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International Journal
of
Behavioral Development
2002,
26
(2), 154
-
166
http://www.tandf.co.uWjournals/pp/O1650254.html
6
2002
The International Society
for
the
Study
of
Behavioural Development
DOI:
10.1
OSO/O
1650250042000645
New tools, new insights:
Kohlberg’s moral judgement stages revisited
The0
Linda Dawson
University
of
California
at
Berkeley, USA
In
this paper, four sets of data, collected by four different research teams over a period of 30 years are
examined. Common item equating, which yielded correlations from .94 to .97 across datasets, was
employed to justify pooling the data for
a
new analysis. Probabilistic conjoint measurement (Rasch
analysis) was used to model the results. The detailed analysis of these pooled data confirms results
reported in previous research about the ordered acquisition of moral stages and the relationship
between moral stages and age, education, and
sex.
New findings include:
(1)
empirical evidence that
transitions between “childhood” and “adult” stages of development involve similar mechanisms;
(2)
support
for
the notion of stages as qualitatively distinct modes of reasoning that display properties
consistent with a notion of
structure
d’ensemble;
and
(3)
evidence
of
a stage between Kohlberg’s stages
3
and
4.
Consistent with reports from earlier research, the relationship between age and moral
development is curvilinear. The relationship between educational attainment and moral development
is linear, suggesting that educational environments have an equivalent impact
across
the course
of
development. Older males have slightly higher scores than older females after age and education are
taken into account (accounting for 0.3% of the variance in moral ability).
During the 1970s and 1980s researchers applied Piagetian
principles to the study of reasoning outside the
logicomathe-
matical domain (for examples, see Armon, 1984; Kegan, 1982;
Selman,
1980a). Much of this research was inspired by
Kohlberg’s seminal work
(summarised in Colby
&
Kohlberg,
1987a) on the development of moral judgement. Although this
research of Kohlberg and his colleagues generally supported:
(1) the ordered acquisition
of
moral stages as defined in his
sequence (Armon
&
Dawson, 1997; Nisan
&
Kohlberg, 1982;
Snarey, Reimer,
&
Kohlberg, 1985; Walker, 1982); and (2) the
absence of statistically significant reversals in the direction of
development over time (Armon
&
Dawson, 1997; Nisan
&
Kohlberg, 1982; Snarey et al., 1985; Walker, 1982), postulates
of
(3)
structured
wholeness
1
-
a global tendency for individuals to
employ
a
single organisational structure to reasoning in the
moral domain
-
and (4)
universality
were not as uniformly
supported.
Ordered acquisition and a lack
of
reversals in moral
development have been demonstrated employing both long
-
itudinal and cross
-
sectional methods. The longitudinal evi
-
dence is compelling. The predicted sequence of stage
acquisition with no stage
-
skipping and no statistically signifi
-
cant reversals were found in Kohlberg’s original longitudinal
study
of
New England schoolboys (Colby
&
Kohlberg, 1987a),
’
The terms “structured whole” and
“structure
d’ensemble”
are used here to
refer to continuity
of
reasoning within the moral domain. For
a
discussion
of
global versus domain
-
specific interpretations
of
structure
d’ensemble,
see Lourenco
and Machado
(1996),
Smith
(1993), Vyuk
(1981).
in
Walker’s longitudinal study of Canadian children and their
parents
(1989), in Nisan’s and Kohlberg’s (1982) longitudinal
study
of
city and country dwelling Turkish children, and in
Snarey’s longitudinal study of Israeli kibbutz residents (Snarey
et
al.,
1985). In Armon’s lifespan longitudinal study of middle
class Americans (1984; Armon
&
Dawson, 1997) the only
statistically significant reversal
(i
stage) occurred in a 72
-
year
-
old respondent.
An
additional, though weaker, source of evidence for the
sequential acquisition
of
moral judgement stages is the
relationship between moral stage and age. Age and moral
stage are strongly correlated in childhood and adolescence. For
example, Armon and Dawson (1997) report that through
adolescence the relationship between age and moral stage is
linear
(r
=
.88). However, this relationship weakens in early
and middle adulthood
(r
=
.61)
Strong correlations between educational attainment and
stage also provide support for the sequentiality of moral
judgement stages. According to Kohlberg
(1969), an impor
-
tant prerequisite of moral development is direct and repeated
experience with moral conflict
in
social contexts. Formal
education has been identified as a potential source of this kind
of
sociomoral experience, and several researchers have
reported
a
moderate to strong positive relationship between
educational attainment and stage of moral reasoning
(e.g.,
Armon, 1984; Colby
&
Kohlberg, 1987a; Markoulis, 1989;
Walker, 1986). The distribution of educational attainment by
moral stage is linear and fan
-
shaped (Armon
&
Dawson,
1997), indicating that this relationship, like the relationship
Correspondence should be addressed to Dr The0
L.
Dawson, introducing me to the Rasch model, to Mark Wilson for teaching me
University of California at Berkeley, Graduate School of Education, how to put it to work, and to Mark Wilson, Cheryl Armon, Karen
CD, Tolman Hall, Berkeley, CA 94720
-
1670, USA. Draney,
W.P.
Fisher, and three anonymous reviewers for their critical
The author wishes to thank
Larry
Walker, Cheryl
Armon,
and remarks on earlier drafts of this paper. The project was supported, in
Michael Commons for the use of their data. Thanks also to
Ann
Colby part, by a grant from the Spencer Foundation. The data presented, the
and the Murray Research Center at Radcliff College, for the use of statements made, and the views expressed are solely the responsibility
Kohlberg’s data. Appreciation is
also
due to Trevor Bond for of the author.
155
INTERNATIONAL JOURNAL
OF
BEHAVIORAL DEVELOPMENT,
2002,
26
(2),
154
-
166
between age and stage, becomes less deterministic as the
number of years of education increases. However, the relation
-
ship between educational attainment and moral stage can be
described as linear rather than curvilinear, as is the case with
age and moral stage.
The notion
of
structured wholeness
(Piaget’s
structure
d’ensemble)
suffered when individual performances within
and across the six issues in the Standard Issue Scoring
Manual (SISM) (Colby
&
Kohlberg, 1987b) were frequently
found to span more than two stages (Fischer
&
Bidell, 1998).
Similarly, although cross
-
cultural studies generally supported
invariant sequence and the absence of reversals
(e.g., Nisan
&
Kohlberg, 1982; Snarey et
al.,
1985), claims of universality
were comprised when notable differences across cultures were
found in both conceptual content and highest stage attain
-
ment (Nisan
&
Kohlberg, 1982; Snarey et
al.,
1985). These
cultural differences are particularly troubling in the light
of
two
features of Kohlberg’s method and theory:
(1)
the stages
are partially defined in terms of particular philosophical
content; and (2) each successive stage is considered not only
to be more differentiated and integrated, but more philoso
-
phically adequate than any preceding stage (for a critique, see
Puka, 1991). Gilligan’s (1982) claim that men’s moral
reasoning is privileged over women’s
in
Kohlberg’s system,
dealt a serious blow to cognitive developmental research in
the moral domain, despite considerable evidence, including
results presented here, that moral stage scores for women and
men are distributed similarly once educational attainment has
been taken into account (Armon
&
Dawson, 1997; Walker,
1984).
