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Color and personality: Strong's Interest Inventory and Cattell's 16PF.

Three studies addressed the relation between color preference and personality, based on subsets of respondents from a pool of volunteers who participated in a series of Career Transition Clinics provided by an Atlanta, GA church. It was hypothesized that the mostly inconclusive findings of earlier research are primarily due to the piecemeal nature of the analysis afforded by standard statistical method. Hence, the central data analyses were performed using a neural net approach. Study I (n = 885) showed that the Strong's Basic Interest Scales (BIS) could be predicted reliably (Median r = 0.68) from the Dewey Color System Test, while Study II (n = 1010) showed slightly weaker correlations when predicting respondents' scores on the Cattell's 16PF (Median r = 0.51). Consistent with earlier research, study III (n = 1245) indicated that yellow was preferred more by women than by men. Also, as was hypothesized, our results suggest that previous research was inconclusive due to many-to-one nature of the relation between color choices and personality. Unfortunately, our neural nets could not fully exploit sex related patterns of color preference differences so as to identify meaningful differences between men and women.


Color is an important aspect of our efforts to create personal spaces to our own liking. Yet, little is known about why people like or dislike the colors they do. This paper asks whether people's color preferences reflect meaningful information about their personalities, interpersonal styles, and behaviors. Surprisingly, relatively little research has been done to investigate the links between such variables and individuals' color preferences. The research summarized here represents our efforts to identify links between people's color preferences and their personal characteristics as derived from two well-established psychological inventories.

From a practical perspective, the use of color preference tests promises to assess personality and occupational qualifications such that the stimuli have no perceived face validity to the respondents. In other words, the purpose of administering the test will not be apparent to respondents as they do not know how their color preferences relate to their personality characteristics or occupational proclivities. