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	<web>http://www.sociology.org/content/vol004.002/schneider.html</web>
	<title>Emergent Clusters of Denotative Meaning</title> 
	<abstract><p>Osgood found that three dimensions &#8212; Evaluation (E), Potency
  (P) and Activity (A) &#8212; are the basic dimensions of affective response .  The author
  supposes that social structural properties should be reflected in the affective
  representation of role-identities. To the extent that Evaluation, Potency, and Activity
  (EPA) ratings of role-identities are similar, these role-identities share one denotation
  that should indicate structural implications. K-means cluster analysis is used to create
  distinct clusters of role-identities with similar EPA profiles. U.S. and German subjects,
  comparable in age, class, and education, rated role-identities on Osgood's semantic
  differential scales. For both cultures the cluster analysis of affective meanings
  identified six clusters. The problem of labeling emergent meaning in explorative cluster
  analysis is addressed by employing methodological triangulation. Labels for emergent
  institutional/structural categories are tested with another independent sample.</p>
	</abstract>
	<availability status="free">Copyright 1999 Electronic Journal of Sociology</availability>
</description>
 <author>
	<name>
	 <first>Dr. Andreas</first>
	 <last>Schneider</last>
	</name>
	<address>
	 <email>Schneider@ttu.edu</email>
	 <organisation>Texas Tech University</organisation>
	 <division>Department of Sociology, Anthropology and Social Work</division>
	</address>
</author>
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 <description>
	<web>http://www.sociology.org/</web>
 <title>Electronic Journal of Sociology</title>
	<idno type="issn">1198 3655</idno>
</description>
 <publisher>
	<name><full>Athabasca University</full></name>
	<address><street>1 University 
Drive</street><city>Athabasca</city>
	 <province>Alberta</province><postalcode>SOG 
OWO</postalcode>
	 <email>mikes@athabascau.ca</email>
	</address>
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	<name><full>International Consortium for Alternative Academic 
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 <description>
	<date><year>1999</year></date> 
	<idno type="VOL">4.2</idno> 
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 <keywords scheme="lcsh"> 
	<item>Sociology</item>
	<item>Social Sciences</item>
	<item>Social Problems</item>
	<item>Periodicals</item>
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 <idno type="IUICODE">100.4.2.2</idno> 
 <startdate><year>1994-</year></startdate>
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<body>

  Osgood found that three dimensions &#8212; Evaluation (E), Potency
  (P) and Activity (A) &#8212; are the basic dimensions of affective response .  The author
  supposes that social structural properties should be reflected in the affective
  representation of role-identities. To the extent that Evaluation, Potency, and Activity
  (EPA) ratings of role-identities are similar, these role-identities share one denotation
  that should indicate structural implications. K-means cluster analysis is used to create
  distinct clusters of role-identities with similar EPA profiles. U.S. and German subjects,
  comparable in age, class, and education, rated role-identities on Osgood's semantic
  differential scales. For both cultures the cluster analysis of affective meanings
  identified six clusters. The problem of labeling emergent meaning in explorative cluster
  analysis is addressed by employing methodological triangulation. Labels for emergent
  institutional/structural categories are tested with another independent sample.
<p>Role-identities can carry different affective meaning in
different cultures. Even though both cultures agree on the definition of a <i>parent</i>
(<i>Elternteil</i>), North Americans experience dramatically more potency than Germans
(1.95 vs. 0.41 on a -3.33 to 3.33 scale). This paper focuses on methods for comparative
inter-cultural research. The main interest is the search for categorizations that allow
cross-cultural comparisons on an aggregate level.</p>

<p>Social structure is established by pattern of
social behavior. &quot;People doing things together in space and/or time&quot; (Wallace
1983,p.29). Since social components are central in the concept of role-identity,
denotative categories of role-identities should reflect structural properties. Affective
meaning is determined by cultural structure, the pattern of perception, thinking, or
feeling. Affective meaning reflects &quot;people perceiving, thinking, or feeling things
together in space and/or time&quot;(Wallace 1983, p.29-30). Social structure and culture
are interdependent. Values and beliefs influence socially patterned behavior, just as
social structure influences values and believes (Wallace 1983). If social structure
influences values and beliefs, affective meanings of role-identities should be patterned
according to structural properties relevant to role-identities.</p>

<p>Higher order categories of role-identities should denote
structural properties. <i>Fathers</i>, <i>teachers</i>, and <i>judges</i> can be
categorized as authorities; the <i>father</i> might also be defined by institutionally
his family membership. Trying to avoid imposing categories, I applied an inductive
methodology that facilitates the emergence of categories from the data. 420
Role-identities were rated in South Carolina (Heise 1978) and Mannheim Germany (Schneider
1990) on Osgood's (1962, Snyder and Osgood 1969) evaluation, potency, and activity (EPA)
dimensions. These EPA profiles are measures of affective meaning, tested to be
cross-culturally comparable (Osgood et al. 1975).</p>

<p>I use an explorative K-means cluster analysis of these EPA
profiles to create categories role-identities with similar EPA profiles. Since the
major categorizing influence on role-identities are their structural properties and
structural properties should influence cultural properties of affective meaning, emerging
clusters of EPA profile should indicate structural properties.  Role-identities
within one cluster should share denotative meaning.  One of the problems in this
approach is to determine the level of abstraction necessary to reflect denotative meaning.
The level of abstraction is directly related to the number of clusters: the higher
the number of clusters, the less abstract is the category denoted by the role-identities
in the cluster. This translates into the problem of determining the correct number of
clusters.</p>

<p>The validity of the cluster solution, the denotative
meaning of clusters, is then tested in methodological triangulation using another
independent sample. This combination of inductive and deductive methodology not only
reveals institutional/structural properties that emerge from affective meaning, but it
also validate categories.  To the degree to which clusters overlap, cultures agree on
denotation. Cross-cultural disagreement is indicated by the degree clusters do not
overlap. I will demonstrate how the U.S. and German cultures agree on the authority
concept but disagree on the sexual-erotic denotation of role-identities.  Even though
both cultures agree to a large extent which concepts belong to the denotative category of
authority, they diverge in the meaning that they assign to this category. In other words,
if both cultures agree on the structural/institutional category of a role-identity, they
do not necessarily have to assign the same meaning to this category.</p>