One originally unanticipated finding from moral develop
-
ment research employing the Kohlberg’s instrument is that
moral development continues into adulthood (Armon
&
Dawson, 1997; Colby
&
Kohlberg, 1987a; Nucci
&
Pascarella,
1987). In fact, an originally unanticipated finding from
research employing Kohlberg’s Standard Issue Scoring System
(SISS),
is
that the highest stages of moral reasoning do not
generally appear until well into adulthood. Two independently
conducted longitudinal studies, Kohlberg’s original 20
-
year
study of approximately 60 males (Colby
&
Kohlberg, 1987a),
and Armon’s 12
-
year lifespan study of 43 respondents, ranging
in age from
5
to 86 (Armon
&
Dawson, 1997), provide
compelling evidence for “adult” moral reasoning stages. Adult
forms of reasoning have also been identified
in
other howl
-
edge domains (Armon, 1984, 1993; Dawson, 1998; King
&
Kitchener, 1994). The highest measured stages
of
moral
reasoning, stages 4 (consolidated formal operations) and
5
(post
-
formal operations), are rarely identified in the perfor
-
mances
of
individuals without some post
-
secondary education.
Walker
(1986), Markoulis (1989), and Armon (1984) found
stage 4 reasoning only among adults who had obtained some
college education, and in Armon’s (1984) and Kohlberg and
colleague’s (Colby
&
Kohlberg, 1987a; Kohlberg
&
Higgins,
1984) studies, stage
5
performances were found only in
individuals with
at
least some graduate work. Nucci and
Pascarella (1987) report similar findings in their review
of
research on the relationship between college and the develop
-
ment of moral reasoning.
The discovery of “adult” stages raises the question of
whether stage transitions during childhood are analytically and
empirically analogous to stage transitions in adulthood. In
other words, are adulthood stages, particularly, the “post
-
conventional” or “postformal” stage,
5
,
really stages?
Although the present project does not address the analytical
question (for this, see Commons, Trudeau, Stein, Richards,
&
Krause, 1998), the modelling methods employed here permit
exploration of the empirical question by examining: (1) the
unidimensionality of the latent trait, moral stage; and (2) the
pattern of stage transitions along the moral development
continuum.
The present project has been undertaken in an effort to
readdress some of the issues outlined here by pooling and
reanalysing the data from four Kohlbergian studies, Kohlberg’s
(Colby
&
Kohlberg, 1987a) study of schoolboys; Armon’s
(Armon
&
Dawson, 1997) lifespan study; Commons’ (Com
-
mons et
al.,
1989a) study of MENSA members; and Walker’s
(1989) longitudinal study of schoolchildren and their parents.
In
a
departure from meta
-
analytic techniques,
I
employ
probabilistic conjoint measurement models (for an overview,
see Kingma
&
Van den Boss, 1988), demonstrating that all
four of these studies assess the same dimension of ability
(moral stage) to an extent that justifies combining their data for
further analysis. Then, using related psychometric techniques,
these data are examined for evidence
of
invariant stage
sequence,
structure d’ensemble,
unidimensionality, and educa
-
tion, age, and sex effects. Pooling the data not only increases
the statistical power for analyses, but provides a lifespan
dataset from
a
broad population with few age gaps. This makes
the overall model of moral development presented here more
compelling and lends additional credence to earlier evidence
about the relationship of moral stage to age, education, and
sex.
The intention here is to explore the extent to which
results from studies employing Kohlberg’s instrument sup
-
port the postulates of his theory, and to re
-
examine
relationships between moral reasoning stage and age, sex,
and educational attainment. It is not an attempt to resurrect
the Kohlbergian research enterprise. This examination
reveals flaws in the SISS as well as strengths. The major
difference between this analysis and meta
-
analysis is that
here we return to the original data, employing sophisticated
modelling tools that were unavailable when these studies
were conducted. This makes
it
possible to look at the data
from new and revealing perspectives.
Method
Data
The pooled dataset consists of 996 estimable cases, comprising
620 males and 376 females between the ages of
5
and 86
(A4
=
32, SD
=
16). Educational attainment is between
0
and 21
years
(M
=
13, SD
=
5).
Some educational attainment and age
data are missing. Participants are predominantly Caucasian
and middle class.
The data for all of these studies were collected and analysed
according to criteria in the
Standard Issue Scoring Manual
(Colby
&
Kohlberg, 1987a, b). Within these guidelines,
however, the method
of
data collection differed across studies.
Original data for Kohlberg’s,
Armon’s, and Walker’s studies
were predominantly from live, audiotaped, and transcribed
interviews, whereas data for Commons’ study were written.
Kohlberg, Commons, Walker, and Armon supervised the
scoring
of
all interviews from their respective projects.
Participants in the Kohlberg, Commons, Walker, and Armon
156
DAWSON
I
KOHLBERG‘S
MORAL
JUDGEMENT STAGES REVISITED
Table
1
Age range, interview formats, and coders across
four
studies
of
the
development
of
moral reasoning and evaluative reasoning about the
good
Age
range
Fom
of
of
sample Administration Coder
~ ~~
Armon
5
-
86 Live interview
Armon
(n
=
147)
Commons
18
-
83 Written
Armon
(n
=
149)
Walker
6
-
53
Live
interview
Walker
(n
=
472)
Kohlberg
10
-
36
Live
interview Kohlberg
(n
=
196)
studies were New England schoolboys, adult MENSA
members, Canadian churchgoers and their children, and a
convenience sample of predominantly middle class Americans,
respectively. The age range of participants also differed across
studies. A summary of the similarities and differences in data
collection is shown in Table 1.
An
additional difference between studies is that Kohlberg’s,
Armon’s, and Walker’s are longitudinal while Commons’ is
not.’ Kohlberg’s sample was tested on six different occasions at
4
-
year intervals. Armon’s sample was tested on four different
occasions at 4
-
year intervals, and Walker’s sample was tested
on two different occasions at 2
-
year intervals. All of the
analyses in this report are conducted on the pooled long
-
itudinal and cross
-
sectional data. When test times are
separated by relatively long intervals, problems with indepen
-
dence and sample
-
size overestimation that can be introduced
with this practice are avoided (Willett, 1989). The
ns
reported
above and in the remainder of this paper, unless otherwise
indicated, include each respondent at each test time. In order
to eliminate concerns about the possible introduction of error
with this approach, all analyses were also run separately on the
data for each test time. The trends found at each test time were
consistent with the trends reported for the pooled sample, with
no exceptions.
