M. Roth
European
et al.: Dimensionality
Journalof Psychological
and NormsAssessment
©
of2008
Rosenberg
Hogrefe
2008;’sVol.
&
Self-Esteem
Huber
24(3):190–197
Publishers
Scale
Dimensionality and Norms of the
Rosenberg Self-esteem Scale in a
German General Population Sample
Marcus Roth1, Oliver Decker2, Philipp Yorck Herzberg2, and Elmar Brähler2
1
Department of Psychology II, University of Leipzig, Germany
Department of Medical Psychology and Medical Sociology, University of Leipzig, Germany
2
Abstract. This study analyzed the dimensionality of the German version of Rosenberg’s Self-Esteem scale (RSES) in a nationally
representative population sample of 4,988 subjects (46.4% males; aged 14–92 years). Using confirmatory factor analysis, one- and
two-dimensional models were tested. Results suggest that the RSES is a two-dimensional scale comprising the highly correlated components positive and negative self-evaluation, which constitute a unitary construct of global self-esteem at the second-order level. In order
to obtain a more conclusive solution, an item response theory (IRT) analysis (partial credit model) was conducted. Results lend support
to a one-dimensional view of the RSES. Furthermore, psychometric properties and norm values based on the representative sample are
reported. Analyses revealed extremely high response probabilities for all items, as a consequence of which self-esteem cannot be differentiated at the upper end of the range.
Keywords: dimensionality, self-esteem, Rosenberg scale, psychometrics
Introduction
Despite the wide range of self-esteem measures proposed
by researchers over the years, the Rosenberg Self-Esteem
scale (RSES; Rosenberg, 1965) remains by far the most
widely used (e.g., Blascovich & Tomaka, 1991; Gray-Little, Williams, & Hancock, 1997). In a systematic review,
Gray-Little et al. (1997) concluded that the RSES is a reliable and valid measure of global self-worth and “deserves
its widespread use and continued popularity” (p. 450).
The RSES comprises five positively and five negatively worded items. The scale was conceptualized as a single-factor scale with scores ranging along a continuum
from low self-esteem to high self-esteem. According to
Rosenberg (1979), the individual with a high level of
self-esteem can be characterized as follows: “he has selfrespect, considers himself a person of worth. Appreciating his own merits, he nonetheless recognizes his faults
. . .. The term ‘low self-esteem’ . . . means that the individual lacks respect for himself, considers himself unworthy, inadequate, or otherwise seriously deficient as a
person” (p. 54).
While psychometric studies have generally supported
the one-dimensionality of the scale (e.g., Fleming &
Courtney, 1984; Mimura & Griffiths, 2007), a considerable number of studies applying exploratory or confirmatory factor analysis have provided evidence to suggest that
the scale reflects a two-dimensional construct. As a result,
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197
DOI 10.1027/1015-5759.24.3.190
two different two-facet models have been proposed. The
first differentiates between a positive and a negative image
of the self (e.g., Goldsmith, 1986; Pullmann & Allik,
2000). Studies supporting this model have found that the
five positively worded items (e.g., “I feel that I have a
number of good qualities”) load onto one factor, referred
to variously as positive self-esteem, positive self-worth,
and positive self-image, while the five negatively worded
items (e.g., “At times I feel that I am no good at all”) load
onto a separate factor, referred to as negative self-esteem,
self-derogation, self-deprecation, or negative self-image.
