EXAMINING THE SCALABILITY OF INTIMACY PERMISSIVENESS
IN TAIWAN
Pei-shan Liao and Su-hao Tu∗
Academia Sinica
This paper examines the scalability of intimacy permissiveness measures in Taiwan by
using island-wide survey data. Among models for scaling analysis, Guttman scaling model
and two latent class models —the latent-distance model and Goodman’s scale model— were
employed to examine the scalability of the scale measures. The data were drawn from the
2002 Taiwan Social Change Survey (Questionnaire No. 2), conducted by Academia Sinica. A
total of 1,983 completed interviews were obtained. This study found that the measures of
intimacy permissiveness scale were able to form a reasonable Guttman-type scale based on
three scaling models. In order to further validate the intimacy permissiveness scale, we
conducted multinomial logistic regression of some socio-demographic variables on the scale.
Gender and age differences were found consistent with previous studies and mostly
reconfirmed the appropriateness of the measures of intimacy permissiveness scale designed
for the Taiwan Social Change Survey.
Key words: Intimacy permissiveness scale, Guttman scale, latent class model
INTRODUCTION
The issue of permissiveness toward premarital sexual behaviors for different age groups,
gendered subjects, at different levels of intimate relationships, has been a focus among
researchers. Considering the progressive nature of sexual behaviors, the items designed to
measure attitudes toward premarital intimacy permissiveness have been found to form a
reliable Guttman-type scale (Reiss, 1964; Hampe and Ruppel, 1974). The premarital sexual
permissiveness (PSP) or intimacy permissiveness scale (IPS), including original items or
revisions, has been widely used and with little attention to its reliability and validity.
Revisions of these scales have, nonetheless, lacked for methodological examination. Few
studies have thoroughly examined the appropriateness of applying such Western-driven
measures to different social contexts and cultures, particularly in societies that are generally
conservative toward sexual attitudes.
Although it has been claimed that a quiet revolution regarding sexual behaviors and
attitudes has occurred in Taiwan (Rindfuss and Morgan, 1983), greater sexual freedom did
not necessarily lead to more intimate behaviors among acquaintances, nor to more premarital
sex among adult women in particular (Chang, 1996). On account of the tenuous relation
between attitudes and behaviors (see Simpson and Gangestad, 1991), it is essential to explore
whether the attitudes of people in Taiwan toward various forms of intimate behavior remain
conservative. In order to gain a better understanding of Taiwanese attitudes toward permitting
intimate behaviors, an applicable scale is needed.
∗
Please direct all correspondence to Pei-shan Liao, email to: psliao@gate.sinica.edu.tw, or write to Center for
Survey Research, Academia Sinica, 128 Sinica Road, Sec. 2, Taipei 115, TAIWAN.
1
This paper aims to examine the scalability of intimacy permissiveness measures in
Taiwan by using island-wide survey data. Scaling analysis for a series of Guttman-type
measures was conducted corresponding to the scalability of commonly-used items in previous
studies. In addition, alternative latent class models are employed to compensate for the lack of
statistical justification in Guttman scaling analysis, while retaining the hierarchical nature of
the scaling items (see Feick, 1987). After the scalability of the intimacy permissiveness
measures were confirmed, we examined the association between sociodemographic
characteristics, in particular gender and age groups, and the intimacy permissiveness scale
(IPS) to further validate the chosen scaling model, based on theoretical explanations proposed
in the previous literature. The following sections review studies on premarital intimacy
permissiveness and discuss the use of Guttman scaling analysis as well as latent class models.
We then present the results of the Guttman scaling analysis and of the alternative models and
discuss the methodological appropriateness of intimacy permissiveness measures.
INTIMACY PERMISSIVENESS
The intimacy permissiveness scale (IPS) was developed by Christensen and Carpenter (1962).
By using a ten-item Guttman-type scale regarding intimacy permissiveness, their study
revealed the association of sexual norms with intimacy attitudes and behaviors. A restrictive
sex norm was found to reduce intimacy permissiveness. Gender differences were revealed
with males being more permissiveness. The reliability and scaling criteria of such Guttmantype scale, however, are not provided.
In a similar vein, Ira Reiss (1964) developed a premarital sexual permissiveness (PSP)
scale. Following the establishment of a set of mutually exclusive categories that described the
sexual beliefs of American youth, two 12-item scales targeted to each gender comprising the
same items were developed. Reiss further examined the reliability of the PSP scale based on a
variety of scaling analysis techniques. Considering different dimensions of affection and a
variety of sexual behaviors, the original twelve items were proved to meet the criteria for an
acceptable Guttman scale. Although both IPS and PSP were claimed to be Guttman-type
scales, a comparison of the measures concluded the former as multidimensional and, in turn,
not an acceptable Guttman scale regarding intimacy permissiveness (Hampe and Ruppel,
1974).
Intimacy permissiveness scales have been widely used in research concerning intimate or
sexual attitudes. Revisions based on those items, or on similar ideas, have been commonly
applied with a focus on young adults or adolescents (cf. Li, 1983; Sprecher, et al. 1988).
