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Revisiting the factorial validity of the 15-item need for Closure Scale

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H. Michael Crowson at University of Oklahoma
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Abstract

The purpose of this study was to re-assess the factorial validity of Roets and Van Hiel's (2011) 15-item Need for Closure Scale (NFCS) while correcting for methodological limitations associated with their initial validation of the measure. Four hundred twelve adult volunteers completed the NFCS as part of an unrelated study. Confirmatory factor analysis of the measure revealed evidence of a single dominant factor with results consistent with those presented by Roets and Van Hiel. Internal consistency for the measure was also high. Despite continued limitations in operationalizing the close-mindedness facet, the 15-item NFCS appears to be a satisfactory measure of the need for closure construct.
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Individual Differences Research www.idr-journal.com
2013, Vol. 11, No. 4, pp. 133-138 ISSN: 1541-745X
© 2013 Individual Differences Association, Inc.
133
Revisiting the Factorial Validity of the 15-item
Need for Closure Scale
H. Michael Crowson
University of Oklahoma
*Mike Crowson, Ph.D.; Department of Educational Psychology, University of Oklahoma, 820
Van Vleet Oval; Norman, OK, 73019; mcrowson@ou.edu (email).
ABSTRACT - The purpose of this study was to re-assess the factorial validity of Roets and Van
Hiel’s (2011) 15-item Need for Closure Scale (NFCS) while correcting for methodological
limitations associated with their initial validation of the measure. Four hundred twelve adult
volunteers completed the NFCS as part of an unrelated study. Confirmatory factor analysis of the
measure revealed evidence of a single dominant factor with results consistent with those
presented by Roets and Van Hiel. Internal consistency for the measure was also high. Despite
continued limitations in operationalizing the close-mindedness facet, the 15-item NFCS appears
to be a satisfactory measure of the need for closure construct.
Need for closure refers to a desire for firm, stable, and secure knowledge as opposed
to experiencing ambiguity (Kruglanski & Webster, 1996). Research indicates that
although the need for closure can arise from external conditions such as mental fatigue,
time pressure, noise, and task dullness, it also can be thought of as a stable dimension of
individual differences (Webster & Kruglanski, 1994). In order to measure individual
differences in closure needs, Webster and Kruglanski (1994) developed a 42-item Need
for Closure Scale. When constructing the scale, the authors wrote items to represent five
different manifestations (i.e., facets) of need for closure: preference for order (ten items),
preference for predictability (eight items), decisiveness (seven items1), discomfort with
ambiguity (nine items), and close-mindedness (eight items). Based on the authors’ factor
analytic work, as well as the work of others (see e.g., Roets & Van Hiel, 2011), the NFCS
is generally treated within the literature as measuring a unidimensional need for closure
construct (see Neuberg Judice, & West, 1997, for a different perspective on the
dimensionality of the NFCS).
Recently, Roets and Van Hiel (2011) noted that the length of the full NFCS has led
many researchers to develop “idiosyncratic” measures of the construct as a practical
solution to the problem of scale length. They correctly noted that lack of standardization
in measurement presents difficulties when it comes to comparing research findings across
studies. As a response to this problem, they developed a shorter, 15-item measure of need
for closure by factor analyzing their 41-item NFCS (Roets & Van Hiel, 2007) in an
aggregated sample of 1584 participants. They selected their items by forcing a single
component solution (from an initial principal components analysis, PCA) and then
choosing the three highest loading items from each (theorized) closure facet from their
Crowson / Individual Differences Research, 2013, Vol. 11, No. 4, pp. 133-138
134
original 41-item measure. This, they reasoned, allowed the “content richness and the
predictive power of the construct” (p. 91) to be maintained. Following, the authors
utilized exploratory (components analysis) and confirmatory factor analysis to provide
factor analytic evidence for the fit of their one-factor model.