<h2>DATA AND METHODS</h2>

<h2>U.S. Data</h2>

<p>Questionnaire data were collected from undergraduates in
North Carolina (Heise, 1988), and each role-identity was rated by approximately 56
subjects. The Doubleday Dictionary (Landau, 1975) was used to choose a broad range of
general concepts.Here the focus will only be on role-identities.</p>

<p>Like all other sentiments, role-identities are experienced
effectively. &quot;Classifications of places, peoples, objects and behaviors get
transformed into a domain of feelings, where they lose their qualitative uniqueness,
become comparable to one another, and begin obeying quantitative principles&quot; (Heise
1987, p.6). The affective level of these concepts can be measured on the three dimensions
of affective meaning. Evaluation (E), potency (P), and activity (A) dimensions reveals
that the affective meaning of sentiments is not only <i>bad</i> or <i>good</i>; they are
also <i>strong</i> or <i>weak</i> and <i>lively</i> or <i>quiet</i> (Osgood 1962; Osgood
et al. 1975). &quot;Psychological evaluation and potency
dimensions have their sociological equivalents in status and power (Kemper 1978, 1987),
the activity dimension in social expressivity (Parsons &amp; Shils 1951). EPA profiles can
be seen as a metalanguage that sociologically describes differences of emotions and
identities. This capacity makes EPA profiles an ideal media for cross-cultural
comparisons, especially when multiple languages are involved&quot;(Schneider 1996:126).
Poles of the semantic scales for the EPA dimensions were defined by adjectives:</p>

<p>Evaluation: good, nice - bad, awful</p>

<p>Potency: big, powerful - little, powerless</p>

<p>Activity: fast, young, noisy - slow, old, quiet</p>

<p>The order of the EPA scales and the orientation of the scale
(left or right) is varied to diminish response biases. Subjects had a &quot;?&quot;
category to indicate unfamiliarity with the given concept. Interval scales were used to
compute means on all three EPA differential scales.<endnotenumber>1</endnotenumber></p>


<h2>German Data</h2>

<p>Blind backtranslation, the reproduction of the U.S.
undergraduate with a weighted ratio of German pupils and students, and the similarity of
the measurement instrument, secured a high level of comparability of both datasets.</p>

<p>To correspond to the undergraduate population in the U.S., subjects were not only
university students but also pupils of the thirteenth grade in &quot;Gymnasium&quot;.<endnotenumber>2</endnotenumber> About 400 subjects
were recruited from Mannheim University and two schools (Gymnasien) in Mannheim, a large
industrial city attracting students mainly from the Rhein-Neckar region in former West
Germany.</p>

<p>The German study uses the same stimuli and scales as the
U.S. study, but employed a more modern measurement technology. The existing U.S.
dictionary is used to construct the German stimuli set. U.S. idiomatic concepts like
&quot;fuddyduddy&quot; or &quot;hooligan&quot; are dropped. First, a fluent, bilingual
native German speaker translates 599 role-identities into German. Then, the method of
blind backtranslation (Krebs and Schuessler, 1987) is employed (fig.1). A bilingual native
English speaker translates all the German concepts back into English.<endnotenumber>3</endnotenumber>  Finally, concepts whose backtranslation matches the original English are selected
for further studies. Remaining concepts are examined by North American native English
speaker; and words whose backtranslations are synonymous with the original are selected.</p>

<h2>Figure One: Schematic of the blind backtranslation process,
used to obtain the German stimuli list.</h2>
<p> Source:      Original list of English
role-identities is selected from the U.S. study.</p>

<blockquote>
  <p>Agent:        First
  bilingual person translated all 599 English role-identity terms into German.</p>
  <p>Result:        German
  translation of English terms.</p>
  <p></p>
  <p>Source:       First German
  list of 599 role-identities.</p>
  <p>Agent:        Second
  bilingual person translates all 599 German role-identity terms into English.</p>
  <p>Result:        
  Backtranslated English terms.</p>
  <p></p>
  <p>Source:        Original
  and backtranslated list of 599 role-identities.</p>
  <p>Agent:
           Native U.S. English speaker compares
  both English language lists.</p>
  <p>Result:
           420 role-identities are judged as
  being identical or synonymous.</p>
</blockquote>




<p>The resulting list of 420 well-translated concepts is used as
a stimuli list for the data collection in Mannheim (Germany).  In this study, every
subject has to rate one set of approximately 115 stimuli, which takes about one hour. The
upper limit of stimuli, that a subject can handle was determined in a pretest.  Here
it was indicated that rating 120 stimuli is the threshold where subjects start to get
tired.<endnotenumber>4</endnotenumber></p>


<h2>ATTITUDE: An Interactive Computer Based Instrument</h2>

<p>For the German study I used the computer program <i>Attitude</i>
(Heise and Levis, 1988). <i>Attitude</i> is an interactive interviewing program that
randomizes the order of stimuli, the orientation of the scale and the order in which the
scales are presented. Using the original Pascal<endnotenumber>5</endnotenumber>  program
code, I translated <i>Attitude</i>'s introduction, help system and labeling of the
scales into German. My translation of the scales profited highly from unpublished material
of Charles Osgood.</p>

<p>The labels of the intervals between the points the same as
in the U.S. questionnaire method. Corresponding to visual distances on the scale,
differences between <i>neutral</i>, <i>leicht</i> (<i>slightly</i>) <i>ziemlich</i>
(<i>quite</i>), and <i>äußerst</i> (<i>extremely</i>) are coded as differences of
1.0. The differences between the scale endpoints <i>äußerst</i> (<i>extremely</i>)
and <i>unendlich</i> (<i>infinitely</i>) are coded 1.33, again corresponding to visual
scale distances (see Heise and Thomas, 1989, for discussion of the metric assumptions).</p>