Sprecher and her colleagues (Sprecher, et al. 1988) revised Reiss’s items by changing sexual
behaviors and relationship stages in the scale, and designed experimental randomization to
examine the levels of permissiveness regarding gender and age of targets and/or subject. With
other revisions, similar intimacy permissiveness scales have been used to examine premarital
sexual standards among college students in different countries (Li, 1983; Sprecher, 1989;
Sprecher and Hatfield, 1996).
Several studies have found sexual double standards, in which women were more
conservative toward premarital sexuality while men were more sexually permissive
(Kraaykamp, 2002; Li, 1983; Sprecher, 1989; Sprecher, et al. 1988). A review for both
quantitative and qualitative studies on sexual double standards suggested that such issues may
be multidimensional, and evidence of them may not always be found (see Crawford and Popp,
2003). Double standards of that type may only be found in research focusing on very casual
relationships rather than affection-involved relationships (Sprecher and Hatfield, 1996). It
may also be expected that measures of intimacy permissiveness may well form a
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unidimensional, rather than multi-dimensional (Hampe and Ruppel, 1974), Guttman-type
scale when affection relationships are not involved. In addition to gender, research examining
adults’ sexual attitudes over decades indicated that older people were more conservative while
singles and the better educated were inclined to approve premarital sexuality in a casual
relationship (Kraaykamp, 2002).
Sociodemographic characteristics have been routinely examined in previous studies
regarding premarital intimacy permissiveness. However, few were concerned with the
scalability of the intimacy permissiveness scale. Reiss’s original paper (1964) and a study
comparing two Guttman scales on premarital intimacy permissiveness (e.g. Hampe and
Ruppel 1974) have confirmed the scalability of the Reiss PSP measures by using Guttman
scalogram analysis, while Christensen and Carpenter’s (1962) IPS was found to be
multidimensional. Other revisions of the IPS or PSP scales have, nonetheless, lacked
methodological examination. These Guttman-type measures of intimacy permissiveness have
been treated mainly as individual indicators, on either the nominal or ordinal level
(Kraaykamp, 2002; Rosenthal and Smith, 1997), to be summed up or taking an average as a
composite index (Kornreich, et al. 2003; Li, 1983; Sprecher and Hatfield, 1996, Sprecher, et
al. 1988), or creating indices by collapsing the responses across different types of
relationships (Sprecher, 1989). As a progression from the least intimate to more intimate
behaviors has evolved (Rosenthal and Smith, 1997), intimate behaviors can be viewed as a
hierarchy. In studying behavioral hierarchies, it is more meaningful to examine a crossclassification of observed data by using Guttman scaling analysis rather than individual
variables (Feick, 1987).
In addition to the scalability concern, different versions of the intimacy permissiveness
scale have been used in various regions, mainly in the United States, as well as for different
groups of people such as adolescents (e.g. Kornreich, et al. 2003; Rosenthal and Smith, 1997),
college students (Christensen and Carpenter, 1962; Reiss, 1964; Sprecher and Hatfield, 1996,
Sprecher, et al. 1988), and nationally sampled adults in the Netherlands and the United States
(Kraaykamp, 2002; Reiss, 1964). Despite popular acceptance among researchers, few have
applied such scales to non-Western societies (with an exception of Sprecher and Hatfield,
1996). It is of interest to validate the intimacy permissiveness scale based on a representative
sample in societies generally viewed as more sexually conservative, or restrictive, than those
in the West.
GUTTMAN SCALING MODEL
The Guttman scalogram analysis is a method for organizing responses to a set of items into a
scale. This method is used to locate respondents by means of a set of items ordered by degree
of difficulty in which a unidimensional, or single hierarchical, response pattern is assumed
(Feick, 1987). Such a unidimensional approach is “designed to ensure that there is only one
unique combination of responses for each scale score” (Bailey, 1994: 353-354). If some
combination of scores other than the desired combination forms a particular scale score, it is
considered an error. This unique combination, or one ordering premise, is designated under
the assumption of population homogeneity (Clogg and Sawyer, 1981). When many scaling
errors or exceptions to the desired pattern occur, the scale is seen as inadequate. Common
criteria for evaluating the adequacy of a Guttman scale include coefficient of reproducibility
(CR) and minimal marginal reproducibility (MMR). Any value of CR over .90 is seen as
adequate to indicate the scalability and the ability to reproduce responses to the various items
but no minimum value has been noted for MMR. Menzel (1952) argued that the coefficient of
reproducibility may not be an accurate measure of scalability and suggested using coefficient
3
of scalability (CS) instead.1 Coefficient of scalability indicates the proportion of the total
possible improvement which is achieved by the scale. A coefficient of scalability of .60 to .65
is considered an acceptable minimum.
Conventional uses of the Guttman scaling model in item analysis and scale construction
have been criticized for having several not necessary mutually exclusive limitations (Clogg
and Sawyer, 1981; also see McCutcheon, 1987). The deterministic form, or “perfect type”
(Borgatta and Hays, 1952), of Guttman scaling does not adequately account for fundamental
statistical concepts such as the presence of error responses, due to either measurement errors
or sampling errors. Moreover, the evaluation criteria are not statistically justifiable in regard
to the number of items involved or the sample size. When the Guttman model is judged to be
inadequate, it is often difficult to provide an explicit quantitative description of alternative
scaling models. So even though the conformity of the empirical data to Guttman’s perfect
model may be incomplete, the Guttman model, nevertheless, provides an acceptable summary
of the attitude in question (Clogg and Sawyer, 1981). In order to provide alternative
approaches to item scaling based on probabilistic ideas, several models using the technique of
latent class analysis have been developed (c.f. Clogg and Sawyer, 1981; Goodman, 1975;
Lazarsfeld and Henry, 1968; also see McCutcheon, 1987).
