





































Graduate Students Journal of Psychology                                                                                  Copyr


Graduate Student Journal of Psychology                                                                             Copyright 2004 by the Department of Counseling & Clinical Psychology  
2004, Vol. 6                                                                                                                          Teachers College, Columbia University                          ISSN 1088-4661 
 
 
 

A Taxometric Analysis of Experimenter-Induced Response Bias  
on Self-Report Data 

 
Jessica Neubauer and Robert E. McGrath 

Fairleigh Dickinson University 
 

Taxometric analysis was developed to help determine whether a latent variable best conforms to a 
dimensional or categorical (taxonic) model.  One study (Beauchaine & Waters, 2003) found that 
the taxonic structure of ratings could be influenced through instructional set.  Their findings raise 
questions about whether taxometric analysis is necessarily identifying the organic structure of rat-
ings-based latent variables.  However, the study was limited to ratings of target individuals un-
known to the rater.  The present study was conducted to determine whether self-ratings are equally 
susceptible to manipulation.  Undergraduate students were asked to complete a battery of four self-
report personality measures which previous research indicated may tap a common latent variable.  
Participants randomly received an instructional set that implied either a taxonic or dimensional 
structure for self-ratings.  The results suggest that self-ratings may be more resistant to instructional 
set than ratings of others, as data from both groups conformed to a dimensional model rather than a 
taxonic structure. 

 
 

cut, and the means are then plotted.  The resulting graph 
will vary in shape depending on whether the underlying 
construct is continuous or discontinuous.  Specifically, a  

Psychologists have long recognized that many psycho-
logical measures offer, at best, a rough approximation of 
the latent variables they are intended to represent (Cronbach 
& Meehl, 1955).  The very structure of psychological con-
structs is often not understood, making it impossible, for 
example, to determine whether a dimensional or categorical 
model provides a more accurate representation of these 
constructs.  One consequence of such disparities is that psy-
chologists develop measurement devices without a clear 
conceptualization of their underlying construct.  Determin-
ing the underlying structure of a psychological construct is 
one step toward making it a more tangible entity.  Taxomet-
ric procedures (Meehl & Yonce, 1994, 1996; Waller & 
Meehl, 1998) are a set of statistical analyses developed for 
the purpose of determining whether a latent variable of in-
terest demonstrates an inherent dimensional or categorical 
structure when multiple indicators of that latent variable are 
available.  Several taxometric methods are currently avail-
able, including Mean Above Minus Below a Cut (MAM-
BAC), Maximum Eigenvalue (MAXEIG), and Latent Mode 
(Lmode). 

latent taxonic structure will be represented in a graph by an 
inverted U, whereas a dimensional structure will manifest 
as U-shaped.  As a consistency test for the results, the pro-
cedure can be repeated reversing the input and output vari-
ables.  When more than two indicators are available, it is 
possible to conduct a MAMBAC analysis twice for each 
possible pair.   

The MAXEIG method also requires that one indicator 
be designated the input variable, but it uses two or more 
other indicators as output variables.  Again, the input vari-
able is sorted.  Subsamples are created based on equally 
spaced intervals along the input variable, and the eigen-
value, based on a factor analysis of the output variables, is 
calculated within each of these subsamples.  In this case, 
the plot of eigenvalues as a function of interval should re-
sult in an inverted U shape for data that conforms to a 
taxonic model, whereas data that conforms to a dimensional 
model will be represented by a U shape.  The procedure can 
be repeated using each available indicator as the input vari-
able. 

MAMBAC involves designating one indicator as the 
input variable and a second indicator as the output variable.  
The input variable is sorted, and a series of “cuts” are then 
made at each value along the input variable.  The mean of 
the output variable is calculated, both above and below each  

Latent dimensional or categorical structures can also be 
identified graphically when research data are subjected to a 
third taxometric procedure known as Lmode.  This proce-
dure is based on a factor analysis of all available indicators, 
with the purpose of generating essentially error-free factor 
scores.  A unimodal frequency distribution of these scores 
is indicative of an underlying dimensional structure, 
whereas two or more peaks suggest a taxonic structure.  If 
enough indicators are available to allow selection of sub-

 
 
Jessica Neubauer and Robert E. McGrath, School of Psychology, 
Fairleigh Dickinson University.  Correspondence concerning this 
article should be addressed to Jessica Neubauer, School of Psy-
chology T-WH1-01, Fairleigh Dickinson University, 1000 River 
Road, Teaneck, NJ 07666-1914.  E-mail: JessNeubauer@aol.com 

32 



TAXOMETRICS AND RESPONSE STYLE 
 

sets, multiple Lmode graphs can be generated from subsets 
of indicators as a consistency check. 