<h2>Cluster Analysis</h2>

<p>Osgood's (1962, Osgood et al. 1975) findings provide
evidence for cross-culturally shared affective meaning. EPA dimensions successfully
distinguish concepts of affective meaning. I set out to investigate my basic assumption
that: to the extent that EPA ratings are similar, corresponding role-identities share one
denotation. The implication is that thresholds for similarity implicitly create boundaries
of meaning. Thus, cluster analysis of EPA profiles may be used inductively to investigate
the possibility that role-identities, measured on their EPA profiles, form clusters of
denotative meaning.</p>

<p>Organizing qualitative concepts according to their
quantitative similarity on EPA dimensions of affective meaning profiles establishes a
Denotative Cluster of Affective Meaning. These Denotative Clusters of Affective Meaning
are higher-order abstractions of Osgood's three dimensions of affective meaning. Clusters
denote institutional/structural membership of role-identities and can be seen as basic
organizations of cultural structure. This paper demonstrates how a system of Denotative
Clusters of Affective Meaning can be explicated and how they are applied in cross-cultural
comparison.</p>

<p>K-means cluster analysis establishes sets of EPA ratings
that are maximally distinctive across sets, while being maximally homogeneous within sets
(Backhaus et al, 1980). &quot;K-means clustering splits a set of objects into a selected
number of groups by maximizing between- relative to within-cluster
variation&quot;(Wilkinson 1990, p.35).<endnotenumber>6</endnotenumber> By selecting the
number of clusters, the researcher influences thresholds of similarity.</p>

<p>Determining the right number of clusters is a difficult
task asking the researcher to listen to her data. The data cannot speak for itself, but
has to be guided through multiple possible solutions to finally deliver emergent
categories. The algorithm in fig.2 is used to enhance the emergence of categories from the
data.</p>

<h2>Figure Two: The algorithm for cluster analysis that is used to enhance the emergence of
natural categories from data.</h2>
<p>(1) If clusters are not distinctive in their denotation, use a higher number of
clusters.</p>

<p>(2) If larger numbers of cluster result in single item
clusters, one should investigate if heightening the number of clusters will still produce
single item clusters. If this is the case the number of clusters should be lowered.</p>

<p>(3) Accept cluster solutions where cases within clusters
hardly vary across solutions with different numbers of clusters, or where clusters split
into cases of different denotation.</p>



<h2>RESULTS</h2>

<h2>Cluster Solution</h2>

<p>Starting with a few clusters in the first solution, I
increase the number of clusters in steps of one. If additional clusters do not change the
existing denotative composition of clusters dramatically, but only result in new
single-item clusters, then the upper limit of the number of clusters is determined.
Conversely, if choosing fewer clusters does combine role-identities with different
denotations into one cluster, the lower end of an interpretable cluster solution is
determined.</p>

<p>I explore solutions for four to twelve clusters, each
cluster possibly representing one category of meaning. Results in solutions with less than
six clusters are not distinctive enough to establish clusters in which role-identities
share an unique denotative meaning. On the other hand, solutions with more than six
clusters resulted in very small clusters, containing extreme ratings that do not address
denotative classes of role identities.</p>

<p>However, not every emergence of a single-item cluster is
an indicator that the upper limit of clusters is reached.  In my particular example a
single-item cluster emerged in the U.S. data. This single-item cluster contained the
identity of &quot;God&quot; and might be interpreted as having hyper-authority
denotation.  Due to its extremity, this one-item cluster established itself very
early in the process of increasing the number of clusters. There is another indicator for
this exceptional quality of this singe-item cluster: even after raising the number of
clusters, new clusters established meaningful categories.</p>

<p>The 6-cluster solution using the K-means method in SYSTAT
(Wilkinson, 1988) is interpreted as offering the most robust and distinctive clusters for
both, the German and the U.S. data. Cluster means of the final six cluster solution are
presented in table one and table two.&quot;.<endnotenumber>7</endnotenumber></p>

<h2>Table One: Cluster Means of the German 6 Cluster Solution.
Cluster Descriptions of <i>Winner</i>, <i>Deviant</i>, <i>Loser</i> and <i>Family</i>
are First Tentative Labellings.</h2>

<table width="100%">
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">German</td>
    <td style="border: 1px solid"><i><font face="Times New Roman"
 size="-2">Winner</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Deviant</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Loser</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Authority</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Sexuality</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Family</i></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">E male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.16</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-1.28</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .31</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.93</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.34</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.40</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">P male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.22</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.58</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .40</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.52</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.03</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .52</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">A male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.79</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.88</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .19</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.07</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.68</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.45</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">E female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.33</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-1.01</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .11</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.11</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.42</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.23</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">P female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.23</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.61</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .34</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.65</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.15</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .26</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">A female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.72</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.77</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .16</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.18</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.68</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.53</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n=420</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 55</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 83</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 77</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 87</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 81</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 37</td>
</tr>
</table>

<h2>Table Two: Cluster Means of the U.S. 6 Cluster Solution. Cluster
Descriptions of <i>Winner</i>, <i>Loser</i> and <i>Family</i> are First Tentative
Labellings.</h2>

<table width="100%">
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">U.S.</td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Sexuality</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Loser</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Authority</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Winner</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">God</i></td>
    <td style="border: 1px solid"><i><font face="Times New Roman" size="-2">Family</i></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">E male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-1.60</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .41</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.97</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">0.89</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">3.02</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.87</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">P male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .31</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-1.01</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.81</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.25</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">3.59</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-.36</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">A male</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.04</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .63</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-.36</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.13</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .50</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.25</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">E female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-1.46</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .04</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.15</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.00</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">3.59</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.97</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">P female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .01</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .72</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.91</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.35</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">3.59</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-.19</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">A female</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">.93</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .49</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">-.22</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.07</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">- .25</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">1.10</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n=420</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 98</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 74</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 111</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 60</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 1</td>
    <td style="border: 1px solid"><font face="Times New Roman" size="-2">n = 76</td>
</tr>
</table>


<p>Coupling of Qualitative and Quantitative Methodology
to Overcome Problems Associated with the Culture Centeredness of the Researcher</p>