In all of the studies, subjects were scored for their stage of
performance in up to six categories of moral judgement (also
referred to as issues or items): (1) life;
(2)
law;
(3)
conscience;
(4)
punishment;
(5)
contract; and (6) a~thority.~
The range of scores includes
1
.O,
1.5,
2.0,
2.5,
3.0,
3.5,
4.0,
4.5, and
5.0,
each of which represents
a
stage or half
-
stage in
Kohlberg’s scheme. Half
-
stage scores can come about in two
ways: (1) they can represent a mix of performances at
adjacent stages; or (2) they can be scored as transitional by
employing criteria in the scoring manual. Some subjects
received scores on only a subset of issues. Moral judgement
interviews are structured around the judgements and justifi
-
cations that are spontaneously generated by participants in
response to moral dilemmas and
a
series of structured probe
We are presently examining the longitudinal results of the combined data
from Armon’s and Kohlberg’s studies with
a
hierarchical linear modelling
approach.
The method for obtaining these scores requires the calculation
of
a
weighted average score from all performances on a particular moral issue in an
interview. I have chosen to use these weighted average scores rather than the raw
scores, because the latter are unavailable in some cases.
questions about life, law, conscience, punishment, contract,
and authority issues as they relate to these questions. The
content of any given interview may or may not address all of
the moral issues, and probe questions vary somewhat,
depending on the responses of participants. Because of this,
and because there are no apparent patterns in the distribution
of missing responses, absent responses are treated as missing
at random.
Analyses
A
procedure from psychometrics, called
common item equating
(Kelderman, 1986), makes it possible to examine whether an
individual instrument performs similarly across studies. If the
instrument functions consistently, data from multiple studies
can be pooled and analysed in
a
common frame of reference.
Fortunately, many developmental studies use the same
instruments to assess developmental level. The body of
research in which the development of moral judgement has
been assessed with Kohlberg’s Moral Judgment Interview
(MJI; Colby
&
Kohlberg, 1987a,b) is a case in point.
At least four potential problems arise when data from
several developmental studies are pooled into a single analysis.
First, the samples may not be from the same population;
second, raters may not score similarly enough; third, the
instrument may not be administered in the same way; and
fourth, different portions of an instrument may be used across
studies, resulting in blocks of missing data. These problems are
addressed by Rasch’s models for measurement (Andrich,
1988; Rasch,
1980), most commonly applied in educational
and psychological testing. These models can be used to
evaluate sample and rater effects and are robust with respect
to missing data, although measurement error is reduced and
estimate precision enhanced by more complete data. A primary
requirement of these methods, when applied to the context of
pooling results across studies, is that all respondents (within
and across samples) are tested on
at
least
a
subset of common
items; thus the term,
“common item equating”.
In the case of the
MJI, each respondent must have received a stage score on
at
least one of six moral issues.
Although they are well known in psychometric circles,
Rasch’s models for measurement have been employed by
cognitive developmentalists only recently (Andrich
&
Styles,
1994; Bond, 1994; Bond
&
Bunting, 1995; Dawson, 1998,
2000;
Draney, 1996; Hautamaki, 1989; Muller, Sokol,
&
Overton, 1999; Noelting, Coude,
&
Rousseau, 1995; Wilson,
1989). One area of application for these models is the
examination of behaviour on measures intended to capture
hierarchies of difficulty, which makes them highly suitable for
developmental applications. Rasch’s models test the extent to
which data meet the requirement that performances and items
(or levels of items) form an invariant hierarchical sequence
(within probabilistic constraints) along
a
single continuum
(Andrich, 1989).
In their raw ordinal form, little can be said about the
amount of difficulty associated with transitions between stage
scores. However, when participants are ordered by the
likelihood that they will perform
at
a given stage, the persons
whose raw scores are high will be closer to the top of the
developmental continuum, and the persons whose raw scores
are lower will be closer to the bottom of the continuum.
Rasch’s models convert these likelihoods into distinct quanti
-
tative estimates
of:
(1) item difficulty; and (2) person ability,
157
INTERNATIONAL JOURNAL
OF
BEHAVIORAL DEVELOPMENT, 2002, 26
(Z),
154
-
1
66
expressed in the same equal
-
interval metric, giving meaning to
the distances between estimates. The common metric along
which both stage difficulty and respondent ability estimates are
arranged is referred to as
a
logit scale, in reference to the log
-
odds unit employed (Wright
&
Masters, 1982). In the analyses
presented here, the mean item difficulty is set at
0.
The logit
range is from
-
7
to
8.
The distance between logits has
a
probabilistic meaning. In
the present case, an ability estimate for a given individual
means that the probability of that individual performing
accurately on an item
at
the same level is
50%.
There
is
a
73%
probability that the same individual will perform
accurately on an item whose difficulty estimate
is
one logit
easier, an
88%
probability that he/she will perform accurately
on an item whose difficulty estimate is two logits easier, and a
95% probability that
he/she will perform accurately on an item
whose difficulty estimate is three logits easier. The same
relationships apply, only in reverse, for items that are one,
two,
and three logits harder. (For more on Rasch’s models, see
Andrich, 1988; Masters, 1982.)
The logit estimates of item difficulty and person ability
are but one of the statistics essential to measurement.
Reliability and validity assessments require: (1)
that
item and
person ability estimates be associated with an error term,
which makes it possible to establish confidence intervals for
all item and person ability estimates; and (2) one or more
model
fit
statistics,
so
both items and persons can be
examined for their conformity with the requirements of the
model. Two types of
fit
statistics are included in the
following analysis, outfit and infit.
Fit
statistics are used to
assess whether a given performance (or item) is consistent
with other performances (or items). They are based on the
difference between observed and expected performances.
Outfit statistics are based solely on the difference between
observed and expected scores. In calculating infit statistics,
however, extreme persons or items are downweighted.
In
most applications, the weighted infit statistics are more
useful for assessing
fit,
because they are not affected by
outliers.
Infits (or outfits) near
I
are desirable. $-Values are
calculated to assess the significance of both positive and
negative divergences from
I.
Interpretation of fit statistics is
demonstrated below, in the results
of
the analysis.
The
partial credit model
(Masters, 1982, 1994)) designed for
items with more than two hierarchical categories, is employed
here. Analyses were conducted with the computer program,
Quest (Adams
&
Khoo,
1993). In keeping with the original
formulation of the Rasch model, Quest treats person para
-
meters as fixed effects.
It
has been argued that this limitation of
the model restricts the generalisability of the results of Rasch
analyses (Bartholomew
&
Knott, 1999), although the specific
implications for research of the present kind are not entirely
clear due to an apparent lack of published scholarly debate on
this issue. Moreover, several researchers employ Quest and
other software that treats person parameters as fixed effects to
explore developmental constructs similar to those examined
here
(e.g., Bergan, 1988; Muller et
al.,
1999). In any case,
concerns about generalisability are minimised in the present
project by the large size of the dataset and its heterogeneity
(Canadian Christians, boys from New England private schools,
MENSA members, and
a
convenience sample from
all
over the
country), combined with the fact that separate analyses of the
four original datasets produced results
that
were highly
consistent with one another.
In order to determine whether the SISM functions similarly in
all four studies, each dataset is first modelled individually, and
the moral stage
-
item difficulty estimates are correlated.
Subsequently, the data from
all
four studies are pooled, and
modelled with
a
single partial credit analysis. Patterns of
performance are analysed in terms of Kohlberg’s stage theory,
and relationships between moral judgement stage and gender,
educational attainment, and age are examined.
Individual analyses
Individual partial credit analyses of the data from each original
study were conducted in order to determine whether patterns
of performance across the four studies were similar enough to
warrant pooling the data for
a
single analysis. Results from the
individual analyses were similar in two ways. First, the patterns
of both stage
-
item difficulties and person ability estimates for
the individual analyses were similar to one another. Conse
-
quently, they were also very similar to patterns in the overall
model of the pooled data (presented below). Second, the
correlations among the stage
-
item difficulties for the four
individual analyses were
very high. Stage
-
item difficulty
estimates for each stage of each of the
six
moral issues were
calculated and compared across the four studies. Despite
differences in the samples, data collection, and raters, the
stage
-
item difficulty estimates were strongly correlated
(B
=
.94-.98), as shown in Table 2. Correlations of this magnitude
are a strong indication that the SISS functioned similarly
enough across these studies to warrant pooling their data into
a
single analysis.
Pooled analysis
Item analysis.
The infit and outfit statistics for all of the stage
-
item difficulty estimates were considered to
fit
the model if
t-
scores were smaller than 2.0. Table
3
shows the
fit
statistics
and standard errors for each of the stage
-
item difficulty
estimates in the analysis. All of the infit
ts
and outfit
ts
are
well below
2.0.
In fact, most are negative. Note, however, that
the infit
ts
for the law and punishment issues are less than
-
2.0. There is less random variation in performances
on
these
items than expected by the model. This is referred to as overfit.
It
means that individuals who have an estimated person ability
higher or lower than the difficulty of a given level of an
item-
say, for example, level 3
-
are very unlikely to have been
awarded
a
score
at
that level of the item. In this particular
analysis, this overfit reflects
a
pattern of performance that is
consistent with the notion that within
a
given domain,
reasoning forms
a
structure d’ensemble.
For the law and
punishment items, individuals with person ability estimates
Table
2
Correlations among stage
-
item da@culty estimates
for
four
moral
development studies
AtWlLm
Walker
Kohlberg
Commons ,9429 ,9696 .9482
Armon
.9824 .9816
Walker
.9830
Table
3
Fit statistics
for
stage estimates
(n
=
996)
Stage thresholds (standard erron below)
Injit Outfit Injit
ozctf;t
Name Score Max.
1.5 2 2.5 3 3.5 4 4.5 5
OMS,)
(MU
(t)
(t)
1.
Life
4274 7352
-
7.31
-
5.70
-
2.54
-
1.47 0.90 2.90 4.73 6.59 0.92 0.92
-
1.7
-
1.3
1.03
0.62 0.24
0.22 0.16
0.17 0.23 0.33
0.41 0.32
0.24
0.22
0.14 0.17
0.22 0.36
0.70 0.58
0.26 0.24
0.18 0.16 0.25 0.36
0.34
0.31 0.26
0.25 0.20
0.14 0.28
0.59 0.51
0.29 0.20
0.15 0.17 0.32 0.42
0.44
0.39 0.31
0.26 0.19 0.19 0.27 0.32
2.
Law
4068 6984
-
5.13
-
3.84
-
1.88
-
0.84 0.75 2.28 4.83 6.76 0.89 0.90
-
2.2
-
1.7
3.
Conscience
3765 6368
-
5.88
-
5.13
-
2.51
-
1.36 0.98 2.62 5.02 6.58 0.95 0.94
-
1.1
-
0.9
4.
Punishment
4042 5908
-
4.56
-
3.41
-
2.14
-
1.17 0.10 2.23 5.79 0.81 0.84
-
3.7
-
2.6
5.
Contract
4279 7336
-
5.75
-
5.05 3.11
-
1.18 0.90
2.62 5.65
6.79
0.99
1
.oo
-
0.1
0.0
6.
Authority
3391 5672
-
4.69
-
3.89
-
2.80
-
1.83
0.70 2.87 4.91 6.10
1.01
1.01 0.3
0.1
Mean
SD
0.00
0.30
0.93
0.94
-
1.4
-
0.07
0.06
1.5
-
1.1
1.1
INTERNATIONAL JOURNAL
OF
BEHAVIORAL DEVELOPMENT,
2002, 26 (2), 154
-
166
159
that reflect
a
high probability of performance at a given stage,
say stage 3, are very likely
to
have been awarded
a
stage 3 score
on the law and punishment issues, whereas individuals with
person ability estimates that reflect
a
high probability of
performance at stages
1,
2,
4,
or
5
are very unlikely to have
been awarded a stage 3 score on the law and punishment
issues. In
a
sense, from the perspective of the model, the
pattern of performance on these items is “too good to be true”.
However, from the perspective of stage theory, this is an
expected pattern of performance.
The map of person ability estimates and stage
-
item
difficulty estimates shown in Figure 1 provides further
information about performance on items. On the far left of
the figure is the logit scale. It spans
-
7.0
to
+8.0
logits.
To
the
right of the logit scale are the person ability estimates, each of
which is represented by a
I,
0,
or
X.
To
the far right are the
stage
-
item difficulty estimates (Thurstone thresholds, Masters,
1982), each of which is labelled with its issue and stage. Wide,
pale
-
grey bands highlight estimates for full stages
2,3,
4, and 5.
Note that the item difficulties for each stage or half
-
stage tend
to cluster together at around the same ability level, with some
overlap between 2.5 and
3.0,
and a great deal
of
overlap
between 1.5 and
2.0.
When 95% confidence intervals for each
of the stage
-
item difficulty estimates are calculated from the
standard errors shown in Table
3,
areas in which there is no
overlap of confidence intervals appear at the
3.013.5, 3.514.0,
and 4.0/4.5 transitions. These are represented with narrow
grey bands. There are no similar gaps between 1.5 and
2.0,2.0
and 2.5,
2.5
and
3.0,
and 4.5 and
5.0.
Gaps between confidence intervals of groups of stage
-
item
difficulty estimates occur when individual performances are
highly consistent.
