Comparing Five Empirical Biodata Scoring Methods for


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COMPARING FIVE EMPIRICAL BIODATA SCORING METHODS FOR PERSONNEL SELECTION Mark J. Ramsay, B.A.
Thesis Prepared for the Degree of MASTER OF SCIENCE
UNIVERSITY OF NORTH TEXAS August 2002
APPROVED: Douglas Johnson, Committee Member and Major Professor Terry Halfhill, Committee Member and Major Professor Joe Huff, Committee Member and Major Professor Ernest Harrell, Chair of the Psychology Department C. Neal Tate, Dean of the Robert B. Toulouse School of
Graduate Studies

Ramsay, Mark J., Comparing Five Empirical Biodata Scoring Methods for Personnel Selection. Master of Science (Industrial Psychology), August 2002, 103 pp., 8 tables, 3 appendices, references, 91 titles.
A biodata based personnel selection measure was created to improve the retention rate of Catalog Telemarketing Representatives at a major U.S. retail company. Five separate empirical biodata scoring methods were compared to examine their usefulness in predicting retention and reducing adverse impact. The Mean Standardized Criterion Method, the Option Criterion Correlation Method, Horizontal Percentage Method, Vertical Percentage Method, and Weighted Application Blank Method using England’s (1971) Assigned Weights were employed. The study showed that when using generalizable biodata items, all methods, except the Weighted Application Blank Method, were similar in their ability to discriminate between low and high retention employees and produced similar low adverse impact effects. The Weighted Application Blank Method did not discriminate between the low and high retention employees.

TABLE OF CONTENTS Page
LIST OF TABLES… .… … … … … … … … … … … … … … … … … … … … … … … … .… ...iii Chapter
1. INTRODUCTION… … … … … … … … … … … … … … … … … … … … … ..… 1 What is Biodata? Assumptions of Biodata Types of Scaling Procedures Lack of Use of Biodata Benefits of Biodata Concerns about Biodata Summary
2. BIODATA STUDY… … ...… .… … … … … … … … … … … … … … … … … ..37 Rationale for Using Biodata and Hypotheses for the Study Method Results Discussion
APPENDIX A… … … … … … … … … … … … … … … … … … … … … … … … … … … … … 61 APPENDIX B… … … … … … … … … … … … … … … … … … … … … … … … … … … … … 83 APPENDIX C… … … … … … … … … … … … … … … … … … … … … … … … … … … … … 85 REFERENCES… … … … … … … … … … … … … … … … … … … … … … … … … … … ..… 92
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LIST OF TABLES

Table

Page

1. Mount et al’s (2000) Four Different Biodata Scales and How They Correlate with Big Five Factors … … … … … … … … … … … … … … … … … … … … … .… … … .… … .13-14

2. List of Criterion Measures Biodata Predicts and Their Validities… … … … … … ..15-16

3. Gender and Race Selection Percentage Rates of the Holdout Sample for each of the Four Scoring Method… … … … … … … … … … … … … … … … … … … … … … … ..49-50

4. Comparison of Mean Retention Rates of the Holdout Criterion Groups for each of the Four Scoring Methods… … … … … … … … … … … … … … … … … … … … … … … 50-51

5. Correlation Matrix Between the Four Scoring Methods and Retention Using the

Holdout Sample ..................................................................................................... ..51

6. Gender and Race Selection Percentage Rates of the Holdout Sample for each of the Four Scoring Methods After Eliminating Adverse Impact Items ............................ ..53

7. Comparison of Mean Retention Rates of the Holdout Criterion Groups for each of the Four Scoring Methods After Eliminating Adverse Impact Items ............................ ..54

8. Correlation Matrix Between the Four Scoring Methods and Retention Using the Holdout Sample After Eliminating Adverse Impact Items...................................... ..55