LATENT CLASS MODELS
The concept of latent class analysis was originated in the work of Lazarsfeld (1950) on latent
structures. The basic goal of latent class analysis is to characterize a discrete latent variable (X)
from a cross-classification of responses to two or more observed categorical variables
(McCutcheon, 1987). It enables researchers to identify a set of mutually exclusive latent
classes within each of which the manifest variables are mutually independent. Assuming the
model, consider four items A, B, C, and D, whose levels are indexed by i, j, k, and l,
respectively. A model with an arbitrary number of items can be written in an analogous
fashion. Let π ijkl denote the expected proportion that a respondent is in cell (i, j, k, l) of the
cross-classification of the four items. The latent class model with T latent classes is, then,
T
π ijkl = ∑ π tX π itA X π Bjt X π ktC X π ltD X
(1)
T =1
where π tX is the expected proportion that X = t , π itA X is the expected conditional probability
that item A takes on level i, given that latent variable X is at level t; the other expected
conditional probabilities are defined similarly. More technical details on the theoretical
foundations and estimation of latent class models are available in existing literatures (Clogg
and Sawyer, 1981; McCutcheon, 1987; Eliason, 1988).
In practice, the number of latent classes is defined based on theoretical or exploratory
reasoning before analysis. Up to the limits imposed by identifiability, we can consider latent
class models with different numbers of latent classes. The parameters of the model are
calculated by using maximum-likelihood estimates to obtain latent class probabilities and
conditional probabilities (Goodman, 1974). Latent class probabilities are estimates of the
population proportions describing the distribution of classes of the latent variable within
which the observed measures are independent of each other. Conditional probabilities, which
are comparable to factor loadings in factor analysis, represent the probabilities of an
individual in a particular class of the latent variable being at a particular level of the observed
variables. The goodness-of-fit of latent class models can be assessed with a comparison of the
1
For the formulas of CR, MMR, and CS, please see Clogg and Sawyer (1981) and McCutcheon (1987).
4
likelihood ratio chi-square (L2) or Pearson chi-square (χ2) values to the degrees of freedom for
a model. Other useful indices for assessing the goodness-of-fit for a model include the lambda
measure of association and the percentage of respondents correctly allocated into the classes
of the latent variable (see Clogg and Sawyer, 1981).
Latent class models are aimed at identifying major response patterns as discrete
categories (Yamaguchi, 2000). An advantage of applying such methods is to reveal intragroup heterogeneity in response patterns. One important use of latent class analysis is as a
method for testing whether a theoretically posited typology adequately represents the data.
Such application can be facilitated by imposing restrictions, or constraints, on latent class
models. A special use of restricted latent class models can be constructed to analyze the
scalability of a set of observed responses, for example a Guttman-type scale. The basic
assumptions of such an approach are:
… that the classes of the latent variable represent a sequence of levels of “difficulty” for an
underlying trait and that the observed measures can be rank ordered in terms of their difficulty on the
latent variable. Consequently, respondents in progressively higher classes of the latent variable are
likely to have scored “correctly” on the progressively more difficult measures, which those in the
lower latent classes are likely to have scored “incorrectly” even on the “easier” measures
(McCutcheon, 1987: 44-45).
Such latent class approaches have been seen as a probabilistic analogue of the Guttman
scaling model.
Research has found that latent class models are useful for the analysis of behavioral
hierarchies that typically apply Guttman scalogram analysis (Feick, 1987, 1989; Taylor, 1983).
Several latent class models have been developed to incorporate the notion of measurement
errors and provide evaluation criteria that, unlike the Guttman scaling model, are based on
probabilistic ideas. These models are similar to one another in the assumption of population
homogeneity, as with the Guttman scaling mode, but differ in the assumption of measurement
errors. Of these alternatives, Lazarsfeld’s latent-distance model provides a substantial
improvement in the goodness-of-fit criteria over the others. The latent-distance model was
proposed as a latent structure modeling approach to item scaling (Lazarsfeld and Henry, 1968;
McCutcheon, 1987).
The latent-distance model retains Guttman’s assumption that all persons in a population
are scalable on the same response pattern while accounting for errors of measurement. It
assumes that error rates are item-specific and that the response error rate for affirmative
answers to an item differs from that for negative answers to the item. The assumption is
implemented for all of the scale items except for the two most extreme items, the easiest and
most difficult items. Since it is not possible to estimate more than one reliable error rate for
each of the extreme items, it is important to ensure these two items serve as “anchors” for the
scale (McCutcheon, 1987). Therefore, each of the intermediate items has two error-rate
estimates: one for the affirmative responses and one for the negative responses. The error rate
of such models can be estimated by imposing restrictions on the conditional probabilities.
This model has long been promoted as preferable to a Guttman scale and to arbitrary
classification (Borgatta and Hays, 1952).
