Preventing Rater Biases in 360-Degree Feedback by Forcing Choice

Preventing Rater Biases in 360-Degree Feedback by Forcing Choice

Anna Brown1, Ilke Inceoglu2, and Yin Lin1,3

Abstract We examined the effects of response biases on 360-degree feedback using a large sample (N¼ 4,675) of organizational appraisal data. Sixteen competencies were assessed by peers, bosses, and sub- ordinates of 922 managers as well as self-assessed using the Inventory of Management Competencies (IMC) administered in two formats—Likert scale and multidimensional forced choice. Likert ratings were subject to strong response biases, making even theoretically unrelated competencies correlate highly. Modeling a latent common method factor, which represented nonuniform distortions similar to those of ‘‘ideal-employee’’ factor in both self- and other assessments, improved validity of competency scores as evidenced by meaningful second-order factor structures, better interrater agreement, and better convergent correlations with an external personality measure. Forced-choice rankings modeled with Thurstonian item response theory (IRT) yielded as good construct and convergent validities as the bias-controlled Likert ratings and slightly better rater agreement. We suggest that the mechanism for these enhancements is finer differentiation between behaviors in comparative judgements and advocate the operational use of the multidimensional forced-choice response format as an effective bias prevention method.

Keywords multisource feedback, halo effect, rater biases, forced choice, Thurstonian IRT model, ideal- employee factor

Three hundred and sixty–degree appraisals are widely used in organizations, and the basic idea is to

capture distinct perspectives on a set of employee behaviors thought to be important in their job

roles. For feedback emerging from such process to be useful, it must converge across raters of the

same target (although some differences are expected and even welcome), and it must be differen-

tiated by behavior. Unfortunately, the opposite is often the case—assessments of conceptually

1School of Psychology, University of Kent, Canterbury, Kent, UK 2Surrey Business School, University of Surrey, Guildford, Surrey, UK 3CEB SHL Talent Measurement Solutions, Thames Ditton, Surrey, UK

Corresponding Author:

Anna Brown, School of Psychology, University of Kent, Canterbury, Kent CT2 7NP, UK.


Organizational Research Methods 2017, Vol. 20(1) 121-148 ª The Author(s) 2016 Reprints and permission: DOI: 10.1177/1094428116668036

distinct behaviors by the same rater are too similar (Murphy, Jako, & Anhalt, 1993) while assess-

ments of the same behavior by different raters are too diverse (Adler et al., 2016; Borman, 1997;

Conway & Huffcutt, 1997). Furthermore, rater perspective (e.g., subordinates vs. peers of target X)

explains little of the typically observed overall variability in 360-degree ratings (e.g., Scullen,

Mount, & Goff, 2000). Thus, there seems to be little value in the usual practice of separating

information from specific rater perspectives (LeBreton, Burgess, Kaiser, Atchley, & James,

2003). Although some plausible explanations of the low rater agreement have been suggested over

time, such as different observability of behaviors (Warr & Bourne, 2000), rater personality (Randall

& Sharples, 2012), differences in organizational level (Bozeman, 1997), and cultural values (Eckert,

Ekelund, Gentry, & Dawson, 2010), many researchers have serious doubts about the validity of

360-degree ratings. For example, summarizing articles of an Organizational Research Methods

special issue dedicated to modern data analytic techniques of 360-degree feedback data, Yammarino

(2003) concluded that ‘‘construct validity of multisource ratings and feedback is faulty or at least

highly suspect’’ (p. 9).

In the present article, we argue that the major issue often overlooked in studies using multi-source

assessments is the problem presented by response biases. Many studies apply measurement assum-

ing that only substantive constructs and random error sources influence responses and ignore

systematic sources of error (biases). Studies that do consider biases tend to either examine effects

of one specific type of bias (e.g., rater leniency; Barr & Raju, 2003), however small it may be, or

conversely, assess the overall ‘‘rater idiosyncratic’’ variance overlooking the nature and type of

biases contributing to it (e.g., Scullen et al., 2000). Both directions of research are valuable; how-

ever, it is important to know what types of bias are likely to have the greatest impact on ratings in a

specific context. To address this question, we use a large data set of operational 360-degree apprai-

sals to search for most potent biasing effects in multiple-rater assessments empirically and assess

two ways of overcoming them—by modeling biases after they occurred and preventing them from

occurring in the first place.

The article is organized as follows. First, we define the type of evaluations that can be reasonably

expected in 360-degree appraisals. Second, we discuss potential threats to this objective, namely,

response biases, and outline two conceptually distinct ways of overcoming biases—modeling them

statistically after the event and preventing them with forced-choice response formats. We then

identify the nature of biasing effects found in our empirical data and evaluate the two alternative

approaches to bias control by comparing the scores obtained with these methods in terms of their

construct validity. We conclude with recommendations for research and practice.

Constructs Measured in 360-Degree Appraisals

Three hundred and sixty–degree assessments typically aim to measure competencies, commonly

understood as ‘‘sets of behaviors that are instrumental in the delivery of desired results or outcomes’’

(Bartram, Robertson, & Callinan, 2002, p. 7). The important question is then whether and to what

extent self- and other ratings measure behaviors of targets. We concur with Van der Heijden and

Nijhof’s (2004) conclusion that raters certainly do not contribute objective information on a target’s

behaviors. Instead, subjective evaluations of behaviors are being captured, which can be reasonably

considered the intended constructs in 360-degree feedback as they have a value in themselves. Thus,

the validity of the method can be defined as the extent to which 360-degree ratings reflect actual

perceptual judgements of the rater (or recall of actual behaviors; Hansbrough, Lord, & Schyns,

2015), as opposed to nuisance factors, which have the potential to contribute to both random and

systematic errors in ratings.

122 Organizational Research Methods 20(1)

Response Biases in 360-Degree Appraisals and Ways to Counteract Them

The literature on 360-degree assessments has predominantly focused on Likert-type question

formats (also referred to as single-stimulus formats). A multitude of biases elicited by the use of

single-stimulus questions is well known and documented. Both self-reported and other-reported

questionnaires can be subject to response styles. Acquiescence, often dubbed ‘‘yeah-saying,’’ is a

response style primarily caused by inattention or lack of motivation (Meade & Craig, 2012);

therefore, it is unlikely to be a major threat in organizational appraisals, which typically constitute

medium-stakes assessments. Extreme/central tendency responding is the individual tendency to use

primarily the extreme/middle response options, which is thought to be related to personality and

culture (Van Herk, Poortinga, & Verhallen, 2004). Specific to ratings by external observers is the

leniency/severity bias, where some raters are lenient and some are severe in their ratings of all targets

(e.g., Murphy & Balzer, 1989; Murphy & Cleveland, 1995). Barr and Raju (2003) investigated the

leniency bias in 360-degree feedback and found that despite statistical significance, its effect size on

observed ratings was small.

Judging by extant research, much more potent in multiple-rater assessments is the tendency to

maintain cognitive consistency in ratings of all behaviors guided by affect felt toward the target—the

so-called halo effect (Kahneman, 2011; Thorndike, 1920). This unmotivated but powerful cognitive

bias results in high dependencies between all assessed qualities—even conceptually unrelated ones.

Moreover, external raters may be influenced by different goals and political pressures (Murphy,

Cleveland, Skattebo, & Kinney, 2004), while the assessment targets may be keen to present a picture

of an ‘‘ideal employee’’ (Schmit & Ryan, 1993)—all examples of motivated processes. Unmotivated

or motivated, response distortions in organizational appraisals may render ratings invalid—because

we cannot assume that perceptual judgments of behavior are measured.

Statistical Correction of Response Biases

One way to overcome response biases has been the application of statistical correction after distor-

tions have taken place. In the past, a commonly used approach was to quantify the biasing effect by

calculating a simple index (e.g., the number of times a person used the extreme response options),

which was then used to partial out the effect from the observed scores (Webster, 1958). Advances in

item response modeling have allowed controlling for some types of response distortions by incor-

porating them in the response model (e.g., Böckenholt, 2012, 2014; Bolt, Lu, & Kim, 2014; Bolt &

Newton, 2011; Maydeu-Olivares & Coffman, 2006). For example, if a bias can be conceptualized as

a random additive effect f, the psychological values (or utilities) that a respondent expresses for A

and B, t 0 A and t

0 B, are a combination of ‘‘true’’ utilities tA and tB that the respondent feels for these

items and the added effect:

t 0 A ¼ tA þ lA f ; t

0 B ¼ tB þ lB f ð1Þ

Such processes can be modeled in a confirmatory factor analysis (CFA) framework, whereby a

latent variable f influences all item responses over and above any common factors that are assumed

to underlie the true item utilities. The extent to which Item A is sensitive to the biasing effect f is

described by the respective factor loading, lA. Uniform response biases assume factor loadings equal for all items; nonuniform biases assume varying loadings. For instance, acquiescence and rater

leniency are uniform additive effects; they can be modeled by adding a common random intercept

with equal loadings for all items (e.g., Barr & Raju, 2003; Maydeu-Olivares & Coffman, 2006). The

‘‘ideal-employee’’ factor as conceptualized by Klehe at al. (2012), on the other hand, is a nonuni-

form additive effect whereby more desirable indicators show higher loadings.