<p>Being bicultural, does not necessarily prevent
culture-centeredness, it only creates the possibility of committing multiple errors. Since
it is more likely to be caught in two cultural traps simultaneously, the researcher
experiences dissonant information. This creates enough confusion for the culturally aware
researcher to realize the contradiction. Bicultural researchers are caught in a
dialectical dilemma (Hegel 1817). Culture-centric theses supported in one culture are
contradicted by antithetical information in the other culture. This contradiction,
finally, leads to a synthesis. Trusting in one's own bicultural background should not
spare the use of proper methodology in cross-cultural research. When emergent properties
are interpreted in an inductive approach, all cultures involved should be represented.
This can be done either by using a scholarly interpreter of each culture, and/or with
secondary data of all cultures.</p>

<p>As a bicultural interpreter of the listing of the U.S.
sexual-erotic cluster, I was challenged between interpreting this cluster as deviant or as
sexual-erotic. Labeling the U.S. sexual-erotic cluster as deviant proved to be a German
culture-centric interpretation. The label sexual-erotic, supported by the second sample of
U.S. raters, is demonstrated to be an U.S. culture-centric interpretation. I finally
considered the label <i>vice</i>.<endnotenumber>8</endnotenumber> Vice reflects
deviance and sexual-eroticism of the large overlapping component and appears to be less
culture-centric. The vice concept is supported by role-identities like <i>pimp</i> or <i>slut</i>
that are located in the overlapping component.</p>

<p>Verification of Denotative Meaning Using Independent
Samples</p>

<p>The interpretation of explorative cluster analyses follows
a grounded theory approach (Glaser &amp; Strauss 1967), where the structure in the data
leads to theoretical reasoning. Reading through the listing of role-identities in an
empirically generated cluster is the first step to identifying denotative roots for
empirically generated clusters. A similar inductive approach is typically used when
factors are named in a factor analytical model. Naming clusters in an explorative cluster
analysis, can be seen as labeling emergent meaning. Assigning labels, like <i>sexual-erotic
</i> or <i>authoritative</i>, to empirically generated clusters is an interpretative
task for the researcher, and thus a potential source of invalidity.</p>

<p>How should we test the validity of theoretical results
that are obtained by inductive reasoning? Strictly speaking, we cannot test theoretical
assumptions derived from one set of data with that same set of data. There are several
ways to get around this problem. Methods, like bootstrapping (Bollen &amp; Stine 1992),
using random sampling of the sample, are not applicable here. Instead, the labeling of the
emergent clusters is tested by independent second samples.  The labels authorities,
sexual eroticism, family, winner,and losers of the U.S. cluster solution of is tested with
North American experts and undergraduate students.The One-Way ANOVA analysis procedure is
used to test if the explained variance of the cluster label ratings differ across the six
cluster solution. This analysis of variance tests if different means are equal. Finally,
it has to be determined if the highest cluster label rating is indeed empirically applied
to role-identities in the cluster that initially received this label.</p>


<h2>Expert Raters</h2>

<p>Twenty-four graduate Ph.D. students who are knowledgeable
about the concepts of identity and authority helped to identify denotative meaning of the
authority concept. They received a complete list of all 420 role-identities and rated
every role-identity as <i>definitely an authority</i>, <i>maybe an authority</i>, or <i>definitely
not an authority</i>. ANOVA analysis revealed that all clusters have significantly
different (F=6.43) authority ratings.  The null hypothesis that ratings of
authoritativeness are equal in all clusters is clearly rejected in the U.S. cluster
solution (alpha=.01). On the average, role-identities in the cluster, that I  labeled
the authority cluster, also has the highest authority ratings.</p>

<p>As in the case of authority concepts, a second sample is
used to test the denotative quality of the sexual-erotic cluster. Again, expert raters
received a list of all 420 role-identities, and identified sexual-erotic denotation on a
three-point scale: as <i>definitely sexual-erotic</i>, <i>maybe sexual-erotic</i>, or <i>definitely
not sexual-erotic</i>.<endnotenumber>9</endnotenumber> A Simple factorial ANOVA model
shows that mean sexual-erotic ratings are significantly different (F=11.13)  in each
cluster. The null hypothesis that the sexual quality is equal in all clusters is rejected
(alpha=.01).</p>


<h2>Undergraduate subjects</h2>

<p>The clusters of sexual eroticism and authority were of my
greatest theoretical interest (Schneider 1993, 1994, 1996, 1998).  They were first
tested with expert raters. However, I also want to test the remaining clusters of <i>family</i>,
<i>winners</i>, and <i>losers</i>.  For this test I used North American
undergraduate students. <i>Family</i>, <i>winners</i>, and <i>loser</i> ratings
significantly (alpha=.01) differentiated each cluster. The cluster of <i>winners</i>
also received the highest <i>winner</i> rating. In the same pattern, the role-identities
of the <i>family</i> cluster were rated highest in being a family identity.  This
did not work for the role-identities of the <i>loser</i> cluster.</p>

<h2>Table Three: U.S. Cluster Label Ratings by Clusters. Alpha of F values &lt;.01.</h2>

