Given the substantial evidence documenting its value as a predictor, when many HR managers in the US, europe, and australia were surveyed concerning their organizations use of biodata,
Distributional properties of the scores on the Web-based measure were more desirable than the scores on the paper-and-pencil measure
Until a general agreement can be reached concerning how broadly or narrowly biodata should be defined, advances in research will be limited.Unlikely that rigorously designed empirical biodata studies will result in a consensus, rather, it is more likely that cogent arguments offered by experts on the topic will be needed to persuade the research community.Validity coefficients from studies that used current employees may overestimate the validity coefficients for job applicants.Stokes et al. - different items may be predictive for current employees and job applicantsQuiones, Ford, and Teachout 1995- the number of times a person has completed a task (r = .36) may be more important than how long a person has been on the job ( r = . 22)Schmidt et al. 2005- Although requiring elaboration showed to lower biodata scores, data are lacking with regard to whether such elaboration increases validity.Use of computer technology allows for customizing the items administered to the job applicant based on certain characteristics (age –have you ever held a full time position-answer may differ depending on the age of the applicant-, different background may mean diferent scoring key needed, computers allow adaptation)Mael 1991 called for a 'clearinghouse for documentation of objective biodata items, complete with previous results and optimal scoring keys'.-drawback is possible compromising of the scale)Correlation between a biodata item and the criterion not often reportedHow can we make a good scale if the previous research does not allow us to use the best possible predictors?Varimax rotation is questionable- It is likely that the constructs underlying the biodata items are correlated. This may explain why many factor analytically-derived solutions are hard to interpret and/or account for little variance in the biodata items used.The biodata scales used involve a large number of items, frequently researchers lack the sample size needed to justify the use of pca OR pfa.Assuming some thought has been given to the selection of biodata items (what underlying variables they tap) confirmatory factor analysis likely represents a more appropriate analytic technique.The use of a factorial strategy can result in valid biodata items being dropped from the final biodata scale.
The use of biodata for employee selection: Past research and future implications
The Use of Biodata for Employee Selection Past research and future directionsObjectives:1. Provide a selective but representative review of the research that has been conducted on the use of biodata for employee selection2. To constructively critique this research to highlight deficiencies that may limit the conclusions that should be drawn3. To stimulate important future research on biodata that avoids the limitations of past research
Biodata Article Overview1. Biodata Research: A 2. Past Biodata Research: 4. Biodata: Future researchselective review of the Three potential concerns directionsresearch 2.1 The Heavy Reliance of Past 4.1 What is biodata? 1.1. A Study by Goldsmith (1922) Studies on a Concurrent 4.2 Do results for concurrent 1.2. Defining and operationalizing Validity Design validity studies generalize to a biodata: Differences in definitions 2.2 The Type of Biodata Scale selection context? and the types of items used Used 4.3 Increased research with an 1.3. Methods of gathering biodata 2.3 The Lack of Information item-focus 1.4. Strategies used for developing Provided on Biodata Items biodata scales 4.4 Greater focus on the use of 1.5. The Reliability of Biodata technology Scales 3. What is biodata? And why 4.5 Ways to increase the 1.6 The Validity of Biodata Scales accuracy of biodata information does it predict employee 1.7 Adverse Impact behavior? 4.6 The value of a biodata 1.8 Applicant Reactions to Biodata clearinghouse 3.1 What is biodata? Rethinking the use of a 1.9 Incremental Validity 1.10. The Accuracy of Biodata 3.2 Why Does Biodata factorial biodata development 1.11. Computing a Biodata Scale Predict Employee Behavior? strategy Score: Unit Weighting versus 5. Concluding Remarks differential weighting 1.12. The Generalizability of Biodata Scales
1.2. Defining and Operationalizing BiodataDifferences in definitions and the types of items used Factual information about life and work experiences, as well as items involving opinions, values, beliefs, and attitudes that reflect a historical perspective. Narrowly defined Behaviors and events that occurred earlier in life How many jobs have you had in the past 5 years? How long have you been in your previous job? Broadly Defined Temperament, assessment of working conditions, values, preferences, skills, aptitudes, and abilities. I like doing things with other people. My teachers regarded me as a sociable boy/girl.