Brown et al. 123

Benefits of explicitly modeling biasing effects are twofold. First, the bias is conceptualized and

measured as a construct and can then be explored in relation to other psychological variables. For

example, Klehe et al. (2012) controlled for score inflation often observed in high-stakes person-

ality assessments by modeling an ideal-employee factor and found that the construct’s relationship

with job performance was fully explained by the applicant’s ability to identify performance

criteria. This example also illustrates the second benefit of modeling bias: ‘‘purification’’ of the

measured constructs, which now can be analyzed without the distortions clouding our understand-

ing of their validity.

Prevention of Response Biases

Another way of overcoming biases has been employing research designs that would prevent them

from occurring in the first place. One popular design is collecting data using forced-choice

formats, whereby a number of items are presented together in blocks and respondents are asked

to rank the items within each block. The format makes it impossible to endorse all items, thereby

counteracting acquiescence and improving differentiation (thus directly combating halo effects).

Moreover, since direct item comparison requires no rating scale, extreme/central tendency

response styles cannot occur.

It has been shown that the forced-choice format eliminates all effects acting uniformly across

items under comparison (Cheung & Chan, 2002). The mechanism for this elimination is very simple.

According to Thurstone’s (1927) law of comparative judgment, choice between Items A and B is

determined by the difference of utilities that a respondent feels for A and for B, tA – tB. If this

difference is positive, tA – tB > 0, then A is preferred to B, and if it is negative, tA – tB < 0, then B is

preferred to A. Because the outcome only depends on the sign of the utility difference, it is invariant

to any transformation of the utilities as long as their difference remains of the same sign. For

example, if item utilities are biased so that the expressed utilities t 0

are linear combinations of the

true utilities t with fixed coefficients c and d,

t 0 A ¼ ctA þ d; t

0 B ¼ ctB þ d; ð2Þ

then the difference of the ‘‘biased’’ utilities has the same sign as the difference of the true utilities

when c > 0,

t 0 A � t

0 B ¼ ðctA þ dÞ � ðctB þ dÞ ¼ cðtA � tBÞ ð3Þ

It can be seen that any additive and multiplicative effects (terms d and c in Equation 2, respec-

tively) are eliminated by the forced-choice format. Importantly, it is not necessary that the effects are

uniform across all items in a questionnaire. It is sufficient that the coefficients c and d are constant

within each block, but they can vary across blocks. This feature has been used by researchers as the

basis for creating forced-choice designs robust to motivated response distortions such as impression

management or faking (e.g., Stark, Chernyshenko, & Drasgow, 2011). Indeed, if faking can be

conceptualized as an additive effect f as in Equation 1, then careful matching of items within blocks

on the extent they are susceptible to faking (i.e., on their factor loadings l) should remove or greatly reduce the effect,

t 0 A � t

0 B ¼ ðtA þ lA f Þ � ðtB þ lB f Þ ¼ tA � tB þ ðlA � lBÞf ð4Þ

On the contrary, combining items with very different susceptibility to faking within one block, for

example, a positive indicator and a negative indicator of two desirable traits would predictably result

in a failure to control faking (e.g., Heggestad, Morrison, Reeve, & McCloy, 2006).

124 Organizational Research Methods 20(1)

Previous research with forced-choice instruments in performance measurement (Bartram, 2007)

demonstrated better discrimination between behaviors and improved predictor-criterion relation-

ships. However, older studies employing the forced-choice format used the classical scoring method,

yielding ipsative (relative to self, interpersonally incomparable) data, with its many spurious effects

(Brown & Maydeu-Olivares, 2013), and hence the true effectiveness of the forced-choice method in

360-degree feedback is unknown.

Separating Substantive and Biasing Effects

In their critical analyses of the literature on halo bias, Murphy et al. (1993) called for a moratorium

on the use of ‘‘halo indices’’ derived from observed scores. One example of such index is the overall

score on all measured dimensions, which Landy, Vance, Barnes-Farrell, and Steele (1980) suggested

to partial out of the dimension-specific scores to overcome halo. The problem with this approach, as

Murphy and colleagues (1993) rightly identified, was that such indices cannot separate the cognitive

bias of inflated coherence in judgements due to affect felt for a target (a.k.a., halo; or ‘‘illusory’’ halo

as often specified in the literature) from the conceptual overlap between assessed traits due to

competence in a wider domain or overall job competence (specified as ‘‘true’’ halo). While the

former might be an interesting psychological phenomenon to study, it is nuisance to organizational

appraisals of competencies. The latter, on the other hand, is probably one of the most important

variables in organizational psychology, and removing it from data would amount to throwing the

baby out with the bathwater.

Fortunately, huge advances that latent variable modeling has made in the past 20 years allowed

researchers to move from indices based on observed scores to modeling biases as latent variables, for

example, assuming a model such as described by Equation 1. The question of such models’ ability to

separate the substantive and biasing effects is then one of model identification. Are the substantive

factors underlying the true utilities t and the biasing factor f separately identified? Generally, one can

control for ‘‘any systematic variance among the items independent of the covariance due to the

constructs of interest’’ (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003, p. 894). This assumption of

independence of the biasing factor from the substantive factors is quite reasonable in many contexts.

However, the constructs of interest must be allowed to correlate with each other, which is necessary

to capture the substantive overlap between them—for example, due to overall job competence.1 A

suitable model is presented in Figure 2 (Panel 2), with a latent common method factor independent

from multiple correlated constructs of interest. This model is generally identified, although empiri-

cal under-identification may occur—for instance, when the number of measured traits is large but

the number of items measuring them is small (Podsakoff et al., 2003). The model allows capturing

common method effects without specifying and measuring them; however, to identify several

biasing effects that act simultaneously further constraints and special research designs are required.

The use of forced choice has a good potential to reduce the inflated coherence (illusory halo) by

directly forcing raters to differentiate. But can we ensure that at the same time the true halo be

retained? Or similarly, while making it impossible to endorse all desirable behaviors in self-

assessment (and thus reducing the ideal-employee factor), can we ensure that any overlap due to

genuine clustering of certain behaviors in the same people be retained? The traditional approach to

scoring forced-choice data resulting in ipsative data made this objective impossible. Ipsative scoring

removed the person overall baseline (all dimension scores add to a constant for everyone), and

consequently, the common basis for scores overlap. One immediate consequence is that the average

correlation between ipsative scores is always negative (Brown & Maydeu-Olivares, 2013). The same

spurious effect is observed when subtracting the person total score from their dimension scores, as

suggested by Landy et al. (1980), which effectively ipsatizes the scores. Understandably controver-

sial in the past, the forced-choice formats are rapidly gaining in popularity since the development of

Brown et al. 125

item response theory (IRT) models that infer proper measurement from them. It has been recently

shown that with the use of Thurstonian IRT models (Brown & Maydeu-Olivares, 2011) such as the

one illustrated in Figure 2 (Panel 3), the still widespread concern among researchers about removal

of any common source of item variance by the forced-choice format can be finally put to rest. Brown

(2016) demonstrated that under general conditions, the covariance structure underlying the mea-

sured traits is preserved in forced-choice data. Thus, forced choice can be used to remove or greatly

reduce any common method effects according to Equation 4 while preserving factorial structures of

substantive traits that are the focus of measurement.

Objectives and Hypotheses

The aim of the present study is to address some methodological limitations of previous research

and examine the effects of bias control and prevention on validity of 360-degree organizational

appraisals. The specific objectives are to evaluate the forced-choice method and explicitly com-

pare this bias prevention method to either ‘‘doing nothing’’ (i.e., scoring single-stimulus responses

without any bias control) or statistically controlling for biases after they have occurred. We apply

model-based measurement to enable proper scaling of competencies based on forced-choice (FC)

assessments and thereby avoid ipsative data (Brown & Maydeu-Olivares, 2013) and separate the

method-related (biasing) factor from the substantive factors (i.e., factors we are interested in

measuring) in single-stimulus (SS) assessments.

The default assumption in traditional scoring protocols of appraisal tools is that any similarities

between observed competency ratings are due only to the substantive overlap between competencies

(overall competence, or true halo). Based on previous research (e.g., Bartram, 2007; Landy & Farr,

1980; Scullen et al., 2000), we contest this assumption and hypothesize that rater idiosyncratic biases

will also play a significant role. More formally:

Hypothesis 1: When the SS format is used, behavior ratings will indicate not only their

designated competency domains but also a common method factor, which will explain a

substantial proportion of the variance.

Based on consistent reports of the ideal-employee factor emerging in high- and medium-stakes

self-assessments (e.g., Klehe et al., 2012; Schmit & Ryan, 1993) and widely reported effects of

exaggerated coherence (illusory halo) in assessments by external observers (e.g., Kahneman, 2011;

Landy et al., 1980; Murphy et al., 1993), we further hypothesize that the common method factor

influencing SS ratings will be mostly due to these influences.

Hypothesis 2a: In SS self-assessments, the common method factor will reflect the extent of

selective overreporting on behaviors associated with those of ‘‘ideal employee.’’

Hypothesis 2b: In SS assessments by others, the common method factor will reflect the extent

of indiscriminate overreporting on all behaviors, due to cognitive bias of exaggerated


When biasing effects are present, either statistical control or prevention is necessary to reduce the

irrelevant variance in measured competency constructs. Based on the previous reports of model-

based bias control in the SS format (e.g., Böckenholt, 2014; Bolt et al., 2014) and bias prevention

using the FC format (e.g., Bartram, 2007; Salgado & Táuriz, 2014), we predict that both methods

will be effective in reducing the irrelevant variance and therefore increasing construct validity of

measured competencies.

126 Organizational Research Methods 20(1)

Hypothesis 3: Construct validities of competency scores based on either bias-controlled SS

ratings or FC rankings will be better than those of straight SS ratings.


Hypothesis 3a: Internal (factorial) structure of competency assessments will show better

differentiation of behavioral domains.

Hypothesis 3b: Agreement between raters will increase, indicating improved convergent

validity of rater assessments.

Hypothesis 3c: Convergent correlations of competency scores with similar external constructs

will increase while correlations with dissimilar constructs will stay low.

If the statistical control and prevention methods are shown to be effective, it would be of major

importance to compare their effectiveness. To our knowledge, this has not been done before—

instead, separate research studies compared either method to the straight SS ratings. Therefore,

we have no specific hypotheses with regard to relative effectiveness of the two methods and

approach this question in exploratory fashion.



Participants in this study were from 21 organizations located in the UK. This was a convenience

sample, comprising archival appraisals data collected between 2004 and 2011. Of the assessed

N ¼ 922 managers, 65% were male, and 92% identified as White. All working ages were repre- sented, with the largest age groups being 30 to 34 years old (18.8%), 35 to 39 years old (18.1%), and 40 to 44 years old (17.2%). The best represented were not-for-profit organizations, including education, government, and health care (67.5% of all participants), and private companies in finance and insurance (17% of all participants).

The managers were assessed on key competencies by 795 bosses, 1,149 peers, and 1,857 sub-

ordinates as well as 874 managers providing self-assessments (total N ¼ 4,675 assessments). Not every target was assessed from all rater perspectives, including some absent self-ratings. The

numbers of raters per target were variable, ranging from 0 to 3 for bosses, 0 to 10 for peers, and

0 to 12 for subordinates. Whenever a particular rater category was present for a target, the average

number of boss raters was 1.08 per target, the average number of peers was 2.44, and the average

number of subordinates was 2.59.


Inventory of Management Competencies. The Inventory of Management Competencies (IMC; SHL, 1997) was administered to all raters to seek assessments on 16 competency domains listed in

Appendix. The IMC consists of 160 behavioral statements (e.g., ‘‘identifies opportunities to reduce

costs’’), with 10 statements measuring each competency. The statements are presented in 40 blocks

of 4, with statements within one block indicating different competencies. Responses are collected

using a unique response format, comprising single-stimulus ratings and forced-choice rankings. The

SS format requires respondents to rate every statement in the block using a 5-point frequency rating

scale (hardly ever, seldom, sometimes, often, nearly always). The FC format requires respondents to

perform partial ranking of four statements in the block, selecting one statement as most represen-

tative of the target’s behavior and one statement as least representative of his or her behavior. Thus,

Brown et al. 127

the SS and FC formats are used in the IMC questionnaire side by side, with every 4 items being rated

and ranked in one presentation. Here is a sample block with example responses:

The IMC has been shown to be a robust instrument for measuring managerial competencies in

multiple studies (e.g., Bartram, 2007; Warr & Bourne, 1999). The IMC scales yield internally

reliable scores; reported alphas for the SS format in the manual (SHL, 1997) range between .83

and .91 (median, .87). In the present study, alphas for the SS format ranged from .84 to .92 for self-

assessments,.89 to .94 for bosses,.87 to .93 for peers, and.86 to .93 for subordinates.

Occupational Personality Questionnaire. To examine the convergent and discriminant validity evi- dence for the IMC competency scores, we used self-reported personality assessments with the

Occupational Personality Questionnaire (OPQ32) available for N ¼ 213 targets in the present study. The OPQ32 is a well-established measure of 32 work-relevant personal styles, with a wealth

of materials on its reliability and validity available (Bartram, Brown, Fleck, Inceoglu, & Ward,

2006). The forced-choice OPQ32i version was used here, which consists of 104 blocks of four,

with statements within one block indicating different personality traits. Responses are collected

using partial ranking, selecting one statement in each block as most and one statement as least

descriptive of self. Normally this version is associated with ipsative scores; for this study, how-

ever, raw responses were used to estimate Thurstonian IRT scores on the 32 traits for each target—

the methodology yielding scores free of problems of ipsative data. To this end, we used an

approach that was later adopted for the successor of the OPQ32i, the OPQ32r, and is published

elsewhere (Brown & Bartram, 2009-2011).


All analyses in this article, unless stated otherwise, were performed in Mplus 7.2 (Muthén & Muthén,


Data considerations. In 360 degree–feedback data, independent sampling required by many statistical tests cannot be assumed. Instead, targets are the primary sampling units, rater perspectives are the

secondary sampling units, and the raters within the perspectives are the third-level sampling units.

Figure 1 illustrates this nested structure. Here, individual assessors (Level 1) are nested within rater

perspectives (Level 2), which are in turn nested within targets (Level 3).

With nested data, two types of effects might be of interest. First, the researcher may be interested

in pooled effects resulting from variation at all levels—variation due to idiosyncratic rater differ-

ences (Level 1), differences between rater perspectives (Level 2), and individual differences

between targets (Level 3). In the present study, the pooled effects were considered when deriving

scores on the 360-degree appraisal tool (see the section ‘‘Fitting Measurement Models’’). To account

for nonindependence of observations due to cluster sampling when computing standard errors and a

chi-square test of model fit, the Mplus feature TYPE¼COMPLEX was used (Asparouhov, 2006). Second, the researcher may be interested in separating the effects due to specific nesting levels. In

the present study, the extent to which the different sampling levels (e.g., being any rater of target X

Mr John Smith . . . Rating (Single Stimulus) Ranking (Forced Choice)

. . . is entrepreneurial Hardly ever Least

. . . draws accurate conclusions Often Most

. . . leads the team Often

. . . produces imaginative solutions Seldom

128 Organizational Research Methods 20(1)

vs. being a peer rater of target X) influenced similarity of ratings was of interest. We used multilevel

(three-level) modeling to this end (see the section ‘‘Evaluating Rater Agreement’’).

Fitting measurement models to each rater perspective. To infer measurement from the observed responses of selves, bosses, peers, and subordinates to SS and FC questions, we fitted appropriate

CFA models with categorical variables (aka IRT models) using the unweighted least squares esti-

mator with robust standard errors (denoted ULSMV in Mplus). Since the IMC was designed to

measure 16 related competencies, all measurement models comprised 16 correlated latent traits. In

the SS format, the competencies were indicated by their respective item responses. In the FC format,

the competencies were indicated by the respective latent utilities of items, which in turn were

indicated by dummy coded rankings of items within blocks (see the subsections ‘‘SS Response

Formats’’ and ‘‘FC Response Format’’ for detail).

To test the hypotheses, two alternative models were fitted to the SS responses, and one model was

fitted to the FC responses. The models are illustrated schematically in Figure 2. The SS model

comprising 16 correlated latent traits reflected the view whereby only substantive competency

perceptions cause variability in SS ratings. The SS-Method model, comprising a common ‘‘Method’’

factor in addition to the 16 correlated latent traits, reflected the alternative view whereby both

competency perceptions and common-to-all-items biases cause variability in SS ratings. The

Method factor was assumed uncorrelated with the 16 competency factors; however, the competency

factors were freely correlated to allow the substantive overlap between them (e.g., due to overall job

competence). Finally, the FC model, comprising the 16 correlated latent traits, reflected the view

that only competency perceptions cause variability in FC rankings. Specific details of the SS and FC

measurement models are described in the following.

Model fit (here, how well the model reproduces the observed tetrachoric/polychoric correlations)

was assessed by the chi-square test (w2), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). For RMSEA, the cut-off .05 has been suggested

for close fit (MacCallum, Browne, & Sugawara, 1996). For SRMR, a value of .08 or less is

considered indicative of an acceptable fit (Hu & Bentler, 1999).

SS response format. Since the single-stimulus response options comprised five ordered categories, Samejima’s (1969) graded response model was used to link the item responses to their respective



RATER PERSPECTIVES � � � � � � � � �

Figure 1. Hierarchical nesting of external raters. Raters are nested within perspectives (boss ¼ “, peers ¼$, and subordinates ¼ #), and perspectives are nested within targets of assessment.

Brown et al. 129

latent traits. Thus, for each item, four thresholds were estimated and one factor loading on the

respective competency factor plus an additional factor loading on the Method factor in the SS-

Method model.

FC response format. To fit Thurstonian IRT models to forced-choice IMC responses, we followed the standard procedure recommended for partial ranking data and used a macro published by Brown and

Maydeu-Olivares (2012) to create an Mplus syntax.2 First, rankings of four items (A, B, C, D) in

each forced-choice block were coded as six binary dummy variables, representing all pairwise

comparisons between items fA, Bg, fA, Cg, fA, Dg, fB, Cg, fB, Dg, and fC, Dg. Each dummy variable was coded 1 if the first item in the pair was preferred to the second item and 0 otherwise.