<table border="0" style="border: medium solid" width="723" align=center
  <tr>
    <td style="border: thin solid" width="188"><b>Clusters initially labeled as</b></td>
    <td style="border: thin solid" width="161"><b>Number of subjects</b></td>
    <td style="border: thin solid" width="107"><b>ANOVA (F)</b></td>
    <td style="border: thin solid" width="251"><b>Cluster that received the highest rating</b></td>
</tr>
  <tr>
    <td style="border: thin solid" width="188"><b>authority</b></td>
    <td style="border: thin solid" width="161">24*</td>
    <td style="border: thin solid" width="107">6.4</td>
    <td style="border: thin solid" width="251"><b>authority</b></td>
</tr>
  <tr>
    <td style="border: thin solid" width="188"><b>sexual eroticism</b></td>
    <td style="border: thin solid" width="161">16*</td>
    <td style="border: thin solid" width="107">11</td>
    <td style="border: thin solid" width="251"><b>sexual eroticism</b></td>
</tr>
  <tr>
    <td style="border: thin solid" width="188"><b>family</b></td>
    <td style="border: thin solid" width="161">46</td>
    <td style="border: thin solid" width="107">14</td>
    <td style="border: thin solid" width="251"><b>family</b></td>
</tr>
  <tr>
    <td style="border: thin solid" width="188"><b>winners</b></td>
    <td style="border: thin solid" width="161">13</td>
    <td style="border: thin solid" width="107">150</td>
    <td style="border: thin solid" width="251"><b>winners</b></td>
</tr>
  <tr>
    <td style="border: thin solid" width="188"><b>losers</b></td>
    <td style="border: thin solid" width="161">24</td>
    <td style="border: thin solid" width="107">133</td>
    <td style="border: thin solid" width="251"><b>sexual eroticism</b></td>
</tr>
<tr><td colspan=4>&nbsp;</td></tr>
<tr><td colspan=4>* Each expert rater shared the task of  rating the
complete set of 420 identities.</td></tr>
</table>



<p>The cluster of identities, that I labeled as losers, did
in fact receive the highest sexual eroticism rating by U.S. undergraduates. This clearly
indicates the need to use empirical verification in the labeling of emergent meaning.
Presenting the loser cluster to my graduate seminar, students offered different
suggestions for labeling this cluster. If one of these suggestions were accepted, the
labeling of emergent meaning in (what I originally called) the loser cluster has to be
tested again with a different sample.</p>



<h2>Constructing the Ideal Type of Authority</h2>

<p>Having  established the methodology of generating
clusters of denotative meaning, I want to focus on the cluster of authority to demonstrate
the usefulness of my methodology in the application of cross-cultural comparison.</p>

<p>The definition of authority in the literature (Weber 1930;
Adorno et al, 1950; Barnard 1966; Milgram 1974; Eysenck &amp; Wilson 1978; Giddens 1991)
allows an operationalization of the authority concept in terms of EPA profiles. Being
coerced is unpleasant, and generally leads to resentment toward the coercer. If the
other's coercion is seen as legitimate, then he or she is an authority, and may be
evaluated positively. Legitimation of authority means that the authority's power is
understood by others, and need not be communicated through expressive actions. The ideal
type (Weber, 1946) for an authority is someone who is potent (P+), positively evaluated
(E+), and not expressive (A, or A-).</p>

<p>U.S. and German undergraduates largely agree on what they
might classify as an authority concept. Authority-clusters in both cultures are quite
similar, and 79% of the German authoritative role-identities are represented in the U.S.
authority cluster.</p>


<h2>Attempted Dichotomization and Trichotomization of
EPA Pattern</h2>

<p>In the case of authorities there is a simple dichotomous
(high and low) EPA pattern. Not every denotative concept is described by such a simple
dichotomous or a trichotomous (high, neutral, and low) EPA pattern.  If denotative
categories would follow simple pattern, it would be much easier to identify them just by
using a simple sorting processes. Clusters do not necessarily have extreme means on all
three dimensions and are, therefore, harder to spot in the data. Since cluster means are
continuous rather than dichotomous or trichotomous, they allow precise borderlines that
emerge from the data. Accounting for all variations, cluster analytical classification
does not lose role-identities, even if they do not follow a simple pattern that is
identifiable by just looking carefully in the data.</p>

<p>The authority cluster was an interesting exception. Here
role-identities in the U.S. data indeed followed a trichotomous scheme of high evaluation,
high potency, and neutral ratings in activity.  It should be stressed that following
this trichotomous EPA pattern the data does not indicate a &quot;better&quot; cluster
solution. It only simplifies qualitative interpretation and comparison with the
literature.</p>


<h2>Qualitative Identification of Cross-cultural
Differences in Denotative Meaning</h2>

<p>In the cross-cultural analysis of abstract relations between denotative categories, the
logical relation of overlapping of meaning becomes relevant. In the comparison of the two
cultures multiple categorical membership become possible. This leads to three logical
relations in the comparison:</p>

<p>One category of inter-cultural agreement:</p>
<blockquote>
      <p>Common components: cultures agree on denotative meaning of a concept
      to the degree to which categories of the same denotative meaning share a common component.
</p>
</blockquote>
  

<h2>Two categories of inter-cultural disagreement:</h2>

    <blockquote>
      <p>(a) Non-common components: are the remainder of common components. They
      indicate cross-cultural disagreement in denotative meaning. The term &quot;non-common
      components&quot; is used if the category of the concept of interest exists in both
      cultures.</p>
      <p>(b) Overlapping components: if the same concept reflects different
      denotative categories in different cultures, denotative meanings for this category
      overlaps.</p>
</blockquote>


<p>Common components that demonstrate cultural similarity of
denotative meaning and overlapping components that represent dissimilarity are represented
for the complete six-cluster solutions of the U.S. and German role-identities in a venn
diagram in <inline><graphic>cluster.jpg</graphic><text>Fig. 3.</text><caption>Figure 3: Venn Diagram of the complete six cluster solution with inter-cultural common components and overlapping clusters.</anchor></inline></p>



<p>Focusing only on the authority clusters and the sexual eroticism clusters and their
relations, we see cross-cultural similarities and differences in the six cluster solutions
of both cultures. For the authority concept we can identify a large common component in
which 61% of the 87 German authorities are also categorized as authorities in the U.S.
data.  Compared to the 25% of German sexual-erotic identities that are classified as
sexual-erotic in the U.S., this can be judged as being a large common component.</p>

<p>The large non-common component of the German and U.S. sexual-erotic cluster is an
indicator of cross cultural differences in the sexual-erotic domain. The large overlapping
component of the U.S. sexual-erotic cluster with the German deviant cluster is another
indicator of cross-cultural differences in the classification of sexual-erotic concepts.
The majority (59) of all the U.S. sexual-erotic concepts (98) are categorized as deviant
in the German sample.</p>