Some researchers have argued that the two facets reflect
different forms of self-image that are influenced by different experiences (e.g., Owens, 1994; Sheasby, Barlow, Cullen, & Wright, 2000). Other researchers have, however,
suggested that the two-dimensionality of the RSES should
be interpreted as a methodological artefact of item-wording (e.g., Greenberger, Chen, Dmitrieva, & Farraggia,
2003; Tomas & Oliver, 1999). In line with this suggestion,
positive and negative self-esteem factors might result
from respondents’ tendency to react asymmetrically to
positively and negatively keyed items. Marsh (1996) has
argued that this artefact might primarily be due to verbal
ability, since responding to negatively worded items (in
particular negating them) implies a higher level of cognitive complexity than responding to positive items. In support of this view, Marsh (1996) demonstrated that students
with lower verbal ability were especially susceptible to
providing responses to negative items that were inconsis© 2008 Hogrefe & Huber Publishers
M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
191
Table 1. Demographic characteristics of the representative population sample
Age
Age groups
Total (N = 4988)
Male (N = 2312)
Female (N = 2676)
M
48.32
48.03
48.56
SD
17.96
18.02
17.92
14–24 years
11.1%
12.8%
9.6%
25–34 years
13.7%
12.8%
14.4%
35–44 years
19.2%
17.1%
21.0%
45–54 years
17.2%
17.4%
16.9%
55–64 years
16.0%
17.1%
15.1%
65–74 years
15.4%
16.8%
14.2%
7.4%
6.0%
8.7%
≥ 75 years
Urbanicity
Educationa
Net household income
Rural area
13.3%
13.2%
13.3%
Urban area
86.7%
86.8%
86.7%
No qualifications
1.1%
0.8%
1.3%
High school
44.2%
45.2%
43.3%
O-levels/technical college
39.7%
37.1%
34.0%
A-levels
7.7%
7.7%
7.6%
University/polytechnic
6.5%
8.0%
5.2%
Currently still at school
3.5%
4.2%
2.8%
4.1%
4.3%
3.9%
750 to 1249 e/month
< 750 e/month
18.3%
14.8%
21.4%
1250 to 1999 e/month
36.1%
36.8%
35.5%
≥ 2000 e/month
41.5%
44.1%
39.2%
a
Note. O-level refers to the German “Realschulabschluss” (10 years); A-level refers to the German “Abitur” (12 or 13 years).
tent with their responses to positively worded items. Similar results were obtained by Corwyn (2000) and Dunbar,
Ford, Hunt, and Der (2000). In their analysis of verbal
competence and self-esteem in German students, however,
Collani and Herzberg (2003) failed to find support for this
theory.
In addition to the positive-negative image model, a second two-factor model of self-esteem as measured by the
RSES has been proposed. Tafarodi and colleagues (Tafarodi & Swann, 1995, 2001; Tafarodi & Milne, 2002) differentiate between the facets self-competence and self-liking.
While self-competence is defined by the authors as “the
valuative experience of oneself as a causal agent, an intentional being with efficacy and power” (Tafarodi & Swann,
p. 654), self-liking is seen to refer to “the valuative experience of oneself as a social object, a good or bad person
according to internalized criteria of worth” (p. 655). According to this definition, self-competence is closely related to Bandura’s (1977) concept of general self-efficacy,
whereas self-liking corresponds to the actual definition of
global self-esteem. The facet self-competence has been
shown to comprise items, 3, 4, 5, 7, and 9, and self-liking
items 1, 2, 6, 8, and 10 of the RSES.
Despite the controversy surrounding the number of factors that underlie Rosenberg’s scale, previous studies have
predominantly attempted to address the issue using nonrepresentative subsamples. Furthermore, with the exception of
the study by Collani and Herzberg (2003), the factorial
© 2008 Hogrefe & Huber Publishers
structure of the German version of the Rosenberg scale has
thus far hardly been subject to investigation. The central
aim of the present study was therefore to investigate the
dimensionality of the German version of the Rosenberg
Self-Esteem scale using a representative population sample. In this way, biased results due to selected populations
were avoided.
A second aim of the study was to provide data on the
psychometric properties and norm values on the basis of
the representative sample. This would appear to be highly
necessary, given the huge gulf between research and practice in applying the Rosenberg scale: Whereas the German
version of the RSES is one of the most widely used instruments in several areas of psychological research, it almost
completely fails to find application in the practical context
of individual diagnostics (see Roth & Herzberg, 2008). It
is possible that this lack of application is in part due to
missing norms, as a consequence of which individual
RSES scores cannot be interpreted.