Since the outcome of comparison between two items not selected as most or least was not observed,

it was coded missing. Here is an example partial ranking and corresponding dummy codes:

1. SS (Single-Stimulus) 2. SS-Method (Single-Stimulus with Method factor)

Competency 1

c1_i1 c1_i2

… c1_i10

Competency 2

Competency 3

Competency 16

c2_i1 c2_i2

… c2_i10

c3_i1 c3_i2

… c3_i10

c16_i1 c16_i2

… c16_i10



Competency 1

c1_i1 c1_i2

… c1_i10

Competency 2

Competency 3

Competency 16

c2_i1 c2_i2

… c2_i10

c3_i1 c3_i2

… c3_i10

c16_i1 c16_i2

… c16_i10


3. FC (Forced-Choice)


Competency 1 c1_t1 c1_t2

Competency 3

Competency 4

Competency 12

c3_t1 c3_t2

c4_t1 c4_t2

c12_t1 c12_t2


{c1_i1, c3_i1} {c1_i1, c4_i1} {c1_i1, c12_i1} {c3_i1, c4_i1} {c3_i1, c12_i1} {c4_i1, c12_i1}

FC block 40


FC block 1



Figure 2. Alternative measurement models for Inventory of Management Competencies (IMC) item responses.

130 Organizational Research Methods 20(1)

Overall, 240 binary dummy variables were created (6 per each of the 40 blocks). Each dummy

variable fA, Bg was modeled as categorization of the difference between two latent variables—the utility for Item A, tA, and the utility for Item B, tB. The item utilities in turn were modeled as the

indicators of their respective competency factors. These features of the FC measurement model

are illustrated in Figure 2.

When complete rankings are collected, fitting the FC model is almost as straightforward as fitting

the SS model. However, because the IMC collects partial rankings, missing responses must be

imputed to avoid bias in parameter estimation (for full detail of the missingness mechanism in

partial rankings—missing at random [MAR] but not missing completely at random [MCAR]—and

of the problem with estimating MAR missing data with limited information estimators such as

unweighted least squares used in this study, see Brown & Maydeu-Olivares, 2012). The multiple

imputation method implemented in Mplus is based on a Bayesian estimation of an unrestricted

model (i.e., model in which the observed dummy-coded binary variables are correlated freely),

which is then used to impute the missing values (Asparouhov & Muthén, 2010). Ten imputed data

sets (independent draws from the missing data posterior) were created, and the Thurstonian model

was then fitted to each set. The estimated parameters in the 10 sets were averaged, yielding the final

item parameters (factor loadings, thresholds, and error variances) and the latent trait parameters (i.e.,

means, variances, and covariances).

Exploring the latent trait covariance structures. To establish the structural validity of competency assessments in each measurement model, the correlations between the 16 latent competency traits

were factor analyzed. Looking at the latent correlations provides the benefit of estimating the

relationships between error-free theoretical constructs rather than the attenuated correlations

between estimated scores. To establish the number of dimensions, optimal implementation of

parallel analysis (Timmerman & Lorenzo-Seva, 2011) was used, which is based on comparison

of the observed correlations to the 95th percentile of the randomly generated data with the same type

of correlations (here, Pearson’s) and the same type of underlying dimensions (here, factors). Then,

unweighted least squares factor analysis with oblique Oblimin rotation was carried out to find a

simple structure with correlated factors. The SS, SS-Method, and FC latent competency correlations

were examined in this fashion using the software FACTOR 9.2 (Lorenzo-Seva & Ferrando, 2013).

Estimating competency scale scores. To enable analyses of rater agreement and convergent and dis- criminant validities (see further sections), it was necessary to work with estimated rather than latent

competency scores.3 To place the scores from all the rater perspectives on the same scale, which is

essential in computation of intraclass correlations as well as averaging scores from all the external

observers (Woehr, Sheehan, & Bennett, 2005), we fitted the SS, SS-Method, and FC models

described previously using multiple-group CFA. Single-group models used so far are not suitable

for these types of analyses since they reset the origin and unit of measurement to each of the rater

perspectives. The technical detail of multiple-group CFA, sample Mplus syntax for conducting such

analyses and scoring responses, and the results of multiple-group analyses of the present study are

described in the downloadable Supplement to this article.

Partial Ranking Dummy Variables

A B C D fA, Bg fA, Cg fA, Dg fB, Cg fB, Dg fC, Dg Least Most 0 0 0 1 1 .

Brown et al. 131

Model-based competency factor scores were estimated using the Bayes estimation maximizing

the mode of the posterior distribution4 (maximum a posteriori, or MAP). Multivariate normal

distributions of the 16 traits with covariances equal to the estimated values in the respective

model were used as the prior. After the scoring procedures were applied to each of the three

measurement models (SS, SS-Method, and FC), each target had three sets of competency scores

for self-assessment (if completed), three sets of scores for each (if any) participating boss, three

sets of scores for each (if any) participating peer, and three sets of scores for each (if any)

participating subordinate.

Evaluating rater agreement. To quantify rater agreement on the traits measured by each of the alternative models, intraclass correlation coefficients (ICC) of the estimated scale scores were

computed at two hierarchical levels of nesting, as illustrated in Figure 1. The ICC is a measure of

homogeneity among scores within units of analysis (Hox, 2010)—therefore it reflects the extent of

absolute agreement between raters. In three-level models, the intraclass correlation is calculated

based on the intercept-only model of assessment scores, which partition the total variance into three

components—between targets (s2target) at Level 3, between perspectives on the same target

(s2perspective) at Level 2, and between individual raters from the same perspective (s 2 rater) at Level 1.

With this, the ICC at the highest nesting Level 3 (assessment targets),

rtarget ¼ s2target

s2target þ s2perspective þ s2rater ð5Þ

is an estimate of the population correlation between two randomly chosen raters of the same target.

Because raters from the same perspective of the target are necessarily nested within the target, the

ICC at Level 2 (rater perspective),

rperspective ¼ s2target þ s2perspective

s2target þ s2perspective þ s2rater ð6Þ

is an estimate of the population correlation between two randomly chosen raters from the same

perspective on the target (Hox, 2010). We note that rperspective cannot be lower than rtarget. The larger the difference between the ICCs at two hierarchical levels, the more variation in ratings can be

attributed to the rater perspective.

Computing convergent and discriminant validities of competency scores. To provide evidence for con- vergent and discriminant validity of the traits measured by the alternative IMC models, their

correlations with personality traits as measured by the OPQ32 were computed. To match the IMC

competencies with the conceptually concordant OPQ32 personality traits, each of the authors under-

took independent reviews of the construct descriptions, followed by the panel discussion. Due to a

substantial parallelism of both assessment tools, IMC and OPQ32, the matching procedure was

straightforward. For each IMC competency, one or two most concordant personality constructs

measured by the OPQ were identified (e.g., IMC Persuasiveness and OPQ Persuasive). All but one

IMC competency (Written Communication) yielded at least one matching personality construct. For

Resilience, the OPQ trait Tough Minded was hypothesized concordant with the self-assessments of

Resilience, while OPQ trait Emotionally Controlled was hypothesized concordant with the others

assessments (i.e., behavioral expressions of Resilience are likely to look as Emotional Control to

others). For the matched Competency-Personality Trait pairs, the correlation coefficients were

computed between the OPQ32 score and the IMC self-assessment for N ¼ 202 targets and between the OPQ32 score and the average IMC assessment by external observers of N ¼ 208 targets.

132 Organizational Research Methods 20(1)


Hypothesis 1: Emergence of a Common Method Factor in SS Ratings

Hypothesis 1 proposed that in the SS response format, behavior ratings would indicate not only their

designated competency domains but also a method factor common to all items. To test this hypoth-

esis, we compared the parameters and goodness of fit of the two alternative measurement models for

rating scale data—the SS model and the SS-Method model. If the SS-Method model, which assumes

a common-to-all-items latent factor in addition to 16 correlated traits (competencies), fits the

observations better than the SS model and the Method factor explains significant proportions of

variance in responses, we have evidence for Hypothesis 1.

The SS and SS-Method models converged for all rater perspectives. Goodness-of-fit indices for

the respective models5 are given in Table 1. Despite the significant w2 statistics (not surprising considering the very large sample size in this study), both SS and SS-Method models had a good

exact fit according to the SRMR, which was comfortably below .08 for all rater perspectives. The

RMSEA values below .05 also suggested a good approximate fit for both models. Because the SS

model is nested in the SS-Method model, we performed the difference of w2 tests. As can be seen from the Dw2 results presented in Table 1, controlling for the Method factor improved the model fit. The improvement was not only highly statistically significant (which is easily achievable with large

samples) but also practically important judging by the nontrivial improvements in the w2/df ratios, the RMSEA, and particularly the SRMR, which is an absolute measure of correspondence between

the observed and predicted polychoric correlations.

Appraising the SS-Method model parameters, all the item loadings on their respective compe-

tency factors were positive and significant. Importantly, all the Method factor loadings were also

positive and significant and of approximately the same magnitude on average as the substantive

factor loadings (the factor loadings will be considered in more detail in the results related to

Hypothesis 2). The competency factors explained slightly more variance than the Method factor

in the average item for self-ratings (30% vs. 25%) and boss ratings (35% vs. 29%) and slightly less variance than the Method factor in the average item for peer ratings (29% vs. 32%) and subordinate ratings (24% vs. 36%).

Taken together, the better goodness of fit of the SS-Method model and the substantial amount of

variance explained by the Method factor comparable to the variance explained by competency

factors support Hypothesis 1. Controlling for the Method factor is indeed important in modeling

SS data.