<h2>Authoritative Role-identities of the Common
Component</h2>

<p>Even when clusters indicate the same denotation, concepts
in the clusters are not fully identical. In the cross-cultural comparison, we have to make
sure that categories, to be compared, are established by the same concepts.  In a
cross-cultural comparison the subset of a common component should be used.  In the
common component, by definition there are the same concepts; therefore, the strength of
cross-cultural difference can be fully quantified.</p>

<p>The ideal type of authority, as derived from the
literature and operationalized as potent (P+), positively evaluated (E+), and not
expressive (A-), is used for cross-cultural comparison. As described above, the
authoritative character is tested by a second sample of authority ratings. Another test,
if this ideal type is reflected in the authority cluster, can be done in the comparison to
role-identities that did not classify as authoritative.<endnotenumber>10</endnotenumber> Table Three shows that ratings in the German and U.S. authority cluster fulfill the
authoritative ideal type significantly (alpha=1%) stronger than the remainder. This is
true for both male and female undergraduates. Means indicate that authority clusters show
higher evaluation, more potency and less activity than role-identities that are not
identified as authoritative.</p>

<p>There are strong cross-cultural differences in the degree
to which authoritativeness of the same role-identity is agreed upon. The affective
representation of authoritative role-identities is significantly (alpha=5%) more pleasant
and powerful and less active for North Americans than Germans. In this way, ratings of
U.S. undergraduates fulfilled the ideal type of authority to a greater extent than the
German subjects.</p>

<h2>Table Four: German and U.S. Means of the Common Component of
Authorities (n=53) and Non-authorities (the Remainder n=275) for Males and Females in
Brackets.</h2>


<table width="100%">
  <tr valign="TOP">
    <td style="border: 1px solid"></td>
    <td colspan="2" style="border: 1px solid"><font face="Courier New"><b>German males (females)</b></td>
    <td colspan="2" style="border: 1px solid"><font face="Courier New"><b>U.S. males (females)</b></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Courier New"><b>Dimensions</b></td>
    <td style="border: 1px solid"><font face="Courier New"><b>authority</b></td>
    <td style="border: 1px solid"><font face="Courier New"><b>non-authority</b></td>
    <td style="border: 1px solid"><font face="Courier New"><b>authority</b></td>
    <td style="border: 1px solid"><font face="Courier New"><b>non- authority</b></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Courier New">Evaluation</td>
    <td style="border: 1px solid"><font face="Courier New">0.87 (1.05)</td>
    <td style="border: 1px solid"><font face="Courier New">-0.20 (-0.08)</td>
    <td style="border: 1px solid"><font face="Courier New">1.17 (1.37)</td>
    <td style="border: 1px solid"><font face="Courier New">-0.38 <font
    face="Courier New">(-0.21)</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Courier New">Potency</td>
    <td style="border: 1px solid"><font face="Courier New">0.51 (0.64)</td>
    <td style="border: 1px solid"><font face="Courier New">0.13 <font
    face="Courier New">(0.22)</td>
    <td style="border: 1px solid"><font face="Courier New">0.88 (0.98)</td>
    <td style="border: 1px solid"><font face="Courier New">-0.26 <font
    face="Courier New">(-0.02)</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><font face="Courier New">Activity</td>
    <td style="border: 1px solid"><font face="Courier New">-0.01 (0.09)</td>
    <td style="border: 1px solid"><font face="Courier New">0.57 <font
    face="Courier New">(0.55)</td>
    <td style="border: 1px solid"><font face="Courier New">-0.37 <font
    face="Courier New">(-0.22)</td>
    <td style="border: 1px solid"><font face="Courier New">0.70 <font
    face="Courier New">(0.66)</td>
</tr>
</table>

<h2>Sub-cultural Differences as a Measuring Rod for
Cross-cultural Differences</h2>

<p>It should not be taken for granted that every reader has a
language-like comprehension of EPA profiles. It takes quite some experience in working
with EPA profiles to actively communicate affective meaning trough EPA profiles. Then,
corrections in conversations like:&quot;yes, like a <i>mugger</i>, but less active&quot;
are helpful to bridge cultural barriers between researchers.</p>

<p>Since cross-cultural comparison of EPA ratings might
appear abstract to the reader, I want to introduce another measuring rod. Within our
native culture, we are intuitively aware of gender differences. I do not want to imply
that gender differences are obvious and always available to us, but they are more
accessible than cross-cultural differences: especially for those who are not truly
bicultural.</p>

<p>Taking the information of table four, I calculated
cross-cultural differences for males and females and averaged them. Cross-gender
differences on each dimension are averaged in both cultures (see table five). Mean cultural
differences are about twice as strong on each dimension as mean gender differences. This
suggests that it is twice as hard to understand the authoritative concept in another
culture as it is to understand a gender difference in our native culture.</p>

<h2>Table Five: Comparing Cultural and Gender Differences of EPA
Ratings in the Authoritative Common Component.</h2>

<table width="100%">
  <tr valign="TOP">
    <td colspan="3" style="border: 1px solid"><b>Cross-Cultural  Differences</b></td>
    <td colspan="3" style="border: 1px solid"><b>Cross-Gender    Differences</b></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid"><b>males</b></td>
    <td style="border: 1px solid"><b>females</b></td>
    <td style="border: 1px solid"><b>mean</b></td>
    <td style="border: 1px solid"><b>Germany</b></td>
    <td style="border: 1px solid"><b>U.S.</b></td>
    <td style="border: 1px solid"><b>mean</b></td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid">.30</td>
    <td style="border: 1px solid">.32</td>
    <td style="border: 1px solid">.310</td>
    <td style="border: 1px solid">.18</td>
    <td style="border: 1px solid">.20</td>
    <td style="border: 1px solid">.190</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid">.37</td>
    <td style="border: 1px solid">.34</td>
    <td style="border: 1px solid">.355</td>
    <td style="border: 1px solid">.13</td>
    <td style="border: 1px solid">.10</td>
    <td style="border: 1px solid">.115</td>
</tr>
  <tr valign="TOP">
    <td style="border: 1px solid">.36</td>
    <td style="border: 1px solid">.31</td>
    <td style="border: 1px solid">.325</td>
    <td style="border: 1px solid">.10</td>
    <td style="border: 1px solid">.15</td>
    <td style="border: 1px solid">.125</td>
</tr>
</table>