Studies have found taxometric analysis superior to al-
ternative procedures, such as cluster analysis, as an indica-
tor of taxonic structure (Cleland, Rothschild, & Haslam, 
2000; Waller & Meehl, 1998).  The method has been used 
to evaluate the latent structure of various psychological 
constructs, including eating disorders (Tylka & Subich, 
2003), sexual orientation (Gangestad, Bailey, & Martin, 
2000; Haslam, 1997), antisocial behavior (Skilling, 
Quinsey, & Craig, 2001), and depression (Ruscio & Ruscio, 
2000).  For example, using five non-behavioral indicators, 
Tylka and Subich (2003) found consistent evidence of a 
dimensional structure across clinical eating disorders.  Such 
results highlight the value of a statistical method that incor-
porates procedures for evaluating the consistency of con-
clusions about taxonicity. 

Though numerous studies have demonstrated that the 
application of taxometric analyses may enhance under-
standing of psychological constructs, questions still remain 
as to how viable the results of such analyses are when they 
are based on data that has been collected via rating scales.  
A recent article by Beauchaine and Waters (2003) raises 
concerns about the degree to which the outcomes of 
taxometric analyses can be influenced by response set.  
Here, undergraduates were asked to evaluate the quality of 
graduate school admission essays based on six criteria.  
Raters were informed either that the “essays reflected a full 
range of graduate school performance,” or that they were 
“written by students who either struggled or excelled in 
graduate school” (Beauchaine & Waters, 2003, p. 6). 

The authors found consistent evidence that the struc-
ture of the ratings differed as hypothesized based on in-
structional set.  Analyses of ratings by individuals who 
were instructed that the essays’ authors reflected a full 
range of performance produced dimensional results, 
whereas those of raters instructed that authors either strug-
gled or excelled produced categorical results.  As a result, 
Beauchaine and Waters questioned whether taxonic struc-
ture can be manipulated through pre-existing expectancies.  
They concluded that “these results have potential implica-
tions for future taxometrics research and suggest that a sole 
reliance on rating-scale data may be ill-advised when test-
ing taxonic hypotheses” (Beauchaine & Waters, 2003, 
p.10). 

In fact, though, their results are only relevant to ratings 
of others who are unknown to the raters.  Therefore, it is 
uncertain whether their conclusions apply when the rater 
has already developed an impression of the target prior to 
receiving the instructions.  Specifically, the application of 
their conclusions remains unclear when both the rater and 
target are the same individual, as is the case in self-report.  
Since the studies listed above all used self-report as the 
basis for drawing conclusions about taxonicity, the present 
study was conducted to determine if the response bias de-
tected by Beauchaine and Waters (2003) extends to self-
report rating scales as well.  The results have important 

implications concerning the degree to which taxometric 
studies that involve self-report measures are reflecting in-
herent structure or rater perceptions. 
 

Method 

Participants 
A total of 115 undergraduate students at Fairleigh 

Dickinson University completed a battery of five self-report 
personality inventories.  Among those who provided demo-
graphic data, 70.8% (75) were females and 29.2% (31) were 
males.  The sample was 49% Caucasian, 25% African 
American, 9.6% Hispanic, 9.6% Asian, 1% Native Ameri-
can, and 5.8% Other.  The mean age of the respondents was 
20.47 years of age (SD = 4.39), with a range from 17 to 40. 

Procedure 
Participants were recruited from introductory psychol-

ogy and freshman seminar courses and asked to complete 
four personality measures.  These consisted of Rosenberg’s 
(1965) Self-Esteem Scale, the neuroticism scale of the Ey-
senck Personality Inventory (Eysenck & Eysenck, 1968), 
the Core Self-Evaluations Scale (Judge, Erez, Bono, & 
Thoresen, 2003), and a generalized self-efficacy scale 
(Judge, Erez, Bono, & Thoresen, 2002).  These scales were 
chosen because Judge and colleagues (2002) have provided 
evidence that these measures all tap a common construct, 
despite the differences in their names. 

Participants were randomly assigned to two groups.  
All participants were given standard instructions for the 
completion of self-report measures, with one exception.  
The Taxonic Instructions group consisted of 54 participants 
(47%) who were told, “Previous studies suggest that most 
people either produce very high or very low scores on these 
types of scales.”  The Dimensional Instructions group con-
sisted of 61 participants (53%) who were told, “Previous 
studies suggest that people produce a wide range of scores 
on these types of scales, from low to medium to high.” 
 

Results 
 

When analyzed, Pearson correlation coefficients sug-
gest that the measures utilized correlate relatively well with 
one another, suggesting they are tapping a shared construct.  
The correlations between measures ranged from 0.32 to 
0.71; each was significant at p < .001 (see Table 1). 