In
this case, the gaps reflect the fact that
a
large percentage
of
individual performances are predominantly
at
a
single stage across
all
six issues. This type of pattern is
expected when learning involves the qualitative restructuring of
knowledge rather than the simple additive accumulation
of
knowledge (Fischer
&
Bidell, 1998; Wilson, 1985). Similar
evidence that stages are qualitatively distinct modes of
reason-
Ability Estimates Stage Estimates
Logits
8.0
0000000
111111111111000
5.0
XllllllllllllllllOO
Xlllllllllllllll
111111111111
.
...
.
.
.
.
.
. .
.
111111111111
xxllllllllllllllllllllllllll
4.0
11111111111111111111llllllllllllOOOOOOoOOOOOOOOOO
XXXl
XXXXXXllllllllllllllllllllllllllllllllllllllllllllll
-
3.0
Xlllllllllllllllllll
Xlllllllllllllll
111111111111
-
7.0
Conscience/4.5
Lifd4.5 Law/4.5 Authority/4.5
Lifet4 Cond4 Authority14
ContracV4
Law14 Punishment4
Lifd3.5 Cond3.5 ContracV3.5
LawI3.5 Authority/3.5
PunishmenV3.5
Authority/2.5
Lifell.5
Figure
1.
Map
of
person ability
estimates
and stage
-
item
difficulty
estimates
(n
=
996).
Each
I,
X,
or
0
=
one
case.
160
DAWSON
/
KOHLBERG’S
MORAL
JUDGEMENT STAGES
REVISITED
ing has been presented elsewhere (Dawson, 1998; Draney,
1996; Fischer, Hand,
&
Russel, 1984; Fischer
&
Kennedy,
1997; Hartelman, van der Maas,
&
Molenaar, 1998; Wilson,
1985).
In
the present analysis, the distribution of stage
-
item
difficulty estimates
is
complex. If Kohlberg’s formulation of
the stages is correct,
a
delay in development that would lead to
gaps is expected following the consolidation of thinking
at
a
given stage, and prior to any reorganisation at the following
stage. Thus, we would expect to see gaps between full stage
-
item difficulty estimates and subsequent half stage
-
item
difficulty estimates (the
2.0/2.5, 3.013.5, 4.0145 transitions).
Once new structures are available, it is expected that they will
be relatively rapidly employed to restructure a range of
knowledge, which means that we would expect smooth
transitions, perhaps even some overlap of estimates,
at
1.5/
2.0, 2.513.0, 3514.0, or 4.515.0. Such
a
pattern of smooth
transitions and gaps is supportive of the
cognitive-develop-
mental notion of structured wholeness
-
that,
at
least within a
given domain, reasoning should ‘‘consolidate”
at
one stage
before advancing to the subsequent stage (Kohlberg, 1969).
Although apparent between stage 3.0 and half
-
stage 3.5,
and stage 4.0, and half
-
stage 4.5, statistically significant gaps
are not seen at the
2.012.5 transition. The lack of
a
gap
at
the
2.012.5 transition may be due to any one (or
a
mixture) of four
factors:
(1)
the smaller sample size in the 2.012.5 range; (2) a
less reliable definition of the stages
at
this level; (3) more rater
error at this level; or
(4)
less consistent reasoning at this level.
Although the sample size is considerably smaller in this range
than in the higher stage ranges, it should be noted that analyses
of quite small samples sizes (140
-
200 cases) produce the same
pattern seen here, with clear gaps at the higher stages, and no
gaps at the lower stages
-
even when the number of respon
-
dents
at the higher stages is fewer than the number of
respondents in the present sample who are performing at the
lower stages (for an example, see Dawson,
2000).
To
determine whether patterns of performance appear less
consistent at lower stages, the relationship between the range
of stages represented in individual performances and ability
estimates was examined.
A
hierarchical ANOVA revealed that
the range of raw stage scores
(from
0
to
2.5), increases
somewhat as ability estimates decrease:
F(5,984)
=
7.294,
p
=
.01,
r
=
.19. Although the effect size is small, this apparent
decrease in consistency within individual performances may
account,
in
part, for the overlap in stage
-
item difficulty
estimates at the
2.0/2.5 transition. The reason for this trend
is not clear, however.
In
addition to the unexplained overlap in stage
-
item
difficulty estimates
at
the 2.0/2.5 transition, there is a
significant, unanticipated, gap at the
3.514.0 transition. This
gap suggests that the transition from half
-
stage
3.5
to stage 4.0
is
a
move from one full stage to another, even though it
is
characterised in Kohlberg’s model as
a
move from a transi
-
tional level to
a
full stage. Both Commons and his colleagues
(Commons, Richards, with Ruf, Armstrong
-
Roche,
&
Bret-
zius, 1983; Commons et
al.,
1998) and Fischer et al. (1984)
have proposed that there are two stages (abstract and formal),
rather than one (Kohlberg’s stage 3.0) between concrete
operations (Kohlberg’s stage 2.0) and systematic operations
(Kohlberg’s stage 4.0). In this formulation, Kohlberg’s stage
3.0
is considered abstract or early
formal,
and his transitional
level 3.5 is
consideredfomzal. The model presented in Figure 1
lends support to Commons’ and Fischer’s assertions.
If
Kohlberg’s half
-
stage stage 3.5 is accepted as a full stage,
the pattern of stage
-
item difficulty estimates from stage
3.0
to
stage
5.0
is
remarkably consistent. Transitions from one full
stage to another are marked by statistically significant gaps
between stage
-
item difficulty estimates. Although this is not
incontrovertible evidence that the transitions between both
“adult” and “childhood” stages represent the same kind of
qualitative change,
it
is, at the least, consistent with this thesis.
Person
analysis.
The overall person separation reliability for
126 nonextreme cases
-
cases with perfect scores and zero
scores are not included in the estimation
-
is .93. The person
separation reliability statistic is equivalent to Cronbach’s alpha,
and is based on the ratio of the variation in the mean squares
(the standard deviation) to the error of measurement, also
known
as
a
signalhoise ratio (Wright
&
Masters, 1982). In this
instance,
a
person separation reliability of .93 means that
persons whose ability estimates are at a given stage can reliably
be differentiated from persons whose ability estimates are as
close as an adjacent stage. Standard errors for the person
ability estimates range from 0.49 to 1.75 logits with
a
mean of
0.64.
The infit and outfit statistics for all person ability estimates
were considered to
fit
the model if t
-
scores were greater than
-
2.0 or less than 2.0. Fit statistics lower than
-
2.0 indicate
a
greater than expected consistency within performances (over-
fit),
whereas
fit
statistics higher than 2.0 indicate less
consistency than expected (underfit). Both underfit and overfit
are types of misfit, but are distinct in their implications,
In Figure 1, each case is represented by
an
I,
X,
or
0.