Hypothesis 2: Nature of the Common Method Factor

Hypothesis 2 proposed that the common Method factor influencing SS ratings would be mostly due

to ideal-employee type distortion for selves and due to exaggerated coherence (illusory halo) for

others. To see what construct the Method factor represented for each rater perspective, we explored

the factor loadings of its indicators. The Method factor loadings were all positive, significant, but

varied in magnitude greatly (the minimum unstandardized loadings were 0.3-0.4 depending on rater

perspective, and the maximum were 1.4-1.7). The Method factor had nonuniform effect on item

responses, thus unlikely representing rating style biases.

Hypothesis 2a: Self-Ratings. For the self-perspective, items with the largest loadings on the Method factor were ‘‘produces imaginative solutions’’ and ‘‘generates imaginative alternatives,’’ measur-

ing Creativity & Innovation (unstandardized loadings 1.41 and 1.31, respectively). These were

followed by ‘‘is committed to achieving high standards’’ (Quality Orientation), ‘‘thinks in strate-

gic terms’’ (Strategic Orientation), and ‘‘motivates others to reach team goals’’ (Leadership). The

Brown et al. 133

lowest loading items included ‘‘uses correct spelling and grammar’’ (unstandardized loading .38),

measuring Written Communication; ‘‘works long hours,’’ measuring Personal Motivation; and

‘‘fits in with the team,’’ measuring Interpersonal Sensitivity. Based on these findings, the Method

factor in self-assessments emphasized the most desirable managerial behaviors across different

measured competencies and deemphasized unimportant or less desirable behaviors (although

some of these behaviors may be important for employees at non-managerial levels). The common

method effect had all the features of the ideal-employee factor described by Schmit and Ryan

(1993), and in the present study of managers, it could be labeled the ideal-manager factor. Thus,

hypothesis Hypothesis 2a was supported.

Hypothesis 2b: Ratings by external observers. For bosses, items with the largest loadings on the Method factor were ‘‘is effective in leading others’’ and ‘‘builds effective teams,’’ measuring Leadership

(unstandardized loadings 1.74 and 1.63, respectively), followed by several items measuring Quality

Orientation (e.g., ‘‘sets high standards’’). Exactly the same items had the largest loadings for sub-

ordinates (unstandardized loading for ‘‘is effective in leading others’’ was 1.77). For peers, items

measuring Quality Orientation (e.g., ‘‘produces high quality results,’’ 1.64) had the largest loadings

on the Method factor, followed by some items measuring Leadership, Action Orientation, and

Creativity and Innovation. The items with lowest loadings were similar across the external rater

perspectives. Item ‘‘pays attention to the political process’’ had the lowest loading for bosses and

peers (0.36 and 0.39, respectively), and item ‘‘takes a radical approach’’ had the lowest loading for

subordinates (0.45). For all external perspectives, items ‘‘works long hours,’’ ‘‘is profit conscious,’’

Table 1. Goodness of Fit for Alternative Measurement Models in Perspective-Specific Analyses.

Model SS SS-Method FCa

Observed Variables 80 (5 Categories) 80 (5 Categories) 120 (2 Categories)

Degrees of freedom 2,960 2,880 Ddf [80] 6,800b

w2 Dw2

Self 6,627 5,675 [787]c 9,914 Boss 7,497 6,311 [1,160]c 10,051 Peers 7,615 6,278 [1,225]c 9,694 Subordinates 11,184 8,889 [2,047]c 11,463

RMSEA (90% CI) Self .038 (.036-.039) .033 (.032-.035) .023 (.022-.024) Boss .044 (.043-.045) .039 (.037-.040) .025 (.024-.025) Peers .037 (.036-.038) .032 (.031-.033) .019 (.018-.020) Subordinates .039 (.038-.039) .034 (.033-.034) .019 (.018-.020)

SRMR Self .061 .052 .080 Boss .064 .052 .086 Peers .059 .047 .077 Subordinates .052 .043 .064

Note: The models were limited to the first half of the questionnaire to enable calculation of w2 and RMSEA (see Footnote 5); SRMR was calculated on the full questionnaire. 90% CI ¼ 90% percent confidence interval; SS ¼ single stimulus; FC ¼ forced choice; RMSEA ¼ root mean square error of approximation; SRMR ¼ standardized root mean square residual. aValues for the first imputed dataset are reported. bThe degrees of freedom in the FC models were adjusted for redundancies among the thresholds and tetrachoric correlations estimated from the binary outcome variables (Maydeu-Olivares, 1999). There are 4 redundancies per block of 4 items, thus the degrees of freedom printed by Mplus were reduced by 4� 20¼ 80. cDw2 is not equal to the difference of the w2 because the difference of w2 are not distributed as chi-square when estimators with robust errors are used (such as the ULSMV used in this study), and adjustments need to be made (the Mplus function DIFFTEST accomplishes that).

134 Organizational Research Methods 20(1)

‘‘identifies opportunities to reduce costs,’’ and ‘‘uses correct spelling and grammar in writing’’ were

among the least salient.

These findings suggest that the Method factor captured a similar construct for the ratings by

external observers and selves, with most desirable managerial behaviors affected most and to the

same extent across the rater perspectives. Table 2 provides the correlations between the Method

factor loadings for each pair of rater perspectives. It can be seen that the loadings were indeed very

similar, with closest correspondence between bosses and peers (r ¼ .90) and least correspondence between selves and subordinates (r ¼ .74).

These results suggest that the Method factor had a very similar meaning for self-ratings and other

ratings. For the external rater perspectives, the effect does not appear fully consistent with the

definition of illusory halo as the cognitive bias of exaggerated coherence because the latter should

lead to uniform overreporting or underreporting of all characteristics depending on whether the

target is appraised positively or negatively in general (Murphy et al., 1993). Instead, we observed

greater distortion of characteristics that the assessors know to be more important for the rated

manager’s appraisal outcome (we will hypothesize possible mechanisms for this in the Discussion).

This process may have been supplemented by the bias of exaggerated coherence (illusory halo),

which we hypothesized. However, it is impossible to separate the two processes with the present

design, and due to the overriding nonuniform effect, we have to reject Hypothesis 2b.

Hypothesis 3: Improved Construct Validity With the Bias Control and Prevention Methods

Hypothesis 3a. Factorial structure of competency domains. Hypothesis 3a proposed that both bias- controlled SS ratings and FC rankings would yield more meaningful factorial structure (i.e., differ-

entiated behavioral domains in line with theoretical expectations) than straight SS ratings. To test

this hypothesis, we compared the covariance structures of the 16 latent competency traits emerging

from each measurement model.

The SS model was characterized by a strong positive manifold of all competency correlations for

every rater perspective. Table 3 reports the average off-diagonal latent (error free) correlations,

which ranged from r ¼ .51 for self-ratings to an astonishing r ¼ .65 for subordinates. Not surpris- ingly, the latent traits were highly suitable for factor analysis—the Kaiser-Meyer-Olkin (KMO)

measures of sampling adequacy were ‘‘good’’ or ‘‘very good.’’ Parallel analysis revealed that just

one factor was sufficient to describe the variability in competency constructs, explaining over 50% of variance for all rater perspectives (see Table 3).

In the SS-Method model, the average off-diagonal latent correlations were positive but much

lower than in the SS model (see Table 3). While correlations between conceptually concordant

competencies remained as strong as in the SS model (e.g., Commercial Awareness and Strategic

Orientation still correlated around .60 to .70 for all rater perspectives), correlations between con-

ceptually unrelated competencies disappeared (e.g., Specialist Knowledge and Resilience correlated

around .40 to .50 in the SS model but became zero in the SS-Method model). The data were no

longer well suited for factor analysis—the KMO measures were barely over .5 and classified as

Table 2. Correlations between The Method Factor Loadings in Perspective-Specific Analyses.

Self Boss Peers

Boss .78 Peers .84 .90 Subordinates .74 .83 .83

Note: Correlations are computed across k ¼ 160 item factor loadings.

Brown et al. 135

‘‘bad’’ (see Table 3). Despite this result, we explored the factorial structure underlying the compe-

tency constructs. Parallel analysis suggested four factors for self-assessments and three factors for

bosses, peers, and subordinates. The three-factor solutions for external ratings yielded very strong

conceptual similarities. The three factors could be labeled: (1) Executing, indicated by Problem

Solving and Analysis, Specialist Knowledge, Planning and Organizing, Written Communication,

and Quality Orientation; (2) Getting Ahead, indicated by Commercial Awareness, Creativity and

Innovation, Strategic Orientation, Action Orientation, Oral Communication, Persuasiveness, and

Personal Motivation; and (3) Getting Along, indicated by Interpersonal Sensitivity, Leadership,

Flexibility, and Resilience. Self-ratings yielded one further factor—Communicating—separating

Oral Communication and Persuasiveness into a distinct domain.

The FC model converged for the self- and peer perspectives; however, some additional con-

straints on factor loadings were necessary to avoid Heywood cases (i.e., negative variance esti-

mates) in models for bosses and subordinates. Table 1 reports goodness-of-fit indices for the FC

models. It can be seen that despite the significant w2, the w2/df ratios were better than for the SS and SS-Method models, and approximate fit according to the RMSEA was good. The SRMR also

indicated acceptable fit for all rater perspectives except bosses, for whom this index was only

slightly above the cut-off.

