A Mathematical Model To Identify Pre Turnover Mindset In Sophomore Students At The University

Academic institutions seek to understand why Science, Technology, Engineering and Math (STEM) students are leaving their programs and transferring into other majors. Previous research has identified multiple reasons for the student retention problem including attitudes toward the engineering field, student’s self-confidence levels, quality of instructor interactions, and robustness of the STEM curriculum. Some researchers suggest that more standardized quantitative measures for departmental environments need to be created, and more appropriate quantitative measurements need to be applied to studying STEM student attrition. This study demonstrates a methodology that will begin to fulfill this need. This paper reports the results of a study conducted at the University of Nebraska-Lincoln that used this methodology to evaluate measures affecting sophomore engineering students’ attrition. Results presented demonstrate that certain measures affect attrition in the College of Engineering & Technology (CoE&T) at the University of Nebraska. INTRODUCTION Academic organizations spend millions of dollars each year to recruit students into STEM majors. The National Science Foundation and other organizations have allocated funds to increase the enrollment of STEM students. Administrators may be able to avoid negative consequences to universities and students by identifying the STEM students who are experiencing high levels of Cognitive Turnover. Jones (2001) defined Cognitive Turnover (CT) as a mind-set that is created by a combination of turnover cognitions brought about by the negative impacts of burnout. Turnover is the voluntary cessation of membership in an organization by an individual who receives current or future compensation for participating in that organization (Mobley 1982). Turnover has cognitive indicators that predicate eventual departure. Chemiss (1980) defines burnout as “a syndrome of inappropriate attitudes toward others and toward self often associated with uncomfortable physical and emotional symptoms.” Maslach (1976) observed that burnout “appears to be a factor of organizational turnover, absenteeism, and low morale. While everyone may manifest this mind-set periodically, excessive CT (eCT) may be detrimental to the individual and the organizations they belong to. Subtle acts such as absenteeism, poor quality, and lack of discretionary effort are related to burnout and are common precursors to a person quitting an organization and become another turnover statistic. This research theorizes that eCT condition occurs when a person is absorbed with the thoughts of turnover created by organizationally driven burnout. For engineering students non-committal types behavior may originate from student stress and burnout created by class structure, administrative neglect, or lack of advisory support. BACKGROUND P ge 1.66.2 The Statistical Evaluation of Cognitive Turnover System (SECtCS) methodology was created by the lead author (Jones 2001) and was previously used to measure and evaluate Cognitive Turnover (CT) in engineering knowledge workers. The results demonstrated four valid constructs for a heterogeneous group of engineers across multiple companies. Results also included Statistical Process Control Charts that demonstrated both “in control” CT respondents and “out of control” CT, or eCT respondents Most undergraduate engineering students are susceptible to quitting engineering programs in the first two years of the program (Feldman 1998). Because of this fact, the test populations for this research were engineering students who are in the first two years of their engineering programs. This would include undergraduates that are 2 nd semester freshmen, and 1 st and 2 nd semester sophomores. This study’s test population consisted of 1 st semester sophomores. The objective of this study was to use the SECtCS methodology as a tool to identify students with eCT and identify measures that lead to student attrition. The central hypotheses tested are which measures of CT are valid for sophomore engineering students in the CoE&T at the University of Nebraska, and what is the magnitude of those measures. The process involved questionnaire development and regression model development which is explained using descriptive statistics. Lessons learned and future opportunities for usage of the proposed methodology are discussed. This information can be used for future research that may help reduce STEM student attrition. RESEARCH METHODOLOGY There are 6 phases of the SECtCS methodology. The 6 Phases of the SECtCS methodology are displayed in Figure 1. Figure 1: 6 Phases of the SECtCS Methodology . 