<h2>CONCLUSION</h2>

<p>The U.S. data was collected in 1978.  Although new
partial data collections with undergraduates at Indiana University by Heise and Schneider
indicated that sentiments stayed astonishingly stable, the time difference to the German
sample endangers the validity of a comparison of both contemporary cultures. However, this
article is mainly concerned about the methodology that can be used for cross-cultural
comparison and not the qualitative difference in both cultures. In establishing a strong
basis for a valid method for cross-cultural comparison of affective meanings and their
structural properties, I hope to encourage researchers to collect new data that allow a
focus on the nature of the cross-cultural differences and similarities.</p>

<p>Using the convenient terminology of  &quot;German and
U.S. culture&quot;, I am aware of the limited representativeness of the undergraduate
student population. Again, I do not find this limitation relevant for the development of
the structural categorization and the development of a methodology for cross-cultural
comparison. The reader should judge for herself to which degree she accepts the
generalizability of the samples.</p>

<p>Higher order denotations emerge from data on affective
meaning. What I call  Denotative Clusters of Affective Meaning can be interpreted as
structural categories. According to structural symbolic interactionists (Heise
1987, 1998, MacKinnon 1994, Stryker 1992, social structure
has an extensive effect on how we create our subjective meaning.  So we should not be
surprised if macro structural properties are represented within data on affective meaning.
I describe a method that identifies such structural meaning in the three dimensional
semantic differential ratings of Charles Osgood. Since cultural differences often imply
structural differences, my Denotative Clusters of Affective Meaning should vary
cross-culturally. Further, their variation in composition can be used as an indicator for
cross-cultural differences. If Denotative Clusters of Affective Meaning are used in direct
numerical comparison, their composition should not vary.  Controlling the cluster
composition for cross-cultural comparison, I suggest conducting the comparison within
inter-culturally common components.</p>

<p>The basic idea is, to the degree that EPA ratings are
similar, corresponding role-identities share one denotation. This idea is tested with
German and U.S. data. Cluster analysis is seen as a valid quantitative procedure to
translate this theoretical reasoning into a quantitative methodology. Results of both
cluster analyses are compared with the initial theoretical assumption that role-identities
within clusters carry the same denotation and that the denotation of each cluster will be
different. This comparison is tested by a second independent sample, verifying the
denotative quality of the cluster.</p>

<p>The authority concept is derived from the literature.
After the ideal type of the authoritative role-identity is defined and tested with a
second sample, it is compared across cultures by checking which of the cluster means
follow this ideal pattern to a greater extent. The emergent trichotomous classification of
the authority cluster can be seen as a special case where trichotomous classification was
successful. Classifications with cluster analytical methods are far superior to
dichotomous or trichotomous classifications where clusters must have extreme means on all
three dimensions simultaneously.</p>

<p>Representing cross-cultural agreement and disagreement on
denotations. Venn diagrams give an overview of common components and inter-cultural
overlapping.  Common components indicate cross-cultural agreement in denotative
classification. Overlapping components are established by clusters of culturally different
denotation. They indicate cross-cultural disagreement in the classification.
Non-overlapping categories are another relation that indicates cross-cultural differences
in the structural classification.  Since solutions with large overlapping components
lead to large sections of overlapping categories, both indicators of cross-cultural
differences are positively related.</p>

<p>Common components only control for denotative agreement.
Affective meaning within these intercultural categories can still vary. In my exemplary
application of the ideal typical EPA pattern of authoritative role-identities, I
demonstrate that U.S. subjects followed the EPA pattern to a stronger extent than German
subjects.</p>

<p><a name="BM_1_"></a>Differences in the sexual-erotic domain did not come as a surprise.
The stigmatization of sexual- erotic concepts in the middle class culture of young North
Americans was already identified in other research. Cross-cultural studies of Weinberg et
al. (1995), Muehlenhard and Cook (1988) and Schwartz (1993) show that young American
subjects stigmatize the sexual-erotic domain. Because of this stigmatization they
experience negative emotions of guilt and shame when they confirm their sexual-erotic
identities (Schneider 1996, 1999; Schwarz 1993;Weinberg et al. 1995). This was not the
case for young Europeans that were compared in these studies.</p>

<p>Fore some people more surprising was the similarity of the authority concepts in both
cultures. Traditionally Germans are stereotyped for their love of authority. This
prejudice was scientifically funded in research conducted right after WWII (McGranahan
1946). It was later supported by research on national types (Adorno 1950). Finally, the
Milgram Experiment (1969) woke up the academic community and demonstrated that being
authoritativeness is not a German trait. North Americans, with very different social
statuses, dramatically demonstrated their obedience to authority.</p>

<p>Just as the Milgram experiment, my study disconfirms the results of early post WWII
research on authority. I find that comparing the affective representation of authority in
both cultures, young North Americans followed the ideal type of authority just like the
German subjects, even a little more. U.S. subjects did assign authorities more power.
Despite such high potential for coercion authorities were seen by North Americans slightly
more positive. Further, the power of authorities was understood by U.S. subjects to such
an extent that there was no need for authorities to expressively communicate their
potential potency.</p>

<p>These results are in line with newer findings of the 80s and 90s. In his study of the
erosion of institutional authority, Roland Inglehard (1997) uses the concepts of
materialism versus postmaterialism. &quot;Postmaterialists feel less need for strong
authority than do Materialists. Moreover, Postmaterialists place relatively strong
emphasis on self-expression - a value that inherently conflicts with the structure of
hierarchical bureaucratic organizations&quot;(p.299). North Americans, for example, were
much more confident in the police, and showed substantially more national pride that
Germans. Both variables indicate traditional authority. Taking the effects of all
indicator variables into account, Inglehard found that North Americans and West Germans
increased their dislike for traditional authority from 1981 to 1990. Compared to North
Americans, West Germans not only started 1981 with less preference for traditional
authority, West Germans also increased their dislike stronger in the following years.</p>