1.2. Defining and Operationalizing Biodata Differences in definitions and the types of items usedMael’s (1991) definition: Does include items pertaining to historical events that may have shaped the persons behavior and identity Does not include items that address such variables as behavioral intentions, self-descriptions of personality traits, personal interests, and abilityAdvantages of Mael’s historical nature definition of biodata: Accuracy in reporting of discrete verifiable events More favorable view of questions that applicant views as job related and reflect experience under applicants control
Biodata Introduction“One of the best selection devices for predictingturnover” Organizations rarely use biodata (<17%) Less than one page devoted to biodata in Evers, Anderson, and Voskuijls Handbook of Personnel Selection (2005) A PsychINFO database search in 2008 for the term biodata turned up one article*Survey of 255 HR professionals ranked biodata as lacking in terms ofvalidity, practicality, and legality
1.1. A Study by Goldsmith (1922) Examined the ability of 9 “personal history” items to predict the first-year sales of insurance agents Marital status Education Belonging to clubs Found that using a person‟s biodata score would improve hiring decisions made 58 of the 259 individuals receiving a score of 4 or above were considered successful (22%) 11 of the 243 individuals receiving a score less than 4 were considered successful (4%) Then and Now: Used few biodata items A number of items used would not be used today (age) Did not report data on the relationship of a given item and sales Provided an explanation for using each item
1.3. Methods of Gathering Biodata Web-based Telephone Paper-and-pencil Study by Ployhart, Weekley, Holtz, and Kemp (2003) compared scores from paper-and-pencil measure to those obtained from a web-based version of the measure o Web based group had lower mean score (may suggest less faking) o Lower scores in terms of skew and kurtosisMumford (1999) suggested there may be benefits from using agreater variety of data gathering methods.
1.4. Strategies Used for Developing Biodata Scales Researchers use a combination of strategies: 1. Empirical “Dust-bowl empiricism” 2. Behavioral Consistency “Best predictor of future behavior is past behavior” 3. Rational/Deductive Job Analysis/Theories 4. Factorial Attempting to explain „why‟ there is a correlation 5. Subgrouping Different groups use different constructs when answering
Scale Development 1. The Empirical Approach “Dust Bowl Empiricism” No theory is involved behind the study. Solely refers to instances arising from entirely inductive processes. We just want to know which items are significantly correlated to form the scale. Large pool of items are used, those that are predictive are chosen for use in the scale Example: Finding a high correlation between two Ideally a cross-validation variables, job turnover and amount of jobs held in study would be past five years, and including „amount of jobs held conducted in past five years‟ as part of your biodata scale.
Scale Development 2. Behavioral Consistency Approach Past behavior predicts future behavior Selects items that are consistent with the criterion of interest Causal variables are usually not investigated Example: When interested in predicting turnover ask, “how long have you been at your most recent job?” Stable work ethic?
Scale Development 3. Rational/Deductive Approach Conduct a job analysis to determine KSAs relevant for the criterion of interest or Use recent research/theories in development of questions Criterion of interest = voluntary turnover Knowledge of the job (realistic expectations) reduces voluntary turnover Example: “Do you know someone who works for the organization?”
Scale Development 4. Factorial Approach Principal Axis Factor Analysis/ Principal Components Analysis Explain why biodata scales predict the criterion of interest Extracts underlying factors that cause the statistical relationship to exist Empirical example: Job turnover and amount of jobs held in past five years? Age Behavioral Consistency example: turnover and amount of time at most recent job? Work ethic Rational/Deductive example: Knowing someone working for the organization and voluntary turnover? Realistic expectations
Scale Development 5. Subgrouping Different groups may have different patterns of constructs that underlie their responses to biodata items Types of biodata items that best predicted military suitability for high school graduates differed from those that predicted suitability for non-graduates
1.5. The Reliability of Biodata Scales One construct e.g. past experience interacting with people Coefficient alpha appropriate Estimates range from .50-.80 A variety of constructs e.g. marital status, age, schooling completed, and number of jobs held in the past 5 years Coefficient alpha not appropriate Test-Retest reliability may be appropriate Estimates range from .60-.90
1.6 The Validity of Biodata Scales Criterion-related Validity Research shows that biodata is a good predictor of: Job performance Voluntary turnover 1.9 Incremental Validity Mount, M. K., Witt, L. A., & Barrick, M. R. (2000) Biodata added unique variance in predicting supervisory ratings of performance beyond that accounted for by tenure, general mental ability, and the Big Five personality traits Allworth and Hesketh (2000) Biodata scale accounted for unique variance in performance ratings when added after a cognitive ability test
1.7 Adverse Impact Causes for concern occur when biodata items are used regarding: Educational level Cognitive ability (GPA) Use careful item screening Compared to other selection devices, biodata has modest adverse impact
1.8 Applicant Reactions to Biodata Poor face validity Applicants are likely to react negatively to items that are perceived as lacking job relatedness, fakable, and overly personal in nature Studies usually do not involve applicants Students or current employees
Biodata Sample FBI InventoryThis inventory contains 40 questions about yourself.You are to read each question and select the answer that best describes you from the choices provided. Answer the questions honestly; doing otherwise will negatively affect your score. 1. How did you typically prepare for final 3. To what extent have you enjoyed being exams in college? given a surprise party? A. Studied a few hours every day across several weeks A. Not at all B. Studied many hours over a few days B. To a slight extent C. Studied the entire night before each exam C. To a moderate extent D. Did not study D. To a great extent E. I have never been given a surprise party 2. How often are your library books 4. In the past year, how many times have overdue? you thrown something when you were A. Always angry? B. Often A. 0 times C. Rarely B. 1 - 2 times D. Never C. 3 - 4 times E. I never take books out of the library D. 5 - 6 times E. 7 or more
1.10. The Accuracy of Biodata Students Fairly Accurate External verification from parents supports the self-reported student data Applicants Accuracy was mixed when studies were conducted in a selection context Faking good answers
1.11. Computing a Biodata Scale Score Unit Weighting versus differential weighting1. Correlational (Unit) Method Compute a simple correlation between an item and the criterion, then use this value to weight the item More highly correlated items receive higher weights2. Differential Regression Method Select all biodata items that are significantly correlated to the criterion and unit weight them. Differential regression method is most beneficial when the correlations among the items are low, there are relatively few items, and there is a large sample Both methods tend to provide comparable results.