In their discussion, Roets and Van Hiel (2011) raised a question in regards to their
study findings. Specifically, they noted that “it might be possible that the responses on
the 15-item scale somewhat depend on whether these items are imbedded in the full
NFCS or whether they are assessed as a stand-alone questionnaire” (p. 94). In effect, this
statement was an acknowledgement that the measurement properties of their 15-item
NFCS may depend, in part, on the context in which the items were originally measured.
By changing the context of measurement, researchers using the reduced 15-item measure
of need for closure may be changing the measurement of the closure construct. Based on
their reasoning, Roets and Van Hiel recommended future research testing the 15-item
scale as a “stand-alone measure” (p. 94). One aim of the present study was to address
this question.
A second aim of this study was to address a methodological limitation associated with
the Roets and Van Hiel (2011) study. Specifically, following item selection based on an
initial factor analysis, the authors utilized exploratory and confirmatory factor analysis to
validate their model using the same sample data from which the 15-item measure was
developed. A potential consequence of this approach is that the authors capitalized on
sample-specific factors, resulting in an overfitting of their model to the data and a
decrease in the likelihood that their model validation results would replicate in future
population samples. Arguably, a better strategy for validating their single factor model is
to cross-validate their model in an independent population sample. In this study, I sought
to address this methodological limitation by factor analyzing the 15-item measure using
an independent sample.
Method
Participants
Four hundred twelve individuals (male 30.8%, female 68.9%, 1 missing) participated
in this study. Participants’ ages ranged from 17 to 82 (Mean=34.46, SD=12.98). In terms
of racial/ethnic breakdown, 71.4% of the sample identified themselves as White, 8.7% as
African American/Black, 6.3% as American Indian or Alaska Native, 4.6% as Asian, and
3.9% as Hispanic/Latino.
Measures and Procedures
Participants were solicited electronically (e.g., via email, Facebook) by graduate
students enrolled in this author’s quantitative methods course to participate in an
unrelated study. Participants who agreed to participate logged onto Survey Monkey and
completed a questionnaire containing the 15-item measure of Need for Closure Scale
(Roets & Van Hiel, 2011), along with other questionnaires measuring political ideology,
dogmatism, and political interest and involvement.3 Participants responded to items on
the need for closure measure on a scale ranging from 1 (strongly disagree) to 7 (strongly
agree). Example items from the 15-item NFCS include, “I dislike it when a person’s
statement could mean many different things” and “I enjoy having a clear and structured
Crowson / Individual Differences Research, 2013, Vol. 11, No. 4, pp. 133-138
135
mode of life. Based on Roets and Van Hiel’s (2011) work, each of the five (i.e.,
preference for order, preference for predictability, decisiveness, discomfort with
ambiguity, close-mindedness) theorized facets of need for closure was measured with
three items from the 15-item scale.
Results
Confirmatory Factor Analyses
LISREL 8.80 was utilized to carry out confirmatory factor analyses on the 15-item
Need for Closure Scale. Missing data were treated with multiple imputation through
PRELIS. Initial data screening revealed departures from univariate normality on a
number of items. Unsurprisingly, the test of multivariate normality was statistically
significant (p < .001) as well, indicating a violation of the multivariate normality
assumption required for accurate maximum likelihood estimation. As such, an asymptotic
variance-covariance matrix was input into LISREL as a weight matrix along with the
sample covariance matrix. Fit of the confirmatory factor analysis (CFA) models was
judged according to the criteria suggested by Kline (2011) and Schumacker & Lomax
(2010). RMSEA and SRMR values less than .05, AGFI and NFI values around .90 or
greater, and CFI values around .95 or greater are each considered indicators of good
model fit.