The latent-distance model has been found to have a better goodness-of-fit for scaling
analysis in empirical studies than other alternative latent approaches based on the same
population homogeneity assumption. However, this model does not necessarily fit the data to
a statistically acceptable degree (Clogg and Sawyer, 1981; McCutcheon, 1987). Goodman
(1975) introduced an alternative model in which the assumption of population homogeneity is
relaxed. Instead, Goodman’s scale model assumes that a given individual in the population is
either “intrinsically scalable,” as the assumption of population homogeneity proposed, or
“intrinsically unscalable.” Methods for the study of quasi-independence in contingency tables
are used to obtain the estimates of model parameters. As a special case of restricted latent
5
class models, the number of scale types estimated for Goodman’s scale model is the number
of scale types that follow the Guttman ordering, plus one additional scale type for unscalable
responses. Specific value restrictions are placed on the conditional probabilities of the
scalable types, but no restrictions are imposed on the conditional probabilities of the
unscalable type.
The Goodman scale model relaxes the assumption of population homogeneity to take
account of the existence of “error response patterns” (Clogg and Sawyer, 1981; Goodman,
1975; McCutcheon, 1987). It does not force researchers to assume that only one ordering
exists to define a unidimensional trait in the entire population (Clogg and Sawyer, 1981).
Instead, it assumes that some of the orderings resulted from the combination of item
responses, which although not considered under the Guttman model, are valid orderings for
certain parts of the population. Such alternative orderings are called “biform” or “multiform”
scales while “uniform” scale indicates the Guttman-type scale (Clogg and Sawyer, 1981;
Goodman, 1975). Data that fit the biform or multiform scales are suggested to be
“multidimensional,” which implies that the assumption of unidimensionality in the Guttman
scaling model is not appropriate for the data. Since the attitudes toward premarital intimate
behaviors involving sexual double standards appear to be multidimensional (Crawford and
Popp, 2003), it is of interest to examine whether the intimacy permissiveness scale in which
there is no specific gendered target would remain unidimensional.
This study attempts to examine the scalability of intimacy permissiveness scale by
employing the Guttman scaling model, the latent-distance model, and Goodman’s scaling
model. Despite the limitations of Guttman scaling analysis, premarital sexual or intimacy
permissiveness scales have been found to meet the requirements of the Guttman scaling
model on all the criteria established, and appear to fit the conceptual framework of established
for premarital sexuality (Reiss, 1964; Hampe and Ruppel, 1974). The scalability of intimacypermissiveness-scale measures are further explored in latent class models, since empirical
data are often “imperfect” for the Guttman scaling model. Given the progressive nature of
intimate behaviors and the above discussions on scaling analysis, Goodman’s uniform scale
model is expected to fit the data, while the latent-distance model is expected to have a fair fit
as well.
DATA AND ANALYSES
The data were drawn from the 2002 Taiwan Social Change Survey (Questionnaire No. 2),
conducted by the Institute of Sociology, Academia Sinica. Main issues examined in this
survey included body images, sexual attitudes and behaviors, and female participation in
political activities, along with individual and household characteristics. After selecting islandwide study sites based on the level of urbanization, household surveys were delivered to
randomly selected individuals within each selected municipality. By using face-to-face
interviewing technique, a total of 1,983 completed questionnaires were obtained.
Measures of intimacy permissiveness used in this survey consisted of a series of
questions regarding whether each of the following behaviors was permissible for unmarried
males or females to do with someone s/he just knows: holding hands, kissing, caressing, and
having sex (sexual intercourse). Responses were coded as (1) absolutely not permitted; (2) not
permitted; (3) it depends; and (4) permitted. Dichotomous variables were created by
combining “absolutely not permitted” with “not permitted” and “it depends” with “permitted”,
recoded as “1” for affirmative answers and “2” for negative answers for the following scaling
analyses. The category of “it depends” was considered as an affirmative answer due to the
distribution proportions of the responses and its relative position, which implied that under
6
certain conditions the requested intimate behavior was permitted. The scale order of these
four items followed the original order of appearance in the questionnaire, with “holding
hands” (H) as the “easiest”, followed by “kissing” (K) and “caress” (C), and with “having
sex” (S) as the “most difficult” item. The sample size analyzed in each scaling model was
reduced to 1,821 mainly due to refusals (ranged from 4.7% to 6.0% for these items) in the
analysis.2
Cross-classification of the four items can be easily obtained from commonly used
statistical programs to test the Guttman scaling model. Several statistical programs are now
available to analyze latent class models. In this study, we use the MLLSA program adapted
by Eliason (1988) to estimate the parameters of latent class models. The MLLSA program
operates from a cross-classification of respondents on the observed categorical variables,
along with specified number of latent classes, and generates maximum likelihood estimates of
latent class probabilities and conditional probabilities (Taylor, 1983). Respondents can be
assigned to latent classes on the basis of cell membership in the manifest variable crossclassification. These latent classes can be used as variables in subsequent analyses. Evaluation
criteria for model goodness-of-fit are provided in the analysis results. Restrictions or
constraints can be imposed on parameter estimates to obtain particular patterns of observed
responses that are required for analyzing the latent-distance model and Goodman’s scale
model.
In addition to the use of the MLLSA program for analyzing latent class models,
multinomial logistic regression was employed to examine whether the survey data reveal
sexual double standards in Taiwan, as well as other sociodemographic characteristics
associated with the intimacy permissiveness scale. Results of multinomial logistic regression
then follow, prior to conclusion and discussion.