Method
Subjects and Procedure
This study formed part of a nationally representative faceto-face household survey conducted in Germany. Study
participants were surveyed using a structured self-report
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197
192
M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
Figure 1. Measurement models for global self-esteem. RSES = Rosenberg Self-Esteem Scale; SCO = self-competence;
SLI = self-liking; POS = positive self-image; NEG = negative self-image.
questionnaire. Participants also completed a self-report
questionnaire that included Rosenberg’s Self-Esteem scale
(1965). The survey was carried out by professional interviewers from a demographic consultation company
(USUMA, Berlin). Within each wave, a representative
sample of the German population aged 14 years or older
was approached using 258 sample points. Addresses were
selected according to the random route procedure. Of the
8,398 addresses selected, 8,106 proved valid. A total of
5,036 persons agreed to participate, provided verbal informed consent, and completed the study questionnaires.
The response rate among those individuals who were asked
to participate by the interviewers was 72.9%.
In the following analysis, subjects with missing data for
the variables measuring self-esteem were excluded (n =
48). Thus, the final sample consists of 4,988 subjects aged
14 to 92 years (Table 1). The characteristics of the sample
given in Table 1 closely match those from the total German
population (Statistisches Bundesamt, 2005).
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197
Measures
The German Adaptation of Rosenberg’s Self-Esteem scale
(RSES) by Ferring and Filipp (1996) was administered.
The RSES as described above is composed of five positively and five negatively worded items. Subjects are asked to
indicate the extent to which the items describe them, using
a 6-point Likert scale from “1” = strongly disagree to “6”
= strongly agree.
Results
Confirmatory Factor Analysis (CFA)
The three models of global self-esteem (see Figure 1) described in the introduction section above were tested via
confirmatory factor analysis using AMOS 6.0 (Arbuckle,
© 2008 Hogrefe & Huber Publishers
M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
193
Table 2. Summary of the fit indices for the estimated models of the German Adaptation of Rosenberg’s Self-Esteem Scale
(left: random sample 1, n = 2499/right: random sample 2, n = 2489)
Modela
χ²
χ²S–B
df
GFI
NFI
TLI
CFI
RMSEA
RMSEA (90% CI)
1
1540.28/1531.70
1007.74/944.97
35
.86/.86
.86/.85
.82/.81
.86/.85
.13/.13
.13–.14/.13–.14
2
1529.08/1527.67
820.59/869.68
34
.86/.86
.86/.85
.82/.80
.86/.85
.13/.14
.13–.14/.13–.15
3
464.06/460.21
296.19/287.60
34
.96/.96
.96/.95
.95/.94
.96/.96
.07/.07
.07–.08/.07–.08
a
Note. See Figure 1. GFI = goodness-of-fit index; NFI = normed fit index; TLI = Tucker Lewis index (nonnormed fit index); CFI = comparative
fit index; RMSEA = root mean square error of approximation; 90% CI = limits of the 90%-confidence interval for RMSEA. In all cases, p <
.001. χ²S–B: Satorra-Bentler chi-square.
Table 3. Summary of the fit indices for the estimated models of Rosenberg’s Self-Esteem Scale in 25- to 44-year-old
Germans with the lowest educational degree (low; n = 471) and the highest educational degree (high; n = 326)
Modela
χ²
df
GFI
NFI
TLI
CFI
RMSEA
1
Low
High
385.21
203.53
35
.81
.87
.81
.85
.77
.83
.82
.87
.15
.12
.13–.16
.11–.14
2
Low
High
383.88
303.21
34
.81
.87
.81
.84
.77
.82
.83
.86
.15
.12
.14–.16
.11–.14
3
Low
101.77
34
.95
.97
.96
.97
.06
.05–.08
High
102.08
.95
.93
.93
.95
.08
.06–.10
Note. aSee Figure 1. GFI = goodness-of-fit index; NFI = normed fit index; TLI = Tucker Lewis index (nonnormed fit index); CFI = comparative
fit index; RMSEA = root mean square error of approximation; 90% CI = limits of the 90% confidence interval for RMSEA. In all cases p <
.001.
2005). Model 1 represents the one-dimensional conception of global self-esteem (e.g., Rosenberg, 1965, 1979),
with all 10 items defined as indicators of a single factor.
Model 2 depicts the two-factor model proposed by Tafarodi and Milne (2002), with two latent variables (selfcompetence and self-liking) and five measured variables
loading onto each. Model 3 represents the claim that the
two poles of self-esteem represent substantively distinct
dimensions (e.g., Pullmann & Allik, 2000), with the five
positively worded items defined as indicators of a positive
self-esteem facet (positive self-image) and the five negatively worded items as indicators of a negative self-esteem
facet (negative self-image). Error covariances were constrained to zero in all models, in order to avoid opportunistic fitting. For the purpose of confirming the goodness
of fit criteria for the models, the sample were randomly
split into two halves, and analysis were conducted separately for each half using the maximum likelihood (ML)
method of estimation.
As shown in Table 2, Model 1 and Model 2 both demonstrated unsatisfactory fit indices (Tucker-Lewis Index
< .90 and root mean square error of approximation > .08).
In contrast, the fit indices associated with Model 3 indicated a reasonable model fit (correlation between POS
and NEG: r = –.49 res. r = –.50). This was supported by
an evaluation of the differences in fit between the two
models: A χ² difference test comparing Model 1 and
Model 3 revealed that Model 3 was superior (∆χ2 =
1076.22/1071.49, df = 1, p < .001). A χ² difference test
comparing Model 2 and Model 3 was not admissible given that these models were not nested. Because the multi© 2008 Hogrefe & Huber Publishers
variate normality assumption of the ML estimation method was not met by the data, all analyses were consequently confirmed by applying a Satorra-Bentler χ² correction
for nonnormality using EQS 5.7. Results yielded using
this method did not produce conclusions which deviated
from those obtained using ML estimation (see Table 2).
Finally, we explored the contribution of verbal ability
method effects as reported by Marsh (1996). Here, education level was used as a proxy for verbal ability of the
participants.
While an optimal analysis would include all six educational levels presented in Table 1, we omitted individuals
with no qualification due to the small sample size. For
space-saving reasons and given that results based on an
analysis of all five groups did not significantly deviate, we
report results for the two extreme education groups only.
These two extreme groups were formed as follows: The
first comprised individuals with the lowest possible level
of school education (8–9 years of completed school education), and the second those with the highest level of school
education obtainable in Germany (“A”-level). Since age
and education level are confounded (with lower education
levels occurring in higher age groups), we only included
subjects between the ages of 25 and 44 years in our analyses. As shown in Table 3, the two-factor model distinguishing between a positive and negative facet of self-esteem
(Model 3) was once again the only model to provide an
adequate model fit in both subsamples. For participants
with the lowest level of education, correspondence between the latent variables POS and NEG was r = –.50, and
r = –.54 for subjects with the highest level of education.
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M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
Item Response Theory Analysis (IRT)
While factor analyses, whether exploratory or confirmatory
in nature, represent the most common technique in evaluating the dimensionality of questionnaires, these approaches do not constitute an exhaustive analysis at the item-level.
An IRT-analysis can provide useful information about a
questionnaire, in particular when the results of preceding
factor analyses are ambiguous (e.g., Herzberg, 2002). More
specifically, the misfit of the one-dimensional model for
the RSES may result from the fact that five items are keyed
in the positive direction and five in the negative direction.