The FC models yielded patterns of the latent competency correlations similar to the SS-Method

models. Table 3 shows that the average off-diagonal correlations were positive but low6; however,

strong positive relationships between conceptually related competencies were preserved. The FC

competencies were slightly more suitable for factor analysis than the SS-Method competencies—the

KMO was ‘‘bad’’ for selves but ‘‘mediocre’’ for all external rater perspectives. Parallel analysis

suggested four factors for self-assessments and three factors for external perspectives. The three

factors for the external perspectives were Getting Ahead, Executing, and Getting Along, with the

same main competencies indicating them as in the SS-Method model but some differences in

salience of various competencies.

To summarize the factor analysis results, the SS-Method and FC models yielded well-

differentiated behavioral domains in line with theoretical expectations. Specifically, conceptually

Table 3. Exploratory Factor Analyses of the 16 Latent Competency Constructs by Measurement Model in Perspective-Specific Analyses.

Model SS SS-Method FC

Average off-diagonal correlation Self .51 .07 .04 Boss .54 .12 .06 Peers .57 .07 .05 Subordinates .65 .06 .04

Kaiser-Meyer-Olkin (KMO) test Self .88 .53 .57 Boss .87 .58 .60 Peers .89 .54 .63 Subordinates .91 .57 .60

Number of factors (% of variance explained) Self 1 (59) 4 (50) 4 (58) Boss 1 (58) 3 (50) 3 (52) Peers 1 (64) 3 (48) 3 (54) Subordinates 1 (70) 3 (47) 3 (49)

Note: SS ¼ single stimulus; FC ¼ forced choice.

136 Organizational Research Methods 20(1)

related competencies correlated strongly while conceptually unrelated competencies did not corre-

late. This is in contrast to straight SS ratings, which yielded competency scores correlating strongly

regardless of conceptual similarity, with just one common factor underlying them. The theoretically

justified patterns of competency correlations as well as the meaningful factorial structures emerging

from the method-controlled SS ratings and the FC rankings confirm the hypothesis.

Hypothesis 3b. Rater agreement. Hypothesis 3b proposed that both bias-controlled SS ratings and FC rankings would yield better rater agreement compared to straight SS ratings. Intraclass correlations

of estimated competency scores in three-level models are reported in Table 4. We remind the reader

that only external rater perspectives were included in these analyses (self-assessments were not

included). In the SS model, agreement between two random raters of the same target was moderate

(average rtarget ¼ .25). The common perspective improved the within-target agreement a little, yielding the average rperspective ¼ .32. However, only for Strategic Orientation and Action Orienta- tion the increment rperspective – rtarget was substantial (.12 and .13, respectively).

In the SS-Method model, agreement between two random raters of the same target was better than

in the SS model (competency average was rtarget ¼ .31). The common perspective again improved the within-target agreement, yielding the average rperspective ¼ .39. Four competencies achieved increments over .1; these were Strategic Orientation (rperspective – rtarget ¼ .23), Planning and Organizing (.13), Action Orientation (.15), and Persuasiveness (.13).

For the FC model, agreement due to common target was substantially better than for the SS

model and slightly better than for the SS-Method model (average rtarget ¼ .33). The common perspective improved the within-target agreement, reaching the average rperspective ¼ .41. Five

Table 4. Agreement Among External Observers (Boss, Peers, and Subordinates) Within Corresponding Sampling Units, by Multiple-Group Measurement Model.

Measurement model SS SS-Method FC

Sampling unit rtarget rperspective rtarget rperspective rtarget rperspective

Commercial Awareness .29 .38 .45 .51 .51 .58 Specialist Knowledge .32 .35 .36 .43 .36 .46 Problem Solving and Analysis .22 .27 .33 .36 .37 .42 Creativity and Innovation .20 .28 .29 .33 .30 .37 Strategic Orientation .19 .31 .26 .49 .32 .54 Planning and Organizing .28 .36 .31 .44 .31 .47 Action Orientation .22 .35 .29 .43 .27 .47 Oral Communication .24 .31 .28 .33 .26 .31 Written Communication .25 .30 .30 .34 .30 .33 Interpersonal Sensitivity .30 .34 .42 .45 .40 .45 Persuasiveness .20 .29 .23 .36 .22 .33 Leadership .30 .37 .31 .37 .34 .41 Quality Orientation .29 .33 .28 .33 .32 .37 Flexibility .19 .26 .30 .36 .35 .40 Resilience .20 .26 .22 .26 .26 .32 Personal Motivation .30 .36 .40 .44 .36 .38

Competency average .25 .32 .31 .39 .33 .41 Overall appraisal score .21 .29 .22 .31 .31 .40

Note: Overall performance is computed as the sum of the 16 competency scores. Intraclass correlations rtarget and rperspective are calculated using Equations 5 and 6. Increments rperspective � rtarget > .1 are underlined. SS ¼ single stimulus; FC ¼ forced choice.

Brown et al. 137

competencies showed increment over .1; these were Strategic Orientation (rperspective – rtarget ¼ .22), Action Orientation (.19), Planning and Organizing (.16), Persuasiveness (.11), and Specialist

Knowledge (.10).

To summarize, the SS model yielded the lowest agreement values, the SS-Method model yielded

substantially better agreement values, and the FC model performed best. For 12 out of 16 compe-

tencies, the FC model yielded small increments in agreement over the SS-Method model (most of

which were statistically significant given the very large sample size for these analyses). To avoid

multiple significance testing but provide a test of overall model performance, we computed inter-

rater agreement based on the sum of the 16 competency scores, representing the overall appraisal

score for each alternative model. While the differences between the SS and SS-Method models were

small (rperspective¼ .29 and .31, respectively), the FC model yielded a substantially better agreement (rperspective¼ .40) over targets’ overall performance appraisal. This confirms the hypothesis and also provides evidence for the relative performance of the bias control (SS-Method model) and preven-

tion (FC model) methods.

Hypothesis 3c. Convergent and discriminant validity. Hypothesis 3c proposed that both bias-controlled SS ratings and FC rankings would yield higher correlations with similar external constructs while

not increasing correlations with dissimilar constructs. Table 5 presents the correlations between the

Table 5. Correlations Between OPQ32 Traits and IMC Competencies Scored by Multiple-Group Measure- ment Models.

Self (N ¼ 202) Others, Mean (N ¼ 208)

IMC OPQ32 SS SS-Method FC SS SS-Method FC

Commercial Awareness Competitive .27** .30** .31** .19** .23** .19** Specialist Knowledge Data Rational .21** .35** .31** –.02 .14* .19** Problem Solving and Analysis Evaluative .37** .50** .49** .13 .25** .28**

Data Rational .21** .42** .43** –.03 .22** .31** Creativity and Innovation Innovative .67** .69** .66** .22** .43** .36**

not Conventional .38** .37** .35** .25** .32** .19** Strategic Orientation Forward Thinking .35** .40** .34** –.01 .13 .20** Planning and Organizing Conscientious .39** .40** .35** .22** .32** .32**

Detail Conscious .34** .49** .49** .20** .45** .49** Action Orientation Decisive .39** .39** .48** .28** .29** .35** Oral Communication Socially Confident .42** .46** .38** .07 .15* .12 Interpersonal Sensitivity Caring .31** .44** .45** .12 .24** .24** Persuasiveness Persuasive .42** .46** .34** .11 .25** .17* Leadership Controlling .48** .32** .37** .29** .12 .24** Quality Orientation Detail Conscious .34** .46** .38** .23** .39** .41** Flexibility Adaptable .04 .18* .20** .15* .11 .08 Resilience Tough Minded .29** .34** .31**

Emotionally Controlled .13 .10 .14* Personal Motivation Achieving .44** .57** .54** .23** .41** .41**

Average convergent validity .35 .42 .40 .14 .25 .26 Average discriminant validity Raw values .03 .01 –.01 .01 .00 –.01

Absolute values .12 .12 .12 .07 .09 .09

Note: IMC ¼ Inventory of Management Competencies; OPQ32 ¼ Occupational Personality Questionnaire; SS ¼ single stimulus; FC ¼ forced choice. *Correlation is significant at .05 level (2-tailed). **Correlation is significant at .01 level (2-tailed).

138 Organizational Research Methods 20(1)

IMC competencies estimated using the alternative measurement models and conceptually concor-

dant personality traits measured by the self-reported OPQ32. As expected, self-assessments of

competencies yielded substantial correlations with self-reported personality. The SS model fared

worst (average convergent correlation r¼ .35), while the SS-Method model and FC model did better (average r¼ .42 and r¼ .40, respectively), although most differences between correlations were not statistically significant with the current sample size (N ¼ 202).

Assessments by external observers correlated weaker with self-reported personality; neverthe-

less, most hypothesized correlations were statistically significant and positive. Here, the advantages

of either modeling biases or preventing them were even more obvious than in the self-assessments,

improving the average validity of the SS model (average r ¼ .14) more substantially (and yielding statistically significant improvements for several competencies), reaching the average validity r ¼ .25 for the SS-Method model and r ¼ .26 for the FC model. However, just like for the self- assessments, differences between the convergent correlations in the SS-Method and FC models

were inconsistent across competencies and insignificant.