The data from the Taxonic Instructions group and the 
Dimensional Instructions group were subjected to three 
taxometric procedures: MAMBAC, MAXEIG, and Lmode.  
Without exception, each procedure yielded results indicat-
ing that the responses given conformed to a dimensional 
structure regardless of instructional set.  Graphs generated 
from the MAMBAC and MAXEIG analyses failed to depict 
the inverted U shape characteristic of an underlying taxonic 
structure in either group.  Similarly, the Lmode graphs for 

33 



NEUBAUER AND MCGRATH 
 

Discussion Discussion both samples were unimodal, again failing to detect a taxon 
(see Figures 1 and 2).       

Each taxometric method allows for estimation of the 
taxonic base rate if a taxon exists. A high degree of vari-
ability in these estimates across analyses provides a secon-
dary piece of evidence supporting a dimensional conclu-
sion.  In the Taxonic Instructions group, the mean estimated 
base rate from the MAMBAC analyses was 0.50, while the 
mean based on the MAXEIG analyses was 0.77.  Lmode 
allows two independent estimates of taxon base rate.  In the 
taxonic instructions group, these estimates were .06 and 
1.00.  These results suggest substantial variability in esti-
mates of the taxon base rate, a finding that supports the 
conclusion that the distribution of scores in the Taxonic 
Instructions group conform to a dimensional structure.  

The results of the current study suggest that responses 
to self-report rating scales may not be as subject to manipu-
lation via instructional set as Beauchaine and Waters (2003) 
suggest.  Even when given instructions suggesting a taxonic 
style of responding, students responded in a dimensional 
manner.  The similarity in the style of responding across the 
two conditions supports the continued use of self-report 
rating scales for the purpose of assessment. 

The results of the current study suggest that responses 
to self-report rating scales may not be as subject to manipu-
lation via instructional set as Beauchaine and Waters (2003) 
suggest.  Even when given instructions suggesting a taxonic 
style of responding, students responded in a dimensional 
manner.  The similarity in the style of responding across the 
two conditions supports the continued use of self-report 
rating scales for the purpose of assessment. 

It is important to note, however, that the sample size 
available for this pilot study is insufficient to justify firm 
conclusions about the absence of taxonic structure.  Data 
continue to be gathered, and more extensive results will be 
made available at a future date. 

It is important to note, however, that the sample size 
available for this pilot study is insufficient to justify firm 
conclusions about the absence of taxonic structure.  Data 
continue to be gathered, and more extensive results will be 
made available at a future date. 

   
 

References References  
Table 1 Beauchaine, T. P., & Waters, E.  (2003).  Pseudotaxonicity 

in MAMBAC and MAXCOV analyses of rating-scale 
data: Turning continua into classes by manipulating ob-
server’s expectations.  Psychological Methods, 8, 3-15. 

Beauchaine, T. P., & Waters, E.  (2003).  Pseudotaxonicity 
in MAMBAC and MAXCOV analyses of rating-scale 
data: Turning continua into classes by manipulating ob-
server’s expectations.  Psychological Methods, 8, 3-15. 

Correlations between Pairs of Scales. 
 
 RSE  EPI EPI CSE CSE GSE GSE 

  
RSE RSE 1.00 1.00       
EPI EPI 0.32 0.32 1.00 1.00     
CSE CSE 0.71 0.71 0.51 0.51 1.00 1.00   
GSE GSE 0.71 0.71 0.48 0.48 0.70 0.70 1.00 1.00 

  
Note. RSE = Rosenberg Self-Esteem Scale; EPI = Neuroticism 
Scale of the Eysenck Personality Inventory; CSE = Core Self-
Evaluations Scale; GSE = General Self-Efficacy Scale.  All corre-
lations are significant, p < .001. 

Note. RSE = Rosenberg Self-Esteem Scale; EPI = Neuroticism 
Scale of the Eysenck Personality Inventory; CSE = Core Self-
Evaluations Scale; GSE = General Self-Efficacy Scale.  All corre-
lations are significant, p < .001. 

Cleland, C. M., Rothschild, L., & Haslam, N.  (2000).  De-
tecting latent taxa: Monte Carlo comparison of taxomet-
ric, mixture model, and clustering procedures.  Psycho-
logical Reports, 87(1), 37-47. 

Cleland, C. M., Rothschild, L., & Haslam, N.  (2000).  De-
tecting latent taxa: Monte Carlo comparison of taxomet-
ric, mixture model, and clustering procedures.  Psycho-
logical Reports, 87(1), 37-47. 

Cronbach, L. J., & Meehl, P. E.  (1955).  Construct validity 
in psychological tests.  Psychological Bulletin, 52, 281-
302. 