RESULTS OF THE SCALING MODELS
The distribution of the four measures of intimacy permissiveness is reported in Table 1.
About 74% of the respondents under no conditions or under certain conditions permitted
single males or females to hold hands with someone s/he just knows, while less than half of
the respondents permitted kissing. Comparably, fewer respondents, under no conditions or
under certain conditions, permitted unmarried males or females to have caressing behaviors
(30.6%) and the fewest permitted having sex (22.9%). Since the proposed order went from the
Table 1. Distribution of Premarital Sexual Permissiveness Measures
Holding hands
Kissing
Caressing
(H)
(K)
(C)
Absolutely not permitted
9.1
19.4
31.7
Not permitted
17.0
33.9
37.7
It depends
34.7
29.7
23.0
Permitted
39.2
17.0
7.6
N
1,849
1,830
2
1,824
Having sex
(S)
43.1
34.0
18.9
4.0
1,829
Questions concerning sexual behaviors, attitudes, and consumption behaviors were printed separately and given
to the respondents at the time of interviewing. Respondents were self-administered to complete those questions
and sealed them in a provided envelope before turning the separate questionnaire back to an interviewer.
Respondents who refused to answer those questions were recorded as “refusals.” For more details of the survey
procedure, please see Chang and Fu (2003).
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Table 2. Distribution of Respondents in Scale Type Corresponding to Ordering (H, K,
C, S)
Scale type
Response patterna
Frequency
Percentage
1
(1, 1, 1, 1)
399
21.91
2
(1, 1, 1, 2)
140
7.69
3
(1, 1, 2, 2)
296
16.25
4
(1, 2, 2, 2)
484
26.58
5
(2, 2, 2, 2)
466
25.59
Total in scale type
1,785
Errors
36
Total
1,821
a
: A code of 1 denotes permitted and a code of 2 denotes not permitted.
98.02
1.98
100.0
least challenging to the most difficult is (H, K, C, S), the distribution of the affirmative
responses to the four items seemed to correspond to the level of difficulty.
Guttman Scaling Model
As the responses were recoded as 1 referring to the permitted and 2 referring to the not
permitted for each of the items, five response patterns:
(1, 1, 1, 1), (1, 1, 1, 2), (1, 1, 2, 2), (1, 2, 2, 2), and (2, 2, 2, 2),
were expected to contain all of the respondents if it were perfectly Guttman scalable. These
scale types were characterized as ranging from the most liberal type, representing the scale
type of (1, 1, 1, 1), and the most conservative type, representing the scale type of (2, 2, 2, 2),
according to the pattern order. Table 2 presents the observed distribution of respondents
among the scale type corresponding to the ordering of (H, K, C, S). A full of 98.0 percent of
the response patterns were found to correspond to the scale type, implying a substantial
amount of agreement between observation and prediction. Less than two percent of the
respondents were then considered to have mistakenly placed their responses. Since the
conformity of the empirical data to the Guttman scale model was not complete (less than 100
percent), the commonly-used evaluation criteria of the model were calculated.
The value of coefficient of reproducibility for the model was .995 and the value of
minimum marginal reproducibility was .44. The CR we found signified substantial
improvement in reproducibility over the minimum level of .44, indicating the adequacy of the
intimacy permissiveness scale for the Guttman scaling model. Also, the coefficient of
scalability was .991 for this sample. The scalogram analysis provided evidence for the
measures of the intimacy permissiveness scale as being reasonably Guttman scalable.
Considering that these evaluation indices lack fundamental statistical concepts, latent class
models were employed to further examine the scalability of the intimacy permissiveness scale.
We first applied the latent-distance model. The analysis of Goodman’s scale model then
followed.
Latent-Distance Model
Unlike the Guttman scalogram analysis, the latent-distance model takes account of response
error rates. There were two error rates (conditional probabilities) estimated for each of the
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Table 3. Error Rates and Scale Type Proportions Estimated under the Latent-distance
Model
Intrinsic type of individual (Scale type)
Probability of response
1
2
3
4
5
error in answering item
(1, 1, 1, 1) (1, 1, 1, 2) (1, 1, 2, 2) (1, 2, 2, 2) (2, 2, 2, 2)
Holding hands (H)
Kissing (K)
Caressing (C)
Having sex (S)
Proportion in scale type
.010
.015
.006
.008
.010
.015
.006
.008
.010
.015
.005
.008
.010
.002
.005
.008
.010
.002
.005
.008
.2256
.0795
.1677
.2698
.2574
intermediate scale items, and one error rate for each of the two extreme scale items. The
proportions of the population in each of the scale type (predicted latent class probabilities),
although not a concern in scaling models, were also estimated with no restrictions imposed.
The restricted conditional probabilities in the latent-distance model used to define error rates
are shown in Appendix A. Conditional probabilities in the same form of squares were
restricted to be equal to each other. Among the four items of H, K, C, and S, one error rate
was estimated for each of H and S as being the “extreme” items. Three error rates for
affirmative responses and two others for negative responses were estimated for item K, while
two error rates for affirmative responses and three others for negative responses were
estimated for item C. Table 3 reports the estimated error rates and scale type proportions
under the latent-distance model for the four-item intimacy permissiveness scale.