1.12. The Generalizability of Biodata ScalesWill a biodata scale developed in one organization bevalid if applied in another organization? In the U.S., research shows that biodata scales have predicted: Brown (1981) Sales volume for insurance agents across 12 companies Rothstein, Schmidt, Erwin, Owens, and Sparks (1990) Performance of supervisors across organizations Carlson, Scullen, Schmidt, Rothstein, and Erwin (1999) Rate of promotions across 24 organizations
1.12. The Generalizability of Biodata Scales (International) Laurent (1970) Valid scale for managers in the US was also valid in predicting management success in Denmark, Norway, and the Netherlands Dalessio, Crosby, and McManus (1996) Scale used to select insurance agents in the US used with equal effectiveness in the United Kingdom and Ireland
1.12. The Generalizability of Biodata Scales Overtime Brown (1978) Scale developed in 1933 for selecting insurance agents predicted survival and performance of agents in 1969-1971 Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W. A., & Sparks, C. P. (1990) Validity coefficients of studies done in 1974 and 1985 were similar Carlson, K. D., Scullen, S. E., Schmidt, F. L., Rothsteing, H., & Erwin, F. (1999) Scoring key for the Manager Profile Record yielded valid scores up to 11 years after the key was developed*Stability likely due to researchers using items that weregeneric/attributes of the jobs tapped by the biodata items have notchanged greatly
2.Past Biodata Research Three potential concerns 2.1 Heavy reliance on concurrent validity designs 2.2 Type of biodata scale used 2.3 Lack of information provided on biodata items
2.1. The Heavy Reliance of Past Studies on a Concurrent Validity DesignAre results taken from current employees comparable to job applicants? Stokes, Hogan, and Snell (1993) Harold, McFarland, & Weekley (2006) Studied sample of incumbents working in a 425 call center employees and 410 sales position and applicants who had applied applicants respond to 20 biodata item for the position Validity coefficients higher for job incumbents o Developed two scales to predict (.27) than job applicants (.18) turnover (i.e. job applicant scale and job incumbent scale) o Validities of scale were similar • Job Incumbent .22 • Job Applicant .23 Switched the scales, i.e. gave job applicant the job incumbent scale ● Validity Coefficient = .08 ● Biodata scales developed for each group had no items in common
2.2 The Type of Biodata Scale Used Generic vs. Situation-specific Scales Developing situation-specific biodata scales may result in higher validity than a more generic scale o Situation-specific validity coefficient = .33 o General validity coefficient = .22 Expensive to develop • Writing items • Pilot testing *Generic scale is better than no scale
2.3 The Lack of Information Provided on Biodata Items Researchers often do NOT report the actual items they used due to: Lengthy biodata measures, < 100 items, journal space issue Used biodata items sold by vendors who do not allow publication of their items Therefore, most studies have not reported: 1. Correlation between each biodata item and the criterion used in the study 2. How each item was weighted in creating the scale 3. Whether an item provided unique variance in predicting a criterion variable 4. Whether an item had adverse impact 5. Correlations among biodata items
2.3 The Lack of Information Provided on Biodata Items Imagine you are developing a new biodata scale. How would this omitted information be beneficial? Valid predictors in past studies Adverse impact Non-significant findings Allow selection of biodata items that are of maximum value while limiting the number of items that are used
3. What is Biodata? And Why Does It Predict Employee Behavior? 3.1 What is Biodata? 3.2 Why does it predict employee behavior?