This results in dissimilar item distributions, which in turn
can lead to spurious factors based on a similarity of the item
distribution rather than the content. Since CFA approaches
make normal curve assumptions and the RSES is plagued
with a bimodal item distribution, it is therefore prudent to
use full information techniques that can handle bimodal
item distributions. IRT overcomes this limitation and allows the one-dimensionality of questionnaires to be tested
free from item bias (van der Linden & Hambleton, 1997).
The 10 items were analyzed using the Partial Credit
Model (Masters, 1982) as implemented in the computer
program LpcM-WiN 1.0 (Fischer & Ponocny-Seliger,
1998). This model assumes that the items measure a onedimensional latent trait and that the response categories are
ordered, while item difficulties are allowed to differ between items. We used sex as an external criterion to split
the sample for testing of statistical fit, and, thus, examined
whether parameter estimates for RSES items were invariant across gender groups. LpcM-WiN provides several
measures of statistical fit. First, an inspection of the graphical goodness of fit test (Figure 2) revealed that none of the
items conspicuously deviated from the 45°-line. Second,
the global model fit with χ² = 61.09 (df = 49, p > .05) was
nonsignificant, indicating homogeneity of the 10 items
across males and females. Third, the Pearson correlation
between parameters was .97, also indicating homogeneity
of the 10 items across gender groups. Finally, beyond the
overall model fit, the one-dimensionality of the items is
supported by z-statistics, which pertain to the single item.
This z value is an approximate asymptotically normal
statistic for testing differential item functioning (DIF) of a
single item, based on a bipartitioning of the total testee
sample into two disjoint samples. With the exception of two
significant z statistics (z > 2.0) for Item 9 (categories 3 and
5, respectively), the remaining z statistics ranged between
–1.91 and 1.87 and were, therefore, not significant at the
5% level. The Rating-Scale Model did not fit the data, indicating that categories were ordered but not equidistant.
Figure 2. Graphical goodness of fit test based on the sample
split by sex. Note: Each data point represents an item category, while the category itself is not depicted. Of importance is the deviation of the points from the 45° line.
negative self-evaluation) were tested using multivariate
analyses of variance with sex and age (graded sevenfold in
correspondence with the ranges depicted in Table 1) as independent variables. Due to the large sample size used in
the present study, we also calculated the effect size ηsquared for the purpose of determining the magnitude of
differences between means. In order to avoid an overestimation of significant results, it would seem reasonable to
primarily rely on this value in large samples.
Multivariate tests revealed a main effect of age (F =
6.52, p < .001, η² = .01), but no main effect of sex (F =
0.52, p = .597, η² = .00), and no sex by age interaction effect
(F = 1.12, p = .111, η² = .00). Univariate tests indicated
main effects of age on the total RSES scale (F = 3.38, p =
.002, η² = .01) and on the positive SE component subscale
(F = 8.172, p < .001, η² = .01), but not on the negative
SE-component subscale (F = 1.63, p = .135, η² = .00). Duncan’s multiple post hoc analyses of the means indicated that
the lowest RSES scores emerged in the age groups 55–64
years and > 75 years, and the highest scores in the age
groups 25–34 years and 35–44 years. With respect to the
positive SE-component, the lowest scores were again found
in the age groups 55–64 years and 75years and the highest
scores in the 25–34 years age group. Age differences were,
however, found to be very small (accounting for a maximum of 1% of the variance in self-esteem).