Performances that overfit the model are indicated with
0.
These performances are more consistent across issues than
expected by the model. Seventy
-
eight of 119 performances
with all issue scores at
a
single stage exhibit ovefit. Forty
-
one
of
95
cases with performances that spanned 1; or more stages
exhibit underfit, because they are less consistent across issues
than expected by the model. These are indicated with
X.
Because Rasch models are probabilistic, a certain amount of
“noise” or random variation is expected in the data. When the
expected variation is not present, as is the case when many
individuals perform
at
a single stage across
all
issues, these
performances will overfit the specifications of the
model.4
However, performances of this kind are not problematic for
stage theory, which expects
a
high level of consistency in the
stage ofreasoning exhibited by an individual in a given domain
(Kohlberg, 1969). More problematic for stage theory are
performances that span a wide range of stages
-
those that
underfit the model. When misfit of this kind occurs,
it
is
desirable to re
-
examine the original data to determine if coding
errors were made or
if
there is evidence that these perfor
-
mances genuinely do not
fit
the expected pattern of response.
Rasch’s probabilistic models expect ability estimates to be more
continuously distributed than they are in the present sample. The jagged,
“toothy”, quality
of
the ability distribution shown in Figure 1, accompanied as it
is by a high degree
of
overfit, is a violation
of
the modelled measurement
requirements. The fact that a pattern of performance that
is
in keeping with
cognitive developmental theory shows up as a significant amount
of
overfit in a
partial credit model points to a discontinuity between the model and both
developmental theory and actual patterns of performance. This phenomenon has
been observed elsewhere, and a model, which extends the Rasch model, has been
developed to encompass the phenomenon (Draney, 1996; Wilson, 1989).
Though promising, this model has not yet been formulated for the type of scored
interview data employed here.
161
INTERNATIONAL JOURNAL
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BEHAVIORAL DEVELOPMENT,
2002,
26
(2),
154
-
166
Unfortunately, the original interviews were not available for
analysis,
so
this kind of evaluation was not possible.
The concentration of person ability estimates at the
4.0,2.0,
and
0.0
logit ranges, along with the general trend toward model
overfit, indicate large subgroups of individuals who have
a
high
probability of performing across all issues at stage 4.0,
half-
stage 3.5, or
3.0,
respectively. For example, an individual
whose ability estimate is 4.0 logits has a greater than 73%
probability
of
performing
at
the stage
4
level
on
all moral
issues, and less than a 27% probability of performing at the
half
-
stage
4.5
levels5
Age,
education, and sex effects
Correlations between moral reasoning ability and the age,
educational attainment, and gender of participants are shown
in Table 4.
Age.
To further examine age, education, and sex effects,
several multiple regression analyses were conducted. First, the
relationship between moral ability estimates and age is
examined. A logarithmic model provides the best
fit,
revealing
a
strong relationship between age and moral reasoning ability:
R
=
.75,
F(1,
964)
=
1244.06,~
<
.01,
Moral ability estimate
=
-
9.69
+
7.641,,,,,.
In order to assess whether some stages in this model should
be considered “adult” stages, the relationship between age and
stage is examined in Table
5.
Stage assignment for this table
was based
on
moral ability estimates as follows: stage
5.0
=
6.01 through
8.00,
stage 4.5
=
4.01 through
6.00,
stage 4.0
=
2.26
through 4.00,
stage 3.5
=
0.01
through
2.25,
stage 3.0
=
-
1.74 through
0,
stage 2.5
=
-
2.99 through
-
1.75,
stage 2.0
=
-
4.49 through
-
3.00,
stage 1.5
=
-
7.00
through
-
4.50.
The
minimum age
at
which any individual in this sample has at
least
a
50%
probability
of
performing at stage
5.0
on
any of the
6 moral issues is 25 [only 2 individuals below age
30
(10%)
were in this group], with a mean age of 44, and although two
individuals below age 21 (2%) had a 50% probability of
performing at transition 4.5, the mean age at this level is 42.
Only
3
individuals below age 21 (1.2%) had a 50% probability
of performing at stage
4.0.
Given that the minimum ages in this
table can be said to represent minimum ages of acquisition, the
results of this analysis support previous reports that stages
4.0,
and
5.0,
and transition 4.5 appear to occur rarely before
adulthood.
Although there are no differences between males and
females when sex and moral ability estimates are correlated
Table
4
Correlations between moral reasoning
ability and education, sex, and age
Education
Age
Sex
.7948
.6593 -.0212
(n
=
929)
(n
=
966)
(n
=
987)
p
<
.O1
p
<
.O1
p
>
.51
Gibbs, Basinger, and Fuller
(1992)
report
a
similar finding employing their
Sociomoral Reflection Instrument.
Table
5
Stage attainment by age
-~
Stage Valid cases Min. age Max. age Mean
5.0 19 25 66 44
4.5 99 17 83 42
4.0 244
18
86 40
3.5 350 13
72 35
3.0 120
8
58 19
2.5 65
7
18 12
2.0 49 6
17
10
1.5 19 5 14
8
directly, when sex is entered into
a
regression of moral ability
estimates by the log of age, the curves for males and females are
significantly different, as shown in Figure 2. Overall, males
appear to perform at slightly higher levels than females,
explaining about 1% of the variance in ability estimates.
(In
order to make the relationship between stage attainment and
the ability estimates clearer, wide, horizontal, grey bands are
included in Figures 2 and 3. These represent the approximate
ranges for performances
at
Kohlbergian stages 2.0, 3.0,
4.0,
and 5.0, as labelled on the right of each figure.) The multiple
regression
of
the log of age and sex on the person ability
estimates results in the following equation:
R
=
.76, F(2, 963)
=
647.19,p
<
.01,
Moral ability estimate
=
-
9.63
+
7.741,,,,, -.53,,,
tlogage
=
35.96,
p
<
.01,
tsex
=
-4.75,p
<
.01.
The relationship represented in the above equation is
complex. Table 6 shows the distribution of moral stage
-
item
difficulty estimates by age and gender. (For
a
sense of where
these standardised estimates fall
on
the stage continuum,
consult Figure 2. Note that the difference in terms of actual
stages are never more than
a
of
a
stage.) The mean moral
ability
(MAE)
estimates for males and females in each age
group are shown on the right. For each age group, the
estimates for the sex with the higher mean estimate are shown
Table
6
Moral ability estimates
(MAE)
by age and sex
Sex
Male
Female
Age
group
(Mean
MAE)
(Mean
MAE)
5
-
9
10
-
1 4
15
-
19
20
-
24
25
-
29
30
-
34
35
-
39
4044
45
-
59
50
-
54
55
-
59
60
-
64
65
-
69
70-86
-
3.32
-
2.11
0.08
1.22
2.05
3.02
2.86
2.17
3.17
3.25
3.53
4.07
3.28
3.14
-
4.03
-
1.76
0.16
2.17
2.83
2.67
1.91
2.12
2.42
1.87
2.69
3.11
2.55
3.30
162
Q)
e
.