Cronbach, L. J., & Meehl, P. E.  (1955).  Construct validity 
in psychological tests.  Psychological Bulletin, 52, 281-
302. 

Eysenck, H. J., & Eysenck, S. B. G.  (1968).  Manual of the 
Eysenck Personality Inventory.  San Diego, CA: Educa-
tional and Industrial Testing Service. 

Eysenck, H. J., & Eysenck, S. B. G.  (1968).  Manual of the 
Eysenck Personality Inventory.  San Diego, CA: Educa-
tional and Industrial Testing Service. 

  

-3 -1 0 1 2 3 4

0.
0

0.
2

0.
4

Factor Scores

R
el

at
iv

e 
Fr

eq
ue

nc
y

LMODE CURVE
Gangestad, S. W., Bailey, J. M., & Martin, N. G.  (2000).  

Taxometric analyses of sexual orientation and gender 
identity.  Journal of Personality and Social Psychology, 
78, 1109-1121. 

Gangestad, S. W., Bailey, J. M., & Martin, N. G.  (2000).  
Taxometric analyses of sexual orientation and gender 
identity.  Journal of Personality and Social Psychology, 
78, 1109-1121. 

Haslam, N.  (1997).  Evidence that male sexual orientation 
is a matter of degree.  Journal of Personality and Social 
Psychology, 73, 862-870. 

Haslam, N.  (1997).  Evidence that male sexual orientation 
is a matter of degree.  Journal of Personality and Social 
Psychology, 73, 862-870. 

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J.  
(2002).  Are measures of self-esteem, neuroticism, locus 
of control, and generalized self-efficacy indicators of a 
common core construct?  Journal of Personality and So-
cial Psychology, 83, 693-710. 

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J.  
(2002).  Are measures of self-esteem, neuroticism, locus 
of control, and generalized self-efficacy indicators of a 
common core construct?  Journal of Personality and So-
cial Psychology, 83, 693-710. 

Figure 1.   L-mode results for Taxonic Instructions group. Figure 1.   L-mode results for Taxonic Instructions group. 

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J.  
(2003).  The Core Self-Evaluations Scale: Development 
of a measure.  Unpublished manuscript, University of 
Florida. 

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J.  
(2003).  The Core Self-Evaluations Scale: Development 
of a measure.  Unpublished manuscript, University of 
Florida. 

Figure 2.   L-mode results for Dimensional Instructions group. Figure 2.   L-mode results for Dimensional Instructions group. 

-3 -1 0 1 2 3 4

0.
0

0.
2

0.
4

Factor Scores

R
el

at
iv

e 
Fr

eq
ue

nc
y

LMODE CURVE

Meehl, P. E., & Yonce, L. J.  (1994).  Taxometric analysis: 
I. Detecting taxonicity with two quantitative indicators 
using means above and below a sliding cut (MAMBAC 
procedure).  Psychological Reports, 74, 1059-1274. 

Meehl, P. E., & Yonce, L. J.  (1994).  Taxometric analysis: 
I. Detecting taxonicity with two quantitative indicators 
using means above and below a sliding cut (MAMBAC 
procedure).  Psychological Reports, 74, 1059-1274. 

Meehl, P. E., & Yonce, L. J.  (1996).  Taxometric analysis: 
II. Detecting taxonicity using covariance of two quantita-
tive indicators in successive intervals of a third indicator 

Meehl, P. E., & Yonce, L. J.  (1996).  Taxometric analysis: 
II. Detecting taxonicity using covariance of two quantita-
tive indicators in successive intervals of a third indicator 

34 



TAXOMETRICS AND RESPONSE STYLE 
 

35 

(MAXCOV procedure).  Psychological Reports, 78, 
1091-1227. 

Rosenberg, M.  (1965).  Society and the adolescent self-
image.  Princeton, NJ: Princeton University Press. 

Ruscio, J. & Ruscio, A. M.  (2000).  Informing the continu-
ity controversy: A taxometric analysis of depression.  
Journal of Abnormal Psychology, 109, 473-487. 

Skilling, T. A., Quinsey, V. L., & Craig, W. M.  (2001).   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Evidence of a taxon underlying serious antisocial behav-
ior in boys.  Criminal Justice & Behavior, 28, 450-470. 

Tylka, T. L., & Subich, L. M.  (2003).  Revisiting the latent 
structure of eating disorders: Taxometric analyses with 
nonbehavioral indicators.  Journal of Counseling Psy-
chology, 50, 276-286. 

Waller, N. G., & Meehl, P. E.  (1998).  Multivariate 
taxometric procedures: Distinguishing types from con-
tinua.  Thousand Oaks, CA: Sage Publications, Inc. 


	A Taxometric Analysis of Experimenter-Induced Response Bias
	on Self-Report Data
	Participants
	Procedure