The estimated error rates were quite low across response items, ranged from .002 to .015.
The two extreme items ―holding hands (H) and having sex (S)― had identical error rates for
positive and negative responses. The small error rates for items H and S (.010 and .008,
respectively) indicated that these two items “anchored” this intimacy permissiveness scale
quite well. A positive response (coded 1) to the Kissing (K) item had a higher response error
rate (.015) than any of the other responses, while the error rate for the negative response (code
2) to the item was smaller (.002). Such findings suggest that the Kissing (K) item
discriminates better for more permissive sexual behaviors than for less permissive ones. Both
of the positive and negative error rates for the Caressing (C) item were quite low (.006
and .005, respectively), indicating its reasonable ordering in the intimacy permissiveness scale.
With respect to the respondents, about 22.6% of them were estimated to be among the
most liberal (scale type 1) toward intimacy permissiveness, which involved high probabilities
(from .98 to .99) of permitting each of the four intimate behaviors for the unmarried with
someone they just know. On the other end, about a quarter (25.7%) of the respondents was
estimated to be the most conservative toward intimacy permissiveness, regarding none of the
intimate behaviors as permitted for the unmarried. The latent-distance model yielded a
likelihood ratio chi-square of 11.59 with 5 degrees of freedom, indicating a fair acceptance
(.01<p< .05) of the model. In addition, the lambda measure of association (.98) and the 98.4
percent of correctly allocated respondents indicated that the latent-distance model fits the data
well.
Goodman’s Scale Model
Since the results of Guttman’s scalogram analysis and the latent-distance model revealed an
acceptable order of the intimacy permissiveness scale, Goodman’s basic model was
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Table 4. Summary of Guttman, the Latent-distance, and Goodman’s Models
Scaling model Guttman scaling
Latent-distance
Goodman’s scale
model
model
model
.2191
.0769
.1625
.2658
.2559
.0198
.2256
.0795
.1677
.2698
.2574
.2160
.0703
.1547
.2602
.2547
.0441
CR= .995
MMR= .44
CS= .991
L2= 11.59
χ2= 11.92
d.f.= 5
L2= 8.32
χ2= 6.27
d.f.= 6
Proportion in scale type
1 (1, 1, 1, 1)
2 (1, 1, 1, 2)
3 (1, 1, 2, 2)
4 (1, 2, 2, 2)
5 (2, 2, 2, 2)
Errors/unscalable type
Scalability criteria/
goodness-of-fit indices
considered here. The restricted conditional probabilities in Goodman’s scale model used to
define error rates are shown in Appendix B. Conditional probabilities in the squares were
restricted to be equal to 1.00. Similar to the latent-distance model, all respondents who
answered according to one of the five Guttman response patterns were included with certainty
in the appropriate class. The remaining respondents whose answers were not in any of the
Guttman-type patterns were included in the unscalable type. According to the assumption of
Goodman’s model, respondents in the unscalable type were assumed to give random
responses. Therefore, no restriction was imposed on the four-item error rates of this type.
The likelihood ratio chi-square for Goodman’s basic model was 8.32 and Pearson chisquare was 6.27, with 6 degrees of freedom. The result indicated that Goodman’s basic model
fits the data well. The lambda measure of association (.96) and the 97.57 percent of correctly
allocated respondents supported the acceptability of Goodman’s scale model for the measures
of intimacy permissiveness scale. The estimated proportions of the five scalable types and the
unscalable type are reported in Table 4. As the proportions among scale types indicated, less
than five percent of respondents were estimated to be classified in the unscalable type. In
other words, more than 95% of the respondents were scalable in the Guttman-type order. The
unrestricted error rates for affirmative answers to H, K, C, and S, were .82, .58, .46, and .32,
respectively, which were, to some degree, consistent with the level of difficulty of the four
items (results not shown). Based on the parameter estimates, the proposed Guttman ordering
was acceptable, and Goodman’s biform- or multiform-scale models were, therefore, not
further considered in this study.
In addition, Table 4 provides a summary of scaling analysis results of the three models
applied in the study. It is clear that Goodman’s scale model fits the data better than the latentdistance model, while Guttman’s scaling model is also appropriate for describing the
measures of the intimacy permissiveness scale. The distribution patterns of the proportions in
each of the scale types were similar across the three scaling models; disregard the errors of the
Guttman scaling model and the unscalable type of Goodman’s scale model. Scalability
criteria for the Guttman scaling model and goodness-of-fit for latent class models provided
evidence that the responses to the four items of the intimacy permissiveness scale contain a
particular ordering. The order of permitting intimate behaviors, from the least intimate to the
most intimate behaviors, was confirmed as a progression from holding hands, kissing,
caressing, to having sex. In order to further validate the Goodman scale model, the
examination of the association between sociodemographic characteristics and this intimacy
10
permissiveness scale then followed in order to understand whether the attitudinal patterns of
Taiwanese toward premarital intimacy permissiveness, particularly the existence of sexual
double standards, are consistent with theoretical explanations suggested by the previous
literature. Multinomial logistic regression is employed for the following analysis to account
for the unscalable type.