Age and Sex Differences
Psychometric Properties
Age and sex differences in the RSES scale as well as in the
subscales (representing the two components positive and
Item and scale characteristics were evaluated on the basis
of the total sample (N = 4988). Based on the results con-
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197
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M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
195
Table 4. Psychometric properties of the German adaptation of Rosenberg’s Self-Esteem Scale (RSES; N = 4,988)
Scale/item
M
SD
α
ris
rit
p
Frequency of scores (%)
1
2
3
4
5
6
RSES-pos
4.82
0.82
.81
–
–
–
–
–
–
–
–
–
Item01
4.75
1.11
–
.56
.60
.79
1.3
3.5
8.0
20.2
40.6
26.5
Item03
4.87
0.95
–
.64
.56
.81
0.4
1.7
5.2
22.8
42.6
27.3
Item04
4.86
1.06
–
.56
.49
.81
0.6
2.2
7.3
23.0
34.5
32.5
Item07
4.89
1.18
–
.61
.57
.82
1.5
2.7
8.4
19.1
29.1
39.2
Item10
4.72
1.15
–
.60
.62
.79
1.3
3.2
9.1
23.2
34.2
28.9
RSES-neg
5.02
1.02
.86
–
–
–
–
–
–
–
–
–
Item02*
5.08
1.26
–
.70
.67
.85
1.2
4.8
8.0
11.1
20.6
54.2
Item05*
4.85
1.32
–
.64
.63
.81
1.7
5.3
11.1
13.0
25.8
43.1
Item06*
4.96
1.34
–
.71
.67
.83
2.0
5.2
10.0
11.4
20.9
50.7
Item08*
4.84
1.32
–
.64
.61
.81
1.4
5.7
11.4
14.4
23.3
43.8
Item09*
5.35
1.09
–
.72
.69
.89
0.6
2.2
6.7
8.3
16.0
66.2
RSES-tot
4.92
0.82
.88
–
–
–
–
–
–
–
–
–
Note. M = mean; SD = standard deviation; RSES-pos = positive subscale of the RSES; RSES-neg = negative subscale of the RSES (reversed
pooled); RSES-tot = total score of the RSES; α = Cronbach’s α; ris = part-whole corrected item-total correlation (related to the subscale); rit =
part-whole corrected item-total correlation (related to the total score); p = response probability. *Negative items were recoded prior to analyses.
Table 5. Normative data from the German general population (N = 4988) for the Rosenberg Self-Esteem
Scale (sum score)
Sum
T
Sum
T
Sum
T
Sum
T
<20
<14
30
27
41
40
52
53
20
15
31
28
42
41
53
55
21
16
32
29
43
42
54
56
22
17
33
30
44
44
55
57
23
18
34
32
45
45
56
58
24
19
35
33
46
46
57
60
25
21
36
34
47
47
58
61
26
22
37
35
48
49
59
62
27
23
38
36
49
50
60
63
28
24
39
38
50
51
29
25
40
39
51
52
Note. Sum = Raw score; T = T-score (mean = 50; standard deviation
= 10).
cerning the internal structure of the scale, we evaluated the
total score of the RSES as well as scores for the subscales
representing the positive and negative evaluation components of self-esteem. As shown in Table 4, internal consistencies (Cronbach’s α) were satisfactory for the RSES scale
as well as for the two subscales (α > .80). Item-total correlations were in the upper range (r > .50).
Finally, with the aim of enabling the application of the
RSES in the context of individual diagnostics, Table 5 presents normative data for the RSES score. Given negligible
sex and age differences, we calculated norms based exclusively on the total sample. Standardized T-scores from this
table can be used to compare an individual’s RSES score
with scores from the general population reference group.
© 2008 Hogrefe & Huber Publishers
Discussion
In the present study, Ferring and Filipp’s (1996) German
version of the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) was administered to a representative population-based sample of 4988 subjects aged between 14 and
92 years with the aim of exploring dimensionality and psychometric properties of the scale. In line with previous
studies using the German Version in selected samples (e.g.,
Collani & Herzberg 2003; Ferring & Filipp, 1996), satisfactory reliabilities were found. Together with the norm
values reported in our paper, this instrument can, thus, be
employed to measure global self-esteem in the practical
context of individual diagnostics.
With respect to the dimensionality of the RSES, the CFA
results of our study clearly indicate a two-factor structure.