Six CFA models2 were tested with the data. For the first model test, all items from the
15-item Need for Closure Scale were loaded onto a single latent need for closure
variable. Consistent with the approach taken by Roets and Van Hiel (2011) in their
validation study, the error terms for items within the same facet were allowed to
correlate. Overall, this model fit the data quite well (see Table 1). All factors loadings
were statistically significant in the model. The R2 values for the items ranged from .02 to
.52, with 13 of the 15 items exhibiting R2 values greater than .15. The two lowest
loading items were “I do not usually consult many different opinions before forming my
own view” (R2 = .02) and “When I have made a decision, I feel relieved” (R2 = .12). The
former item was from the close-mindedness facet, whereas the latter was from the
decisiveness facet, of Roets and Van Hiel’s (2007) 41-item NFCS. Ranges of within-facet
R2 values for the one-factor, correlated errors model are as follows: preference for order,
.32 - .34; preference for predictability, .29 - .49; decisiveness, .12 - .26; discomfort with
ambiguity, .31 - .52, close-mindedness, .02 - .33.
The second CFA involved testing a one-factor model that did not allow for correlated
errors. The overall fit of this model was poor (Table 1), although all factors loadings
were statistically significant. The R2 values for the items ranged from .04 to .61, with 12
of the 15 items exhibiting R2 values greater than .15. The third CFA was a test of a five-
factor model with the five need for closure facets (see Webster & Kruglanski, 1994)
being treated as uncorrelated. The R2 values for the items ranged from .05 to .92, with 14
of the 15 items exhibiting R2 values greater than .15. The fit indices (Table 1) for this
analysis indicated that the model was also a very poor fit to the data.
The fourth CFA involved a test of a correlated, five-factor model. The fit statistics for
this model were suggestive of good fit to the data (see Table 1). However, upon further
inspection, it was apparent that the correlation matrix of latent factors for this final model
was non-positive definite. This appeared to stem from the presence of linear
Crowson / Individual Differences Research, 2013, Vol. 11, No. 4, pp. 133-138
136
dependencies in the matrix (see Schumacker & Lomax, 2010), as evidenced by several
correlations falling at or above .79 (with two exceeding .90).
The fifth CFA involved a test of a correlated two-factor model, consistent with
Neuberg et al.’s (1997) claim that items from the Need for Closure Scale measures two
factors: “desire for decisiveness” (represented by the decisiveness items) and the “need
for simple structure” (represented by the remaining items; p. 1408). Although Roets and
Van Hiel (2007) revised the decisiveness scale to better measure the need for closure
construct as originally conceptualized by Webster and Kruglanski (1994), a two-factor
test of their 15-item Need for Closure Scale nevertheless seemed prudent for this study.
The overall fit of the correlated two-factor model was poor (Table 1), although all factors
loadings were statistically significant. The R2 values for the items ranged from .04 to .80,
with 13 of the 15 items exhibiting R2 values greater than .15. Further supporting a
unidimensional interpretation of the scale, the correlation between the two factors was .53
(p < .001). Finally, the sixth CFA involved a test of an uncorrelated two-factor model.
The fit of this model to the data was poorer than that of the correlated-errors model.
Table 1
Model Fit Statistics
SB χ²
RMSEA
RMSEA 90% CI
CFI
NNFI
One-factor, correlated errors
151.58 (p < .001)
.05
.04, .06
.97
.96
One-factor, uncorrelated errors
648.51 (p < .001)
.12
.11, .13
.87
.84
Five uncorrelated factors
800.97 (p < .001)
.14
.13, .15
.83
.80
Five correlated factors
160.85 (p < .001)
.05
.04, .06
.97
.96
Two correlated factors
586.69 (p <.001)
.12
.11, .13
.89
.87
Two uncorrelated factors
671.11 (p < .001)
.13
.12, .13
.87
.85
Note. **Correlation matrix of latent variables is non-positive definite
Internal Consistency
Cronbach’s alpha for the 15-item scale was .87 in this sample.