While improving the convergent correlations, the bias control and prevention methods did not

inflate the correlations between conceptually unrelated constructs of IMC and OPQ—the heterotrait-

heteromethod correlations for self-assessed and other-assessed competencies were low and much

lower than the convergent correlations (see Table 5). The convergent and discriminant validity

evidence of the improvements achieved by the use of bias control and prevention methods confirms

the hypothesis. We also have evidence for the approximately equal performance of both methods in

relation to convergent and discriminant validities.


The objective of this study was to examine the extent to which 360-degree appraisals of compe-

tencies are subject to biases, examine the nature of these biases, and test whether validity gains can

be achieved by either statistically controlling for biases or preventing them with forced-choice

formats. We compared operational appraisals data collected using Likert scales (SS format) with

multidimensional forced-choice (FC format) data by applying model-based measurement. We sys-

tematically compared three methods of inferring measurement: (1) assuming that only substantive

constructs (competencies) are captured by the SS format, (2) modeling a method factor to control for

biasing effects in the SS format, and (3) preventing the occurrence of biases by employing the FC

format. To our knowledge, the present study is the first to apply Thurstonian IRT modeling (Brown

& Maydeu-Olivares, 2011) to multisource feedback collected using multidimensional forced choice

to overcome the problems of ipsative data and ensure proper scaling of competencies.

The results suggested that SS responses were subject to strong common method biases at the item

level, making behavior ratings across all competencies highly similar. When ignoring these effects

in scoring, one factor was sufficient to explain the variability in theoretically distinct and diverse 16

competencies. Clearly, administering a long instrument (the IMC comprises 160 items) to measure

just one construct defeats the purpose of a differentiated 360-degree assessment; it is a waste of time

for everyone involved in the process.

The important question is what caused this similarity of assessments on all competencies—

substantive overlap (i.e., real clustering of competencies in the same individuals or true halo), rater

biases (including seeming clustering of competencies in the same individuals or illusory halo and

desired clustering of competencies or ideal-employee factor), or both substantive overlap and rater

biases? From the analyses in which statistical modeling of a common method factor was attempted,

we found that both causes were at play, with rater biases having a greater influence. Indeed, while

the competency factors and the Method factor explained approximately equal amounts of variance in

the average item (around 30% each), only about half of the competency-related variance could be

Brown et al. 139

attributed to broader domains of competence—the substantive overlap (see the results of factor

analysis in Table 3, column SS-Method). By explicitly modeling rater biases, competency percep-

tions, and errors of measurement in SS ratings, this study contributes to the debate of differentiating

between illusory and true halo (Murphy et al., 1993).

The next important question is the nature of the detected rater biases. The evidence suggests that

the Method factor represented a meaningful construct, which was remarkably similar across the rater

perspectives. We found that the distortion was nonuniform, with the most salient indicators of the

Method factor representing the most desirable leadership behaviors spanning many competency

constructs, including transformational and transactional leadership qualities (Judge & Piccolo,

2004). The salient indicators incorporated positively charged words such as reach team goals, high

standards, motivate others, committed, effective, and so on. The weakest indicators of the Method

factor represented behaviors not typically associated with effective leadership, such as ‘‘writes in a

fluent manner’’ or ‘‘works long hours.’’ For self-assessments, this was not surprising since the ideal-

employee factor, previously found and replicated in the literature (Klehe et al., 2012; Schmit &

Ryan, 1993), has these features of emphasizing the important job characteristics. Interestingly,

however, external appraisal ratings had the same features. Instead of or in addition to expected

cognitive bias of exaggerated coherence (which presumably influences all behaviors uniformly), we

observed particular overreporting of behaviors that made the target look like a more effective

manager (ideal-manager factor). Interestingly, the extent of overreporting was similar across raters

of the same target—this is evident from non-ignorable nesting effects of the estimated Method factor

scores (rtarget ¼ .24 and rperspective ¼ .32). It appears that the external observers overreported behaviors of particular managers and that they were selective about which behaviors to overreport.

Two mechanisms might have been at play here. First, motivated distortions similar to impression

management but on behalf of another person might have taken place. This is in line with earlier work

by Murphy and Cleveland (1995) and also Murphy et al. (2004), who showed that raters do manip-

ulate appraisal ratings in pursuit of their own goals, for example, overreport others’ behaviors when

seeking greater harmony with colleagues (also Randall & Sharples, 2012) or distort selected beha-

viors of executives pursuing own political goals. Second,7 distortion may have resulted from raters

applying their own implicit theories of what makes an effective leader (e.g., Eden & Leviatan, 1975;

Rush, Thomas, & Lord, 1977), with the behaviors most central to perceived leadership effectiveness

affected most. Given that observing and evaluating leadership behavior is a complex cognitive

process and followers are unlikely to observe and accurately recall all behaviors, it has been argued

that the reliance on implicit leadership theories reduces the amount of information processing

involved (e.g., Rush et al., 1977). We believe that the Method factor identified in the present study

contributes to future research on nonuniform manipulation of behavioral ratings by external asses-

sors, be they driven by rater goals or implicit leadership theories or both. We cannot support or

disprove either explanation based on the data we have; experimental manipulations or external

covariates controlling for rater goals or the level of familiarity with the target of assessment could

delineate the possible causes. Future research should investigate the specific motivations underlying

such distortions, particularly with respect to different rater perspectives.

When biasing effects were explicitly modeled, the ‘‘purified’’ competency constructs captured

intended behaviors more closely. The strong positive correlations between conceptually concordant

competencies remained intact while correlations between unrelated competencies disappeared,

supporting the expected relatedness between some behaviors but also distinctiveness of domains

underlying managerial performance. As a result, meaningful second-order competency domains

emerged. Three easy-to-interpret dimensions were identified in the present study, which we labeled

Executing, Getting Ahead, and Getting Along, after the distinct vectors described by Hogan and

Shelton (1998). The bias-controlled competency scores also yielded substantially better interrater

agreement and convergent validities than the straight SS scores. To summarize, the researcher can

140 Organizational Research Methods 20(1)

make better use of the collected SS ratings by explicitly modeling the common method factor

causing all items to overlap.

Like the statistical bias control method, the prevention method using the FC format was

effective in improving validities of the competency constructs. In terms of structural validity of

FC rankings, the competency correlations were in line with theoretical expectations, and the

second-order competency domains were meaningful. This is not surprising since sufficient evi-

dence already exists for good structural validity of properly scaled FC rankings (e.g., Brown &

Maydeu-Olivares, 2011, 2013). Furthermore, the similarity of the SS-Method and FC factor

structures supports the validity of the SS-Method model and reinforces its effectiveness in separ-

ating the substantive and the biasing effects. In terms of rater agreement, the FC rankings yielded

substantial improvements compared to straight SS ratings and small improvements compared to

bias-controlled SS ratings. When the choice between behaviors was forced, impressive levels of

agreement were achieved for some competencies (e.g., rperspective ¼ .58 for Commercial Aware- ness and rperspective ¼ .54 for Strategic Orientation). Most importantly, further gains were made in perspective-related agreement, where previous research struggled to find any non-ignorable

effects (LeBreton et al., 2003; Yammarino, 2003). Five competencies showed perspective-

related increment in agreement exceeding .1, and two of them reached the increment around .2.

Such substantial differences support the practice of separating raters by perspective, while neg-

ligible differences provide evidence against this practice. Importantly, any discussions about the

behaviors for which raters have similar perceptions or for which rater perspective matters should

be based on scores that are as free from rating distortions as possible.

In terms of relationships with external personality measures, the FC rankings performed slightly

better on average than the straight SS ratings and on par with the bias-controlled SS ratings. These

findings corroborate earlier meta-analytic results (Salgado & Táuriz, 2014) on similar or slightly

higher convergent correlations of forced-choice questionnaires compared to single-stimulus ques-

tionnaires. The unique contribution of the present study is the demonstration of convergent and

discriminat validities of properly scaled (i.e., not ipsative) forced-choice rankings in the condition of

high biases. To our knowledge, this is the first study to show that reduction of biases with the forced-

choice format can actually translate into better correlations with external measures in the context of

high response distortions. Previous research in a low bias context found the FC format performing

slightly worse than the SS format (Brown & Maydeu-Olivares, 2013).

Overall, the FC format demonstrated substantial gains over the straight SS ratings in all aspects of

construct validity and further small gains over the bias-controlled SS ratings with respect to inter-

rater agreement. This is a significant achievement considering that the rater agreement analysis was

based on estimated scores and FC scores are typically less reliable than SS scores derived from the

same items (Brown & Maydeu-Olivares, 2013). Furthermore, the forced-choice format is not

immune to nonuniform distortions within the same block. What might be the mechanism for the

small incremental gains of FC rankings over bias-controlled SS ratings that we observed? Kahneman

(2011) argued that explicit comparisons (here, comparisons between behaviors from different beha-

vioral domains) engage the cognitive ‘‘System 2,’’ which comprises slow and rational thinking

processes. This contrasts the fast and intuitive ‘‘System 1,’’ which is engaged by default, resulting

in many cognitive biases. Kahneman attributed finer discrimination achieved by using comparative

judgments in his research to engagement of System 2 instead of System 1. The present research may

have evidence for such a differentiating and contrasting process having taken place. Despite the

strong similarities between the SS-Method and FC factor structures underlying the competency

constructs, the FC model comprised a larger number of small negative cross-loadings. For example,

boss appraisal of a target’s Interpersonal Sensitivity that primarily loaded on Getting Along in the SS

format kept its central role to that factor in the FC format but also acquired a weak negative loading

on Getting Ahead. Thus, ranking interpersonal sensitivity above other competencies indicated not

Brown et al. 141

only the boss’s high appraisal of the target’s ability to ‘‘get along’’ but also slightly lesser appraisal

of the target ability to ‘‘get ahead,’’ thus demonstrating how explicit contrasts with other behaviors

may have enhanced cognitions.