As shown by confirmatory factor analyses, only the model
in which positive and negative items load onto separate
factors, which in turn constitute global self-esteem on a
higher order level (referred to as Model 3 in Figure 1),
yielded an adequate model fit. Contrary to the findings of
Marsh (1996), and Collani and Herzberg (2003), we did not
find any evidence to suggest that this dimensionality is an
artefact of verbal ability. Indeed, the two-factor structure
emerged in participants with a low level of education as
well as in those with a high level of education. This is in
line with results reported by Greenberger et al. (2003), who
also used only a proxy indicator of verbal ability (grade and
generational status, i.e., first-generation immigrants vs.
higher generation immigrants). Nevertheless, even if proxy
indicators of verbal ability do not contribute to explaining
the structure of the scale, we still assume that the two-dimensionality comprising a positive and a negative self-image factor represents a methodological artefact of the two
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197
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M. Roth et al.: Dimensionality and Norms of Rosenberg’s Self-Esteem Scale
types of item-wording used in the scale. An inspection of
the different answer distributions for positively and negatively worded items supports this assumption. As shown in
Table 4, the most frequent response to positively worded
items was 5, whereas negatively worded items were predominantly responded to with 1. Participants, thus, tended
to disagree with or negate negatively worded items more
strongly than they tended to agree with positively worded
items.
In order to rule out that the two factor solution was
caused by the bimodal item distribution, we conducted an
IRT analysis. IRT analyses are not based on covariance matrices and are, therefore, not biased by item distribution
artefacts. Results of the IRT analyses support a one-dimensional structure of the RSES.
The assumption of an artefact of item-wording also corresponds with the findings of Greenberger et al. (2003). In
their study, a clear one-dimensional structure emerged
when the wording of RSES items was altered in such a way
that all items were worded in a consistent direction, regardless of whether the wording was in a consistently positive
or negative direction. Furthermore, the authors were able
to demonstrate that the mean scores for the differently
worded versions of the RSES did not significantly differ,
and that the wording of the items did not seem to affect the
magnitude of correlations between the RSES and validityrelated measures (for two exceptions see Greenberger et al.,
2003). In conclusion, it therefore seems reasonable to use
the global score in analyzing individuals’ self-esteem rather than measuring the two components separately.
Although previous findings found both age and sex differences in self-esteem (e.g., Kling, Hyde, Showers, &
Buswell, 1999; Robins, Trzesniewski, Tracy, Gosling, &
Potter, 2002), age differences in our study were very small,
and gender differences or an age by gender interaction did
not occur1. The norms provided are therefore valid for
males and females as well as for all age groups.
The distribution of item scores leads us to the most serious problem in applying the RSES to measure global selfesteem. As the psychometric properties of the items show,
response probabilities were far too high, in particular when
considering that such high probabilities (p = .79–.89) were
found for all items. A clear ceiling-effect was found with
respect to the distribution of self-esteem scores. The RSES,
thus, only enables the measurement of self-esteem in the
low to medium range, whereas differentiations at the upper
end of the range are not possible. This, unfortunately,
means that the RSES is not an adequate instrument when
it comes to measuring self-esteem at extreme levels associated with, for example, overestimation and narcissistic
tendencies (for a summary see Schütz, 2000). The inclusion
of items with lower response-probabilities in the scale
1
would therefore seem necessary in order to avoid ceilingeffects and enable the identification of subjects with extremely high levels of self-esteem.
Furthermore, future efforts should focus on extending
the external validity of the Rosenberg Self-Esteem scale by
investigating the usefulness of the norms provided. Norms
may, for instance, enable researchers to conduct clinical
single case studies or to evaluate the effects of treatment
programs.
Acknowledgments
The study was funded by the Friederich-Ebert-Stiftung,
Germany.
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PD Dr. Marcus Roth
University of Leipzig
Department of Psychology II
Seeburgstraße 14–20
D-04103 Leipzig
Germany
Tel. +49 341 973-5918
Fax +49 341 973-5909
E-mail mroth@uni-leipzig.de
European Journal of Psychological Assessment 2008; Vol. 24(3):190–197