Sociodemographic Differentials in the Intimacy Permissiveness Scale
Table 5 presents the results of multinomial logistic regression on the intimacy permissiveness
scale formed by the four items regarding attitudes towards premarital intimacy with
acquaintances. Type 1 represents the most liberal type—scale type (1, 1, 1, 1)—and Type 5
represents the most conservative type—scale type (2, 2, 2, 2)—in terms of the scale types of
the intimacy permissiveness scale. Types 2 through 4 represent the intermediate scale types.
Respondents were assigned to each of the Guttman scale types based on the observed
response patterns to the four items. Those whose response patterns were not any of the five
Guttman scale types were grouped into Type 6—the unscalable type—in the analysis. The
distribution proportions of the six types were, therefore, the same as those of the Guttman
scaling model in Table 4, with “errors” as the unscalable type instead. The most conservative
type, indicated by scale type (2, 2, 2, 2), was used as the baseline category in the multinomial
logistic regression.
The results indicated that a gender effect was significantly associated with attitudes
toward premarital intimacy with acquaintances, except for Type 4. Male respondents were
over one and a half times more likely than females to be liberal toward premarital intimacy
with acquaintances. In other words, premarital intimate behaviors were more acceptable
among males than among females. Putting aside the significant gender effect in the unscalable
type, this likelihood of males’ acceptance of premarital intimacy was highest among those
who were in the most liberal type when compared to the most conservative type. Regardless
of including both genders in the target described in the item question, sexual double standards
were found among Taiwanese respondents.
Among other sociodemographic characteristics, age was found to be significantly
associated with attitudinal types toward premarital intimacy permissiveness. As indicated by
the negative coefficients, older respondents were more conservative toward premarital
intimacy with acquaintances than younger respondents. Pseudo R-square of this model
was .16 with a sample size of 1,806.
CONCLUSION AND DISCUSSION
This study attempts to validate premarital intimacy permissiveness measures using islandwide
survey data in Taiwan. The results of scaling analyses indicated that the response combination
of the measures of the intimacy permissiveness scale can be considered to meet the criteria of
a Guttman-type scale. In other words, the permissiveness towards unmarried males or females
having premarital intimate behaviors with someone s/he just knows was found to follow a
hierarchical order from the least intimate to the most intimate behaviors. For the majority of
respondents, those who permitted a more intimate behavior, such as caressing, could also be
expected to permit less intimate behaviors, such as holding hands and kissing. On the other
hand, those who did not permit less intimate behaviors could be predicted to not permit more
intimate behaviors. The estimated probability of correct prediction was proved to be high in
the study, as indicated by the relatively low error rates.
11
Table 5. Multinomial Logistic Regression on Types of Premarital Sexual Permissiveness (N=1,806)
Type 1 vs. Type 5
Type 2 vs. Type 5
Type 3 vs. Type 5
Type 4 vs. Type 5
Variables
β
Exp(β)
β
Exp(β)
β
Exp(β)
β
Exp(β)
Intercept
1.28***
-1.23*
.57
.28
Age
-.06***
.94
-.02**
.98
-.03***
.97
-.01*
.99
Education
-.03
.97
.07*
1.07
-.00
.99
.02
1.02
Personal income
.00†
1.00
.00
1.00
.00
1.00
.00
1.00
Gender (male)
1.54***
4.66
.45*
1.57
.56***
1.75
.22
1.24
Pseudo R-square
.16
†: p<.10; *: p< .05; **: p< .01; ***: p< .001.
1: Types 1 through 5 were the most liberal type to the most conservative type; Type 6 was the unscalable type.
12
Type 6 vs. Type 5
β
Exp(β)
-2.44*
-.02
.98
-.05
.95
.00
1.00
1.56***
4.76
The measures of the intimacy permissiveness scale in this study consisted of four items,
among which holding hands is not used in previous studies. Also, the findings of the latentdistance model suggest that the Kissing (K) item discriminates better for more permissive
intimate behaviors than for less permissive ones, which in this study is the Holding hands (H)
item. However, the error rate for the Holding hands item is relatively low (.010). It is also
fairly common for couples in Taiwan, as well as in some Western countries, to announce
their relationships by holding hands in public. It should be reasonable to include such item in
the intimacy permissiveness scale.
Unlike the previous studies demonstrating that Goodman’s scale model is superior to the
Guttman scaling model in analyzing hierarchical behaviors (e.g. Clogg and Sawyer, 1981;
McCutcheon, 1987), this study revealed the usefulness and appropriateness of the Guttman
scaling model for measures of premarital intimacy permissiveness. In addition, the latentdistance model and Goodman’s scale model were shown to be appropriate as well, although
the latter had a better goodness-of-fit for the issue of premarital intimacy permissiveness.
While the purpose of this study is to examine the scalability of intimacy permissiveness
measures, rather than comparing different scaling models, the appropriateness of a particular
model seems to matter more to issues that a study intends to address than do particular
modeling techniques. Among studies applying both the Guttman scaling model and latent
class models, Goodman’s scale model was not necessarily the ultimate one for varied issues,
including consumer problems, environmental concerns with “don’t know” responses, and
political questions (e.g. Feick, 1987, 1989; Taylor, 1983). Although the underlying
mechanism has not been clarified, a Guttman scaling model may be sufficient for issues
related to premarital intimacy or sexuality.