3.1 What is Biodata? Article‟s Position: Biodata consists of applicant‟s past behavior and experiences Past behaviors and experiences can reflect events that occurred in a work context (quit a job without giving notice), an educational setting (graduated from college), a family environment (traveled considerably growing up), community activities (led a cub scout troop), or other domains (active in local politics) Does not mean that past experiences are unrelated to such variables as interests, personality, values, knowledge, and skills Schmidt et al. (1999) It is likely that an individual who possesses certain interests, personality traits, values, and/or KSAs will be more likely to seek out certain situations that are captured by historical biodata In summary: Many of the variables (personality traits) that have commonly been confounded with biodata are actually antecedents of consequences of the personal experiences that biodata taps
3.2 Why Does Biodata Predict Employee Behavior? Most studies focus on criterion-related validity and few models offer an explanation to „why‟ Mumford, Owens, and Stokes (1987, 1990) developed the (Interactive) Ecological Model to help determine the “why”
3.2 Why Does Biodata Predict Employee Behavior?Persons life begins with certain environmental andhereditary resources... • A nurturing mother • excellent eyesight...and certain limitations... • Substandard nutrition • Poor coordination...which determine individual differences early in life. • High cognitive ability • Poor health • Self-confidence Given these individual differences, an individual attempts to maximize adaptation to the environment.
3.2 Why Does Biodata Predict Employee Behavior?• The ecological model presumes that an individual makes decisions about what situations to enter.. • What college to attend • Whether to accept a job offer…based upon the perceived value of the outcomes • Social status • Financial rewards • Intrinsic satisfaction…that are likely to be derived from the situations.• Interactive: An individuals choice at a given point in time about what situation to enter affects his subsequent development, which influences his future choices of situations, which affect future development, etc. Thus, over time, an individual may develop new skills, satisfy existing needs, increase academic goals, or decrease his work ethic.
Environmental Experience at Time 2 Non-choice/Uncontrollable events: e.g. unemployment, health problems Environmental Experience at Time 2 Time 1 Time 2 Time 3 An individual possesses several The experience of the new The individual is now attributes, based on these attributes environment will lead to changes different on one orthe model suggests the individual will in the person’s attributes. more attributes than at. actively choose to enter a new Time 1. situation/environment that is perceived to aid in development.
3.2 Why Does Biodata Predict Employee Behavior? By limiting the definition of biodata to past behaviors and experiences, the data collection is on events reflected in the boxes labeled Environmental Experience at Time 2. Did you graduate from college? How long were you at your most recent job? What percentage of you college expenses have you paid? This model explains how defining biodata by past behavior and experiences does NOT mean a biodata score is unrelated to such variables as conscientiousness, ability, interests, knowledge, etc. Rather, it shows how such variables are likely antecedents and/or consequences of an individuals behaviors and experiences. Better way to get information that could be faked Asking how long someone was in a prior position in sales helps gauge the persons dependability, knowledge of a sales position (realistic job expectations), communication skills, etc. without asking Are you a dependable person
4. Biodata: Future Research Directions4.1 What is biodata? Doubtful that rigorously designed empirical biodata studies will result in a consensus, more likely that cogent arguments by experts will need to persuade the research community4.2 Do results for concurrent validity studies generalize toa selection context? Increase predictive validity research designs Concurrent validity coefficients may overestimate coefficients for applicants4.3 Increased research with an item-focus Increase attention to item-level issues Work vs. education, amount vs. time4.4 Greater focus on the use of technology Individual differences different questions different scoring keys?
4. Biodata: Future Research Directions4.5 Ways to increase the accuracy of biodata information Elaboration lowers scores (decrease faking), but does this increase validity?4.6 The value of a biodata clearinghouse Easier scale development but risk of compromising „answers‟4.7 Rethinking the use of a factorial biodata developmentstrategy Varimax rotation-constructs found may be correlated (explain little variance) PCA-large number of biodata items used warrants large sample size May result if valid biodata items being dropped from final scale Confirmatory Factor Analysis may be more appropriate
5. Concluding RemarksConcerns are unfounded or only true for certain biodata scales Validity Research has shown biodata to be an excellent predictor Legality Adverse impact and a lack of face validity may be minimized by careful selection of items Practicality Biodata scales do not need to involve a large number of items Barrick and Zimmerman (2005) and O‟Connell et al. (2002) used < 10 itemsTo further use of biodata and research 3 issues need to be addressed 1. Agreement on what biodata is 2. Greater reliance on predictive validity designs 3. Greater attention given to the specific biodata items used in studies