1. PHASE 1 – DEVELOP TEST INSTRUMENT – Develop a customized test instrument (questionnaire) for the knowledge worker population, administer the questionnaire, and collect and record scores. Conduct reliability testing on the questionnaire. This testing continued until the questionnaire was reliable. (SECtCS Analyzer) 2. PHASE 2 – DEVELOP MATHEMATICAL MODEL – Use the data collected in phase 1 and incorporate it into a mathematical model to give a valid CT index score. (SECtCS Modeler) 3. PHASE 3 – (Not in study) STATISTICAL PROCESS CONTROL CHARTS – Use data from the model developed in phase 2 for the statistical measurement of individuals with respect to all respondents and identify at-risk CT index scores. (SECtCS Evaluator-i) Establish a tracking mechanism for “at-risk,” and “low-risk” respondents. The respondents are required to retake the questionnaire every 3 months in order to monitor changes. They will also report if they become actual turnover. 4. PHASE 4 (Not in study) – INTERVENTION – Educate, implement, monitor and develop solution. (SECtCS intervention) 5. PHASE 5 (Not in study) – INTERVENTION MEASURMENT – Re-measure the respondents after they have been subjected to the intervention and compare to the results of phase 3. (SECtCS Evaluator-r) 6. PHASE 6 (Not in study) – RESULTS OF INTERVENTION – Document the results and conclusions and add to solutions database -Intervention Note: The intervention, like organizational mentorship, has to be coordinated for effectiveness. The intervention contributors must be provided guidelines so there will be data consistency. These guidelines will also allow for efficient collection of feedback. P ge 1.66.3 PHASE 1 TEST INSTRUMENT DEVELOPMENT (SECtCS questionnaire) The summated rated scale methodology was used to create the SECtCS questionnaire. Summated rated scales have good psychometric properties and are well-developed scales that have good reliability and validity. A well-devised scale is usually quick and easy for respondents to complete and typically does not induce complaints. The questionnaire was developed grouping questions into construct, or measurable variables that relate to CT. Constructs Constructs were developed using burnout and turnover questions. Burnout is commonly assessed using the Maslach Burnout Inventory (MBI) (Maslach and Jackson 1981). The MBI is a widely accepted questionnaire that has been used for numerous burnout studies that and has been proven both reliable and valid. It generally measures 3 areas; depersonalization, personal achievement, and emotional exhaustion, which relate burnout to the respondents’ physical well-being. See Exhibit 1 for a description of burnout constructs. Turnover refers to individuals who voluntarily exit an organization within a particular period of time. Because of time and sampling constraints, it has been difficult for organizations to measure turnover (also called attrition). Previous research has shown that certain job-related factors, or constructs, have been demonstrated to be correlated with employee attrition. Such measures are useful in the context of studying retention-related interventions because they may provide specific measurement on related items so results can be determined over relatively short periods of time. The 8 main constructs related to turnover are general satisfaction with engineering major, goals, comfort, challenge, future financial rewards, relationships, resource adequacy, and perceived ability to get a job. See Exhibit 1 for a description of turnover constructs. Exhibit 1. General Definitions of Constructs Cognitive Turnover Determinant Construct Construct Definitions Burnout Depersonalization Distancing oneself from others Burnout Personal Accomplishment Performing well on things that matter Burnout Emotional Exhaustion Ability to cope in high stress situations Turnover Overall Major Satisfaction Satisfaction with engineering major that determines turnover Turnover Goals Feeling that goals are attainable and have meaning Turnover Comfort The space and physical conditions of the job are adequate to perform at the job Turnover Challenge Feeling that engineering is not boring and has reasonable challenges Turnover Future Financial Rewards Financial Compensation will be reasonable and fair from effort made in engineering studies Turnover Relationships Ability and willingness to work with others students and faculty Turnover Resource Adequacy Organization provides adequate supplies and resources to graduate and get a job Turnover Perceived ability to get a Opportunity for fair chance at competitive P ge 1.66.4 job engineering jobs in the marketplace The foundations for the SECtCS questionnaire were questionnaires that have been proven valid and reliable from previous studies. The Minnesota Satisfaction Questionnaire, MSQ (Lofquist and Dawesl, 1967), is one of the most widely used measures of organizational satisfaction. The Facet-Specific Job Satisfaction Questionnaire, FSJSQ, (Cook, Hepworth, Wall, and Warr, 1989), is commonly used in measuring specific organizational satisfaction items. The items, each measuring a “facet” as indicated in the scale’s title, were previously used in a 1973 survey, and a similar measure was employed in 1969 (Cook et al., 1989). Reliability Coefficient alpha (Cro