Discussion and Conclusion
The first model test in the current study largely replicated the Roets and Van Hiel
(2011) results. Although the overall fit of the 15-item NFCS was somewhat poorer than
that presented in the Roets and Van Hiel study, the fit of the one-factor model in the
current sample was reasonably good. Also similar to the previous study, items measuring
the facets preference for order, preference for predictability, and discomfort with
ambiguity appeared to be the stronger, whereas items measuring close-mindedness and
decisiveness appeared to be weaker, indicators of the need for closure construct. It is
notable that the same poor loading close-mindedness item in the Roets and Van Hiel
study also emerged as the poorest indicator of close-mindedness in the current study,
suggesting that perhaps this item in particular needs re-consideration in the long- and
short-forms of the NFCS. The internal consistency of the 15-item measure in the current
study was exactly that presented in Roets and Van Hiel’s study, suggesting that the
internal consistency of the measure may hold up across independent samples (holding
other test conditions constant).
Five alternative models were considered in this study. A comparison of the single-
factor models (with and without correlated errors) revealed that there is significant
Crowson / Individual Differences Research, 2013, Vol. 11, No. 4, pp. 133-138
137
variation in the items that may be facet-specific, irrespective of the variation attributable
to need for closure. A comparison of the model fit statistics from the one-factor
(correlated errors) and five-factor (correlated and uncorrelated factors) models reveals
that a single factor model better explains the variation in the 15-item measure than a five
factor model. Similarly, the one-factor (correlated errors) model appears to exhibit a
substantial improvement in fit over that of two-factor (correlated and uncorrelated)
models.
Conclusion
The current study was designed to address methodological limitations associated with
Roets and Van Hiel’s (2011) validation of the 15-item measure of Need for Closure.
First, the study was based exclusively on the 15 items Roets and Van Hiel selected for
inclusion in their shortened scale, thereby addressing the question of whether item-
presentation context may have impacted their results. Secondly, the study was based on
an independent sample of respondents, unlike Roets and Van Hiel who developed their
measure and validated it on the same set of participants. Based on the findings from the
current study, it appears that methodological limitations associated with Roets and Van
Hiel’s (2011) study did not bias their results or interpretation of the measure in any
substantive way. It seems reasonable to conclude that the 15-item Need for Closure Scale
does function as a reasonable alternative to the 41-item scale described by Roets and Van
Hiel (2007). Even so, it appears that more work needs to be done to identify a better set
of indicators of the close-mindedness facet.
Footnotes
1Criticisms surrounding the operationalization of the decisiveness facet (i.e., that it
measured ability rather than need content) prompted Roets and Van Hiel (2007) to revise
the decisiveness facet (6 items).
2Technically, the first model represents a direct effort to replicate the results obtained
by Roets and Van Hiel’s (2011) in their analysis of the 15-item scale. I also tested five
additional models that I considered to be plausible alternative models concerning the
factor structure of the scale. Thanks to an anonymous reviewer for this suggestion. Three-
and four factor models were not considered as there are no theoretical or empirical
precedents for loading the items in these ways.
3Because these variables are not used in the current study, they will not be described
further.
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... Its psychometric properties were found to be similar to those of the revised full version of the NFCS. In an independent test, Crowson (2013) confirmed the psychometric properties of the 15-item short version of the scale. Over the past decade, the brief 15-item NFCS has been shown to be a valid alternative measure of the NFC construct and has been widely used (Staszak et al., 2022;White, 2022). ...
... Participants' responses to the NFCS in sample 2 were then modeled using the original single-factor model proposed by Roets and Van Hiel (2011), before moving to the factor structure found in the EFA and two alternative models based on existing theoretical models (see Houghton & Grewal, 2000;Neuberg et al., 1997). To note, previous 42-item and 15-item NFCS development studies set the errors between the items of the same facet free in their unidimensional CFAs, as the five facet scales of NFCS were thought to represent various ways in which NFC expresses itself and the facet-based associations were thought to be theoretically meaningful (Crowson, 2013;Roets & Van Hiel, 2011;Webster & Kruglanski, 1994). However, as noted by Morin et al. (2016), whenever an instrument is explicitly designed to assess a single overarching construct from items that also tap into a variety of dimensions, it is important to consider the fact that items are likely to reflect two sources of true-score variance (i.e., the global construct and the more specific components that form this global construct). ...