-
E
e
Y
DAWSON
/
KOHLBERG‘S
MORAL JUDGEMENT STAGES REVISITED
ia
Male
[y
=
8.241-0G(age)
-
10.43
r2
=
0.591
Female
[y
=
7.048~0G(age)
-
9.142
r*
=
56
-
8
1
I
I
I
I
0
20
40
60 80
1
00
Age
Regression
of
moral
ability
estimates
with
the
log
of
age
by
sex
(female
=
376;
male
=
620).
in bold. Although the males appear to have an advantage
between ages 5
-
9 and 30
-
69, the females have the advantage
from ages 10 to 29 and 70 to 86. One possible explanation for
this complex pattern is cohort differences.
It
is plausible that
older women did not have the same educational and lifesryle
advantages afforded to men in their age cohort, whereas social
change resulting from the women’s movement of the 1960s
and 1970s may have provided women in the younger cohort
with more of these opportunities.
Educational attainment.
Next, the relationship between ability
estimates and educational attainment is examined. The multi
-
ple regression of educational attainment on ability estimates
results in the following equation, in which individuals advance,
on average, about stage for every four years of formal
education:
R
=
.79, F(1, 927)
=
1590.12,
p
<
.01,
Moral ability estimate
=
-
4.33
+
.42,+
A scatterplot of this regression, with moral ability on the
y
-
axis and educational attainment on the x
-
axis, shows a linear,
but fan
-
shaped distribution of estimates is shown in Figure
3.
The range of moral ability estimates increases with advances in
educational attainment, indicating that the relationship be
-
tween educational attainment and moral development weakens
as years of educational attainment increase, though the overall
slope appears to remain relatively constant.
To
examine this
relationship further, a quadratic component was added
to
the
regression to examine whether the effect of educational
attainment declines as educational attainment increases.
Although the quadratic component made a statistically sig
-
nificant contribution: F(2, 926)
=
839,
p
<
.01, it explained
only an additional
1
%
of the variance in person ability estimates.
As shown in Table 7, in this sample, the minimum number
of years of education required to achieve a
50%
probability of
performing at stage
5
on any issue was 15, or three years of
post
-
secondary education. Only one person without a bache
-
lor’s degree
(5%)
performed at this level. Although the
minimum number of years required to achieve a 50%
probability of
performing at the 4.5 level on any issue was
Table
7
Stage
by
educational attainment
Stage Valid cases
~~~
Min. ed. Max. ed. Mean
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
18
96
232
334
116
65
48
19
15 21
19
11
21 17
9
21 17
I
21 14
1
19 10
2
18
7
1 15 4
1
9
3
INTERNATIONAL JOURNAL
OF
BEHAVIORAL DEVELOPMENT, 2002, 26 (2), 154
-
166
8
7
6
5
4
83
.E
2
(0
.w
I1
.-
-
$0
-
2
-
1
0
=
-
2
-
3
-
4
-
5
y
=
0.42,d
-
4.3
P
=
.63
:;I:
@
-
8
’
I
I
I
I
I
0
5
10
15
20
25
Educational Attainment
Figure
3.
Regression
of
moral
ability
estimates
with
educational attainment
(a
=
996).
11,
only
two
individuals with less than one year of college
education (2%) performed at this level. Similarly, although the
minimum number of years required to achieve a 50%
probability of performing
at
the 4.0 level on any issue was 9,
only two individuals with less than a high school diploma
(1
%)
performed at this level.
Sex, age, and educational attainment.
Adding sex to the
regression
of
educational attainment on the moral ability
estimates does not explain any additional variance. However,
sex explains about
0.3%
of the variance when entered into a
stepwise regression
of
moral ability estimate with education
and age:
R
=
.81, F(3, 922)
=
604.88,
p
<
.01,
Moral ability estimate
=
-
6.97
+
.28,d
+
3.221,,,,,
-
.28,,,
ted
=
14.86,
p
<
.01,
tiogage
=
9.02,
p
<
-01,
tsex
=
-
2.74,
p
<
.Ole
Clearly, education accounts for most of the variance (63%)
in
moral ability estimates. The log of age adds an additional
3%,
whereas sex contributes less than
0.3%.
The reduction in
the effect for sex, after education is taken into account, lends
support to the argument that most, if not
all
of the sex
difference in moral ability estimates is due to cohort effects
rather than systematic biases in the scoring system or
theoretical perspective.
163
Discussion
The analyses presented here brought together four sets of data,
collected by four different research teams over
a
period of
30
years. The samples included
a
group of parents and their
children, a diverse life
-
span sample, a group of MENSA
members, and
a
group of private
-
school boys. Four groups of
rates scored the data for stage using Kohlberg’s Standard Issue
Scoring Manual (SISM).
All of these differences between the datasets would interfere
with attempts at comparison using conventional analytical
methods. At best, a meta
-
analysis could be conducted,
comparing statistical results from one sample to another, but
there would be no way to assess just what was being compared.
Rater agreement and consistency would have to be assumed,
despite the fact that differences in interpretation and interview
methods could easily vary in ways that would influence
outcomes.
Exploring the datasets for
fit
to
a
probabilistic measurement
model provided a basis for comparing results from these three
studies. Despite their independent samples and execution, the
stage scoring across the studies was congruent enough to result
in very high correlations between stage
-
item difficulty esti
-
mates (.94-.98). Pooling the four datasets employed here was
easily justified by these correlations.
The detailed analysis of these pooled data resulted in
164
DAWSON
I
IZOHLBERG’S MORAL .JUDGEMENT
STAGES
REVISITED
interesting evidence that confirms results reported in previous
research about the ordered acquisition of moral stages and the
relationship between moral stages and age, education, and sex.
This analysis also provides new support for the notion of stages
as qualitatively distinct modes of reasoning that display
properties consistent with a notion of
structure d’ensemble,
and
reveals evidence
of
a stage, between Kohlberg’s stages
3
and 4,
that has not previously been revealed
in
analyses of Kohlber-
gian data.
Moral development, as assessed by the SISM, is strongly
related to both educational attainment and age.
In
keeping
with findings from previous research, the relationship between
age and moral development is curvilinear and fan
-
shaped, as
shown in Figure 2. The relationship between educational
attainment and moral development is linear, rather than
curvilinear, suggesting that educational environments have an
equivalent impact across the course of development. However,
this relationship, too,
is
fan
-
shaped, suggesting that as we age
the impact of education becomes more variable.
The analysis of the relationship of age and moral develop
-
ment also supports previous evidence that the higher stages of
moral development are appropriately labelled “adult
stages”.
