 is a decision tree software distributed by Salford Systems, Inc., a data mining software and consultancy firm (Salford Systems, 2000). Figure 1 shows a small decision tree created by CART from some simulated data. This decision tree seeks to predict job status 6 months after hire based on variables known at the time of hire. The three categories of job status are fired, quit, and still on the job. The variables used to predict job status are conscientiousness test scores, job classification (sales or customer service), and age. CART has a strong history of use in financial and marketing research. Most of the applications in finance, such as the selection of stocks, result in proprietary reports that are not publicly available. Likewise, the CART applications in marketing are also usually proprietary. However, there are many published applications of CART, ranging from language development (Delaney-Black et al., 2000), memory recovery from traumatic brain injury (Stuss et al., 2000), to relapse in schizophrenia (Doering et al., 1998). CART has also been used to conduct research in psychology on topics ranging from identifying risk for functional impairment (Lemsky, Smith, Malec, & Ivnik, 1996) to examining the relationship between job stress and mental health (Brook & Brook, 1995). In addition, CART has been used in management research to examine major league baseball salaries (Hoaglin & Velleman, 1995) and the prediction of business failures (Dimitras, Zanakis, & Zopounidis, 1996). In the realm of decision tree science, CART is known as a binary recursive partitioning algorithm. It is a partitioning algorithm because it seeks to partition a set of observations into subgroups. In the tree in Figure 1, CART has partitioned the data into 4 groups called terminal nodes. The terminal nodes are the nodes that are not further subdivided. CART is a binary algorithm because it splits each parent node into exactly two child nodes. Thus, at the top of the tree, there is a parent node containing all 384 observations in the data set. If CART splits a node, it always splits the node into two child nodes. In Figure 1, CART has split the parent node containing 384 observations using the conscientiousness test score. Those who have a conscientiousness score of 4 or lower are sent to the left node, and the remaining observations are sent to the right node. CART is a recursive algorithm because once it creates a child node, it then treats the child node as a parent node and attempts to split it. Thus, after the initial split created a child node containing all cases with a conscientiousness score of 4 or lower, CART treated that child node as a parent node and sought (successfully) to split the node again (this time on whether or not the job classification was a call center). One of the biggest problems in building decision trees is capitalization on chance. It is possible and likely that one can build a tree that will perfectly (or almost perfectly) describe a set of data. Such trees are very likely to have very poor predictive value because they find patterns that only fit the current data but do not generalize to new