... Following Crowson (2013), the unidimensional models with and without correlated errors among items from the same facet were first examined via CFA using the data of sample 2. In addition, three other multidimensional CFA models, including the four-factor model suggested by the EFA and different existing theoretical models with correlated factors, were tested with the data, followed by the examination of bifactor-CFA models. The fit indices for all models are shown in Table 3. Regarding CFA or bifactor-CFA models, the bifactor-CFA with a global factor and four specific factors provided the best fit, as it had the lowest χ 2 , RMSEA, SRMR, AIC, and BIC values and the highest CFI and TLI values. ...
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... This motivated them to obtain a reduced, 15-item and one-dimensional version of the scale, that would have a good internal consistency, without losing content and predictive power, by selecting the three items in each facet with the highest factor loadings. This brief scale has been reviewed by Crowson (2013), who sought to address the original's methodological limitation using an independent sample. The findings showed results similar to Roets and Van Hiel (2011b), acknowledging its one-dimensionality and high internal consistency, and concluding that the 15-item scale does function as a reasonable alternative to the 41item scale (Crowson 2013). ...
... This brief scale has been reviewed by Crowson (2013), who sought to address the original's methodological limitation using an independent sample. The findings showed results similar to Roets and Van Hiel (2011b), acknowledging its one-dimensionality and high internal consistency, and concluding that the 15-item scale does function as a reasonable alternative to the 41item scale (Crowson 2013). ...
... Those two items did not show an important increase in the alpha if deleted but alerted us to monitor them in the confirmatory factor analysis (CFA). In this study, we opted for testing some of the Crowson (2013), where the factorial validity of the scale was already tested, via CFA. ...
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... Items such as ''I don't like situations that are uncertain'' were scored on a 6-point Likert-type scale ranging from strongly disagree to strongly agree. Subsequent analysis by Crowson (2013) confirmed the scale's factorial validity and internal consistency (a = 0.87), and it was found to be a reasonable alternative to the 41-item NFC scale. The Cronbach's alpha in this study was a = 0.83. ...
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Full-text available
  • Mar 2007
The validity of the Need for Closure Scale (NFCS) has recently been debated in the research literature. In the present study, it is argued that the traditional Decisiveness scale primarily taps ability content instead of the hypothesized motivational need; therefore, new items that explicitly probe the need for quick and unambiguous answers were developed. It was shown that these need items form a reliable scale (Study 1); were predictive of the seizing process over and beyond ability, whereas the old Decisiveness scale was not (Study 2); and were sensitive to experimentally manipulated levels of task motivation (Study 3). Finally, a reassembled NFCS with the new items replacing the traditional Decisiveness items showed superior fit as a unidimensional model. In the Discussion, it is argued that the specific position of Decisiveness is due to its particular operationalization, not to its theoretical status.
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What the Need for Closure Scale measures and what it does not: Toward differentiating among related epistemic motives.
Article
  • Jun 1997
  • J PERS SOC PSYCHOL
The Need for Closure Scale (NFCS; D. M. Webster & A. W. Kruglanski, 1994) was introduced to assess the extent to which a person, faced with a decision or judgment, desires any answer, as compared with confusion and ambiguity. The NFCS was presented as being unidimensional and as having adequate discriminant validity. Our data contradict these conceptual and psychometric claims. As a unidimensional scale, the NFCS is redundant with the Personal Need for Structure Scale (PNS; M. M. Thompson, M. E. Naccarato, & K. E. Parker, 1989). When the NFCS is used more appropriately as a multidimensional instrument, 3 of its facets are redundant with the PNS Scale, and a 4th is redundant with the Personal Fear of Invalidity Scale (M. M. Thompson et al., 1989). It is suggested that the NFCS masks important distinctions between 2 independent epistemic motives: the preference for quick, decisive answers (nonspecific closure) and the need to create and maintain simple structures (one form of specific closure).
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