Conclusions and Recommendations

Today the evidence is overwhelming that response processes involved in 360-degree appraisals

are complex and are affected by response distortions, likely both unmotivated and motivated.

From the analyses of a large sample of responses to a comprehensive 360-degree assessment tool

in this study, we obtained the evidence that these distortions are so strong as to substantially

deteriorate the score validity. We therefore argue that whenever ratings are collected using Likert

scales, model-based control of biases at the item level is necessary. In the present study, we

modeled the common Method factor, retaining substantive overlaps between measured traits

(i.e., true halo) while removing biases affecting all items (e.g., due to motivated distortions aimed

to present a picture of an ideal manager by self or others, unmotivated cognitive bias of exag-

gerated coherence, or implicit leadership theories held by external observers). Following guidance

on model assessment by Williams, Edwards, and Vandenberg (2003), we obtained the evidence

that the Method factor (1) accounted for a very sizeable proportion of variance in responses, (2)

yielded significant improvements in terms of model fit, and (3) yielded significant improvements

in terms of convergent and discriminant validity.

Another and possibly even better alternative is bias prevention by collecting forced-choice

rankings of targets’ behaviors. The impressive validity gains obtained by using rankings when

compared to straight ratings, and small further gains in interrater agreement when compared to

ratings statistically controlled for the Method factor, suggest that forcing differentiation between

facets of assessment is a viable and effective method, which is well placed to capture the essence of

360-degree feedback—perceptual judgements of competencies. In practice, the FC method can be

implemented operationally so that the model parameters are established once (based on the multiple-

group CFA) and then applied automatically to all new assessments yielding estimated scores on

traits of interest. This is in contrast to impracticalities of modeling the Method factor in SS ratings,

which cannot be done once and for all since different assessment contexts are likely to change the

model parameters severely. To make the best use of the forced-choice method, careful matching on

desirability of behaviors within each block is strongly recommended to minimize nonuniform

response distortions; other guidelines for creating good forced-choice designs must also be followed

(e.g., Brown, 2016; Brown & Maydeu-Olivares, 2011; Maydeu-Olivares & Brown, 2010). Impor-

tantly, a suitable IRT-based model must be used to score the forced-choice responses to avoid

ipsative data, such as Thurstonian IRT models for dominance items (Brown & Maydeu-Olivares,

2011) or multi-unidimensional pairwise preference models for ideal-point items (Stark et al., 2005).

Practical implications are far-reaching as we can make better use of information collected in 360-

degree assessments but also improve performance appraisals and leadership assessment in organi-

zations more widely, which suffer from the same problems (Adler et al., 2016; Landy & Farr, 1980).


Inventory of Management Competencies (IMC) Competencies

1. Commercial Awareness: Identifying opportunities for new business and for cost savings.

Taking account of revenue and cash flow. Showing awareness of competitor activity.

2. Specialist Knowledge: Demonstrating specialist knowledge in job. Keeping up to date with

advances in own area of expertise. Quickly assimilating new technical information.

142 Organizational Research Methods 20(1)

3. Problem Solving & Analysis: Making rational judgments. Drawing appropriate conclu-

sions from information provided. Integrating data from different sources. Effective problem


4. Creativity & Innovation: Generating creative ideas. Coming up with imaginative solu-

tions and fresh insights to work-related issues.

5. Strategic Orientation: Understanding organizational strategy and corporate aims. Relating

own work or that of teams to long-term organizational goals.

6. Planning & Organizing: Effective planning and organizing. Setting up and monitoring

timescales and plans, allocating realistic time scales for activities, keeping track of


7. Action Orientation: Making decisions without delay. Making decisions under pressure.

Taking initiative to act.

8. Oral Communication: Speaking clearly and confidently. Presenting in a compelling man-

ner to groups. Expressing ideas clearly. Articulating key points of an argument concisely.

Responding to feedback from an audience.

9. Written Communication:- Writing clearly and succinctly. Using correct spelling and

grammar. Producing written communication that is easy to follow.

10. Interpersonal Sensitivity: Supporting others in their work. Interacting with others in a

sensitive way. Listening and showing concern for others.

11. Persuasiveness: Persuading others to own viewpoint. Convincing with counterarguments.

Lobbying effectively. Negotiating well. Changing opinions of others.

12. Leadership: Coordinating group activities. Building effective teams. Motivating and

empowering individuals or teams to reach organizational goals. Identifying development

opportunities for staff.

13. Quality Orientation: Setting high standards. Paying attention to quality issues. Producing

high quality results. Encouraging a sense of high standards in others.

14. Flexibility: Adapting own behavior to new circumstances. Reacting positively to change.

Supporting change initiatives.

15. Resilience: Staying calm under pressure. Keeping control in stressful situations. Working

effectively under pressure.

16. Personal Motivation: Showing drive and determination. Taking on new work. Seeking

career progression. Seeking responsibility. Working long hours.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or pub-

lication of this article.


The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

The online supplemental analyses and results are available at


1. This is in contrast to orthogonal bifactor models, which assume one general and several specific factors

mutually uncorrelated with each other. The general factor in such models is the only source of shared

variance between items measuring different traits, and the specific factors capture the residual variance

within specific domains not explained by the general factor. This approach would be suitable for modeling

Brown et al. 143

‘‘true halo’’—the substantive common cause of the overlap between competencies. In fact, any data where a

common second-order factor explains the covariation of measured traits can be presented in the bifactor

form (Rindskopf & Rose, 1988). Model presented in Figure 2 (Panel 2) is different in that it allows the

competency traits to correlate; therefore, the general ‘‘method’’ factor (e.g., ‘‘illusory halo’’) is not the only

source of shared variance between the items measuring different traits; it is in addition to any covariance

structure underlying the competencies (e.g., true halo).

2. Sample Mplus syntax for analysis of partial rankings in forced-choice blocks of size 4 is available for

download from the online version of Brown and Maydeu-Olivares (2012); the macro writing Mplus syntax

for analysis of forced-choice questionnaires of different configurations is available for download from http://

3. Multilevel modeling of the latent traits is possible; however, it is not computationally feasible in this study

due to the very large number of items and traits in the measurement part of the model.

4. To estimate the forced-choice scores, the average model parameters across 10 imputations were applied to

the original data set with one of every six responses missing by design.

5. Unfortunately, current computing capabilities are prohibitive of obtaining goodness of fit for models with a

very large number of categorical outcomes, such as the models in our study. To overcome this problem and

obtain a reasonable indication of how the alternative models fared in comparison, we obtained fit indices for

models including only the first half of observed responses (80 items rather than 160 items). Conveniently,

the IMC questionnaire employs a balanced design, with the first half containing 20 blocks of 4 items and

exactly 5 items measuring each competency. This allowed testing the measurement models with the full

hypothesized latent structure but the reduced number of observed indicators.

6. This is in contrast to ipsative scores, which always yield negative average off-diagonal correlation. For 16

scales, the off-diagonal correlations would necessarily average at –.07 (Brown & Maydeu-Olivares, 2013).

7. We would like to thank one of the anonymous reviewers for pointing out the possible link with implicit

leadership theories.


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Author Biographies

Anna Brown is a senior lecturer in psychological methods and statistics at the University of Kent, United

Kingdom. She received her master degree in computational mathematics from Lomonosov Moscow State

University, Russia, and her PhD in psychology from the University of Barcelona, Spain. Her research has

focused on advanced psychometric modeling using item response theory and other latent variable models; she is

particularly interested in modeling preference decisions and processes contributing to response biases. She has

extensive experience in psychometric test development, including her past role of a principal research statis-

tician with SHL Group, a global test publisher and management consultancy.

Ilke Inceoglu is a senior lecturer in organizational behavior and human resource management, and an associate

dean (research) at the University of Surrey, United Kingdom. She received her undergraduate degree in

philosophy from the University of St Andrews, United Kingdom; her M.Phil. in psychology from the University

of Glasgow, United Kingdom; and her PhD in organizational psychology from the University of Munich,

Germany. Prior to her current role, she worked for SHL Group, where in her last role as managing scientist

she directed the research and product development work of the UK R&D team. Her research has focused on

Brown et al. 147

work motivation and values, work behavior and performance, employee engagement, and more recently on

temporal issues in motivation and emotion.

Yin Lin is a PhD researcher at the University of Kent, United Kingdom, and a senior research scientist at CEB

SHL Talent Measurement Solutions. She holds a Master of Science in applied statistics from the University of

Oxford, and a Master of Arts in mathematics from the University of Cambridge. Prior to her current roles, she

worked as a statistician for the National Foundation for Educational Research in England and Wales. Her

current research focuses on multidimensional item response theory, forced-choice response modeling and

computerized adaptive testing.

148 Organizational Research Methods 20(1)

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