Moreover, the model of development presented in Figure 1
suggests that these stages represent the same kind of qualitative
shifts in modes of reasoning that take place at stages that
predominate in childhood and adolescence. This adds support
to an increasing body of evidence that characterisations of
adulthood as a period of decline in mental abilities are narrow,
if not incorrect. Notions of adult stages in particular, and adult
development in general, raise interesting questions. First, how
can adult stages be reconciled with theories that link stage
change with childhood biological changes
(e.g., Epstein,
1990)? And in a different vein, is it possible that some of the
changes in cognition previously viewed as declines, such as
evidence pointing to the
“crystallisation” of intelligence, are
better viewed as symptoms of higher order functioning? These
and other issues are being explored in an increasing body of
research into positive adult development (for examples, see
Alexander
&
Langer, 1990; Commons et al., 1989b; Kohlberg
&
Higgins, 1984; Sinnott
&
Cavanaugh, 1991).
Only a weak relationship, accounting for less than 0.3% of
the variance, was found between sex and stage after age and
education were taken into account, with older males perform
-
ing at slightly higher levels than older females. Walker (1984),
in his meta
-
analysis of 79 studies of sex differences in moral
reasoning development, found inconsistent evidence
of
differ
-
ences in childhood, with males doing better in some studies
and females doing better in others. Adult differences were
more consistent, with males apparently doing better than
females, but this effect disappeared when educational attain
-
ment was taken into account. The present analysis suggests
that some effect of sex on moral ability remains after taking
both education and age into account, but the effect is very
small and nonsystematic,
in
that males and females appear to
have the advantage at different ages. Gilligan (1982) challenges
the universality
of
Kohlberg’s moral stages on the basis of the
assumption that males and females perform differently
on
the
MJI. The preponderance of evidence strongly suggests other
-
wise.
The map of development in Figure 1 provides evidence of
gaps between full
-
stages that support both the concept
of
an
invariant hierarchical sequence in stage development, and the
notion of stages as qualitatively distinct modes of reasoning
that display properties consistent with a notion of
structure
d’ensemble,
an idea that has been much debated in the literature
(see, for example, Bidell
&
Fischer, 1992; Demetriou, Efklides,
Papadaki, Papantoniou,
&
Economou, 1993; Kohlberg
&
Higgins, 1984; Turiel
&
Davidson, 1986). The gaps at the
3.0/
3.5 and 4.0/4.5 transitions indicates that reasoning tends to
consolidate at a given stage before progressing to the next
stage. From stage
3
onward, individual stage
-
item difficulty
estimates across life, law, conscience, punishment, contract,
and authority issues tend to cluster within narrow ranges,
about one logit in width, with statistically significant gaps
between groups of full
-
stage and subsequent half
-
stage
-
item
difficulty estimates. Keeping in mind that here we are looking
at reasoning within a narrowly defined domain, this pattern
supports the notion of stages as
structured
wholes,
coherent
systems
of
thought that tend toward consolidation at a given
order of complexity until conditions are such that movement to
the next order of complexity is possible. The absence of a gap
between the estimates at the
2.0/2.5
transition violates this
pattern. Further research must be conducted to determine
whether this is the result
of
measurement error or differences in
the nature of moral development at this level.
The distribution of stage
-
item difficulty estimates in clumps
along the moral ability scale is supportive of the notion that
stages represent qualitatively distinct modes of reasoning.
Although stage
-
item difficulty estimates occur in clumps,
person ability estimates can fall at any point on the ability
scale. The fact that person ability estimates can fall at any point
along the scale could be taken to support a cumulative model
of learning. However, the pattern of these estimates is not
smooth, as might be expected if learning can best be described
as a cumulative rather than transformative process. Instead, the
distribution of person ability estimates is
“toothy”. Though a
given individual can perform at any point on the develop
-
mental continuum, more individuals are clustered at points
where consolidated performances are likely than at points
where mixed performances are likely. This distribution
suggests that learning is not a smooth additive process, but a
transformative one, in which one qualitatively distinct mode of
reasoning is replaced by another qualitatively distinct mode of
reasoning.
An interesting finding is the apparent existence of an
additional stage between Kohlberg’s stages
3
and 4 (see note
5).
This is in keeping with assertions by both Fischer, Hand,
and Russel (1984) and Commons (Commons et al., 1983,
1998) that the concrete stage (Kohlberg’s stage
2)
is followed
by both an abstract stage (Kohlberg’s stage 3) and a formal
stage (Kohlberg’s half
-
stage 3.5 or 3/4). Kohlberg’s stages were
initially modelled on Piagetian stages, and developed into their
present
form
through a process of bootstrapping. Criteria for
scoring at transitional levels were developed through the
bootstrapping process, and these levels were never viewed as
stages in their own right. That the criteria for
3.5
appear, to a
large extent, to capture the formal stage as defined analytically
by Commons and Fischer (though Fischer calls them levels
rather than stages), and the criteria for stage
3
appear to
capture the abstract stage, is a fortuitous “accident” of the
bootstrapping method.
This analysis reveals considerably more about moral
development than traditional methods, primarily by providing
a means for estimating probabilistic, equal
-
interval item
difficulties and person ability estimates. The Rasch family
of
measurement models have been used extensively in educa-
165
INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT, 2002, 26 (2), 154
-
166
tional measurement and outcomes assessment. Their potential
value in developmental research is enormous. They can be
applied to many of the problems faced by developmental
researchers. For instance, they can be used to:
(1)
construct
developmental measures;
(2)
examine the construct validity of
developmental measures;
(3)
calibrate developmental instru
-
ments;
(4)
examine the pooled results from studies that
intentionally measure the same developmental construct;
(5)
compare different developmental scoring systems; and
(6)
contribute to the creation of universally recognised and
accepted sample
-
free units of measurement (Fisher,
1994).
In addition, as demonstrated here, they are an excellent tool for
examining stage performance because of the rich information
they provide about
both
individual performances of items and
persons in combination with the information they provide
about developmental trends.
Our understanding of developmental phenomena hinges, in
part, on our ability to construct theoretical models
of
development and submit these to rigorous empirical examina
-
tion. Shared understanding of development could be greatly
enhanced by “common
currencies” for the exchange of
quantitative information (Fisher,
1994)
such as the sample-
free logit metric suggested by the results of the analysis
presented in this paper. Until relatively recently, the practical
difficulties surrounding developmental research, such
as
restrictions on sample size imposed by time and expense
constraints, have made it difficult to devise and adequately test
developmental instruments, particularly outside of the
logico-
mathematical domain. The rigorous but flexible measurement
principles employed by Rasch’s models permit
us
to simulta
-
neously re
-
examine our theoretical constructs and instruments,
and open the door to new insights.
Manuscript received December 1999
Revised manuscript received March 2000
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