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CHAPTER 1 INTRODUCTION What is Biodata? Selecting the right people for the right job is becoming increasingly more important for organizations. Due to increased global competition and an increase in technology, customers can get goods or services from numerous companies throughout the world. As a result, the one way organizations can gain a competitive advantage over its rivals is through their employees, their intellectual capital. In the age of information, the employees are the ones who hold the company together, retain customers, and help the company grow with their creativity. Therefore, personnel selection is more critical than ever in today’s business world. Selecting the wrong person for the job can be costly. Using a complicated and expensive selection process while a cheaper and equally effective one is available can also be very detrimental to the success of an organization. One selection method that is inexpensive, compared to other methods, and has good predictive validity for job success is biodata (Hunter, 1986; Hunter & Hunter, 1984; Reilly & Chao, 1982; Schmitt, Gooding, Noe, & Kirsch, 1984). Biodata are questions, usually in multiple-choice format, that measure one or more criteria that human resource professionals use to predict future applicant performance. Biodata comes from two basic information sources. One source is people’s
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past interests and experiences, and the other is people’s opinions or attitudes as a consequence of those experiences (Dickinson & Ineson, 1993). Hence, biodata ask applicants questions about their personal background, past life, work experiences and about their opinions, values, beliefs and attitudes of the aforementioned areas.
Mael (1991) identified 10 dimensions of biodata. The 10 dimensions are: ? Historical vs. Hypothetical – whether an item asks about past or future
situations Examples: Historical: “Have you worked in a team environment?”
Hypothetical: “How well would you work in a team environment?” ? Objective vs. Subjective – whether an item asks to recall facts or asks for
opinions Examples: Objective: “How many accounts did you handle last year?”
Subjective: “Would you describe yourself as a good accountant?” ? First vs. Second Hand – whether an item asks about observations of yourself
or other people’s perceptions of you Examples: First Hand: “Do you communicate well with your employers?”
Second Hand: “How would others describe your communication skills?” ? Verifiable vs. Non Verifiable – whether human resource professionals can or
cannot check the item’s answer for accuracy Examples: Verifiable: ”What was your last employee rating?”
Non-Verifiable: “Are you a hard worker?”
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? External vs. Internal: whether an item asks about observable events Examples: External: “When you were in school, how much time did you spend studying?”
Internal: “What best describes your feeling, when you last worked in a team environment?” ? Job Relevant vs. Non Job Relevant – whether an item asks about job related
aspects or not Examples: Job Relevant: “In your last job, how often did you work with computers?”
Non-Job Relevant: “How many times do you go to the movies in a week?” ? Discrete vs. General – whether an item asks about a single particular event or
not Example: Discrete: “How old were you when you first had a job?”
General: “While growing up what activities did you enjoy most?” ? Controllability vs. Non Controllability – extent to which items ask about
experiences subjects had direct control over. Non-control questions usually ask about applicants’demographics or parent’s behavior. Example: Control: “When you were in school, how much time did you spend studying?”
No Control: “What was the population of the city you grew up in?”
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? Equal Accessibility vs. Unequal Accessibility – extent to which the question is relevant to or applies to all subjects. Some questions might not apply to all the subjects because the subjects did not have an opportunity to do or have access to use what the question is asking.
Example: Equal Accessibility – “Do you communicate well with others?” Unequal Accessibility – “How much time did you spend on the
Internet per week while in high school?” ? Invasiveness vs. Non-Invasiveness – extent to which the question is found
offensive by the subject because it asks about private or confidential information. Subjects usually find questions about marital status and political or religious affiliation to be offensive. Example: Invasive – “What is your marital status?”
Non-Invasive – “How often did you work in a team environment at your last job?”
Assumptions of Biodata The rationale underlying the use of biodata for personnel selection comes from several assumptions. The main assumption is that the best predictor of one’s future performance is one’s past performance (Mumford & Owens, 1987). Social Identity Theory (SIT) links to this assumption and provides a rationale for it. The SIT states that a person’s past experiences and values will help typify how the person will act in new social situations and group settings like organizations (Mael & Ashforth, 1995). To create order in their society, individuals identify their places and other people’s places in
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society. They do this by using race, skills, interests, backgrounds etc. to categorize themselves and others into groups or affiliations. Once in these groups, individuals take on the characteristics of these groups. Many groups and affiliations actually instill new values and attitudes into their members in which the members then internalize and use long after leaving their group to make decisions on how to act and what to do. Social Identity Theory argues that biodata reflects a person’s past experiences, which in turn, affect what values, a person internalizes and hence how a person will act in the future. For example, background questions about what past clubs, interests, or societies a person was a member of may be important in determining how successful the person is on the job (Mael & Ashforth, 1995). An example of a successful predictor of success in flight school for the Air Force during World War II was “Did you ever build a model airplane that flew” (Cureton, 1965). This question was successful because people who built model airplanes that flew probably enjoyed working with and learning about airplanes. Hence they had a better understanding of how planes work and therefore did better in flight school.
O’Reilly and Chatman (1986) showed that internalization, compliance, and identification with organizational values relates to prosocial behaviors like intent to stay, and low turnover. The Organizational Commitment Survey, measures commitment by looking at the individual’s congruence with organizational goals and values, and willingness to remain a member (Ashforth & Mael, 1989). So if a person’s values do not fit with the organization’s values, organizational commitment can be low and the person might not perform as well. Biodata questions are useful in these areas because they can
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identify what backgrounds or past experiences create values in people that will predict retention and future success on the job.
A second assumption of biodata is individuals will be more willing to discuss objective facts about past experiences than discuss subjective reasons for why they act in a particular way. People are less willing to discuss their motivations behind their actions because it is more personal to them. Therefore, since biodata tends to ask objective questions about past experiences and not ask about people’s motivations, the answers one receives, under this assumption, should be more valid, and the falsifying of answers should be less of a worry (Korman, 1971). The third assumption of biodata is that systematically measuring a person’s past behavior through empirical keying, rational, or factorial scaling methods can indirectly measure their motivational characteristics (Korman, 1971).
Types of Scaling Procedures Human resource professionals generally use one of the following three types of scaling procedures to help develop and score biodata items. They are the empirical keying method, the rational scaling method, and the factorial scaling method (Mumford, 1999). The most commonly used method is the empirical keying method. This scaling procedure selects and weights biodata items on their ability to discriminate between applicants who measure high on a certain criterion to applicants who measure low on the same criterion. Based on their relationship with the criterion measure, the test developer scores and gives weights to the individual item responses (Mumford, 1999). So if the criterion measure is unreliable or biased then so will be the scoring key. Generally with
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Comparing Five Empirical Biodata Scoring Methods for