In addition to the progressive nature of intimate behaviors, the intimacy permissiveness
measures in this study did not consider different levels of affection involved or the gender of
the targeted persons as measured in the previous studies (Crawford and Popp, 2003; Reiss,
1964; Sprecher, 1989; Sprecher and Hatfield, 1996; Sprecher, et al. 1988). It has been argued
that the level of relationships between a target and a hypothesized other is associated with the
level of permissiveness for intimate behaviors (Reiss, 1964; Sprecher, 1989; Sprecher and
Hatfield, 1996; Sprecher, et al. 1988). However, the definitions of and the typology for
distinguishing different levels of relationships are not consistent. Moreover, the meanings of
relationships at different levels, such as seriously dating or engaged, may vary in different
social and cultural contexts, under which the allowed intimate behaviors for the unmarried
may vary even when the same level of relationship is involved (Chang, 1996). This paper
contributes to studies on premarital intimacy or sexuality by confirming the scalability of
intimacy permissiveness measures under the condition of non-affection relationship. Since a
contextually consensus definition of affection relationship remain in need, it is reasonable to
examine people’s premarital intimacy permissiveness by specifying a non-affection
relationship.
Gender of the targeted object was not particularly specified in this study. Instead, this
study examined the association between the gender of respondents and attitudinal types
towards premarital intimacy permissiveness. Consistent with previous studies, females were
found to be more conservative than males in matters of premarital intimacy (Sprecher, 1989;
Sprecher and Hatfield, 1996). Research concerning sexual double standards may argue the
failure of distinguishing male targets from females since the level of permissiveness for
sexual behavior could vary depending on the gender of the target (Reiss, 1964). However,
previous studies have suggested that sexual double standards are multidimensional in
contemporary societies, and it is more a matter of the gender of respondent rather than a
matter of the gender of a target (Crawford and Popp, 2003; Sprecher and Hatfield, 1996).
Although the wording of the intimacy permissiveness measures included both genders,
13
instead of specifying a particular gender, the results of multinomial logistic regression
provided evidence for such sexual double standards among Taiwanese respondents. The
validation of the intimacy permissiveness scale was then established.
Although the scalability of the intimacy permissiveness measures is confirmed, one may
be curious about whether Taiwanese, in fact, remain conservative toward premarital sexual
behavior after a supposed quiet revolution in sexuality (Rindfuss, and Morgan, 1983; Chang,
1966). The results indicate that slightly more than one-fifth of the respondents were very
liberal in terms of premarital intimacy behaviors, but the majority remains conservative.
Because of lacking longitudinal data, we are not able to examine whether Taiwanese become
more liberal toward premarital intimacy. Despite the conservative attitudes among
contemporary Taiwanese, the intimacy permissiveness scale contributes to sex research by
providing reliable measures for understanding people’s intimate attitudes and behaviors.
Nevertheless, as implied by divergent findings on gender and age effects, factors associated
with the intimacy permissiveness scale need to be further examined by replicating studies
across different subjects and over time to provide a better understanding of premarital
intimacy attitudes and behaviors in Taiwan.
14
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16
Appendix A. Restriction on Conditional Probabilities in Latent-distance Model
Latent Class t b
Item Responsea
1
2
3
4
5
(1, 1, 1, 1) (1, 1, 1, 2) (1, 1, 2, 2) (1, 2, 2, 2) (2, 2, 2, 2)
H
K
C
S
1
π 11HX
π 12HX
π 13HX
π 14HX
π 15HX
2
π 21HX
π 22HX
π 23HX
π 24HX
π 25HX
1
π 11kX
π 12kX
π 13k X
π14kX
π 15k X
2
π 21kX
π 22kX
π 23kX
π 24kX
π 25kX
1
π 11C X
π 12C X
π 13C X
π 14C X
π 15C X
2
π 21C X
π 22C X
π 23C X
π 24C X
π 25C X
1
π 11S X
π 12S X
π 13S X
π 14S X
π 15S X
2
π 21S X
π 22S X
π 23S X
π 24S X
π 25S X
a
: A code of 1 denotes permitted and a code of 2 denotes not permitted.
: Conditional probabilities in the same form of square are assumed to be equal to each other.
b
17
Appendix B. Restriction on Conditional Probabilities in Goodman’s Scale Model
Latent Class t b
Item Responsea
1
2
3
4
5
(1, 1, 1, 1) (1, 1, 1, 2) (1, 1, 2, 2) (1, 2, 2, 2) (2, 2, 2, 2)
H
K
C
S
1
π 11HX
2
Unscalable
type
π12HX
π 13HX
π14HX
π 15HX
π 16HX
π 21HX
π 22HX
π 23HX
π 24HX
π 25HX
π 26HX
1
2
π11KX
π 12KX
π 13KX
π 14KX
π 15KX
π 16KX
π 21kX
π 22kX
π 23kX
π 24KX
π 25KX
π 26KX
1
2
π 11C X
π 12C X
π 13C X
π 14C X
π 15C X
π 16C X
π 21C X
π 22C X
π 23C X
CX
π 24
π 25C X
π 26C X
1
π11SX
π 12S X
π 13S X
π 14S X
π 15S X
π 16S X
2
π 21S X
π 22S X
π 23S X
π 24S X
π 25S X
π 26S X
b
a
: A code of 1 denotes permitted and a code of 2 denotes not permitted.
: Conditional probabilities in squares are assumed to be equal 1.00.
b
18