<p>Combining the findings of Inglehard with previous research on authority in Germany
suggests a continuous delegitimation of authorities since WWII. Contrary, one might argue
that the ideological stance about the German authority concept, employed by U.S.
scientists in early post WWWII research, was responsible for a substantial experimenter
effect (Rosenthal 1964). If, like in the construction of Adorno's construction of the
F-scale, no empirical data of the studied population is used, the potential for an
researcher effect is especially high. However, I do not want to fall into a sociology of
science mode and examine the potential bias in the early post WWII research in the U.S.
For now, I would like to accept the premise that young Germans in the 1940s and 1950s were
indeed more willing to attribute power to authorities, to legitimize this power and
consequently assign high status to authorities.</p>

<p>Taking this premise, the change of values legitimizing authority in Germany was
substantial (Habermas 1977). What where the causes of this change? Cross-cultural
differences in the education system can be seen as one possible structural explanation for
a change of the authority concept in the German culture. The re-education program in West
Germany after World War II (Fischer, 1978; Tent, 1982), imposed by the Allied Forces,
initiated a cultural change that influenced attitudes toward authorities. German pupils
were systematically encouraged to develop anti-authoritarian standpoints. The curriculum
of politics and history classes in the German school system puts an emphasis on the
argument that the glorification of authority and the obedience of the masses caused the
horror of the Third Reich. Movies of open brain surgery, conducted on living subjects by
Nazi physicians, were still used in the 1960s and 1970s as negative conditioners for
pupils as early as in the eight grade. The re-education was successful in changing
legitimation rules for authorities, and thereby make Germans view their authorities less
authoritative.</p>

<p>As reflected in Inglehard&#146;s study, the majority of young Americans still engage in
a patriotic glorification which does not allow them to self critically reflect upon the
legitimacy they assign to their leaders. My results support recent findings in
cross-cultural research and seem to indicate a more conservative trend in the U.S. of
legitimizing authorities. I argue that since WWII young people in the U.S. did not change
their attitudes towards authority as dramatically as Germans. However, it is obvious that
the reasons for structural cross-cultural differences are highly complex. Here, I only
used one example to illustrate how structural variables differ in both nations, and how
these structural differences cause cultural change. The focus of this paper was to
establish a sound methodology of cross-cultural comparison and to indicate areas of
cross-cultural differences and similarities. Adding more evidence with new methodology, my
results might inspire researchers in the fields of philosophy, history, sociology and
political sciences to more thoroughly research potential reasons for the cross-cultural
differences that I found in my analysis.</p>
</body>

<endnotes>

<endnotetext><num>1</num><p>Further
details on the data collection instrument and a list of all mean ratings by U.S. males and
females are available in Heise and Lewis (1988).</p></endnotetext>


<endnotetext><num>2</num><p>&quot;Gymnasium&quot; is the German educational equivalent to the
U.S. high school, a prerequisite for entering university that lasts two years longer than
the U.S. High school. My personal experience in the German &quot;Gymnasium&quot; and
&quot;Grundstudium&quot;, and my experience teaching U.S. undergraduates informed the
construction of a German equivalent of an U.S. undergraduate in Germany. Prof. Frank Banta
who taught German at Indiana University was another extremely helpful resource. He was
significantly involved in the reformation of the German education system after 1945. Thus,
interviewing 143 pupils of the eleventh and twelfth grade of the &quot;Gymnasium&quot;,
and 294 students of the &quot;Grundstudium&quot; gave a close equivalence of the U.S.
undergraduate in age structure and education.</p></endnotetext>


<endnotetext><num>3</num><p>Dr. Paul Jackson, assistant professor in the English (Anglistik)
department at Mannheim University performed the backtranslation.</p></endnotetext>


<endnotetext><num>4</num><p>Herman Smith indicated astonishing cross cultural differences in
concentration spans (or in obeying authorities?). In his study of Japanese students
(Smith, Takanori, and Umino 1991) he was able to give students the complete list of
stimuli, which they willfully rated.</p></endnotetext>


<endnotetext><num>5</num><p>Pascal is a registered trademark of Borland International</p></endnotetext>


<endnotetext><num>6</num><p>This clustering algorithm is described in detail by Hartigan
(1975).</p></endnotetext>


<endnotetext><num>7</num><p>A complete listing of the role-identities and their cluster
memebership in both cultures is provided in Schneider 1997:191-201.</p></endnotetext>


<endnotetext><num>8</num><p>The term &quot;vice&quot; is relatively new in the U.S.  
English language and was, according to Black (1980), first used in the scholarly
literature by Schur (1965). Vice is characterized by role-identities engaging in gambling,
prostitution, drug abuse and homosexuality (Schur, 1965). Since vice occurs within a
deviant sub-culture often in mutual consensus, it is also referred to as &quot;crime
without victim&quot;(Black, 1980).</p></endnotetext>


<endnotetext><num>9</num><p>The questionnaire for sexual-erotic denotation of
role-identities proves to be an instrument with high validity. In a pretest with twelve
graduate students, the wording &quot;sexual&quot; instead of &quot;sexual-erotic&quot; was
used. This change of wording, adding &quot;erotic&quot;, is suggested by participants of
the pretest as more accurate. It avoids the gender denotation of &quot;sex&quot;. Because
of the wider denotation of sexual, compared to sexual-erotic, role-identities are more
likely to classify as sexual than sexual-erotic. As expected, average ratings in the
pretest are higher. Specifying &quot;sexual&quot; as &quot;sexual-erotic&quot; has a
significant (alpha=1%) effect. Using average sexual ratings of the pretest as a covariate
in the ANOVA model, the null hypothesis that ratings of both samples are equal is
rejected.</p></endnotetext>


<endnotetext><num>10</num><p>Alternatively we might compare cluster means of the authority
cluster to dimension means; the EPA means of all concepts. Here we reach similar
conclusions.</p></endnotetext>
</endnotes>
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</ixml>

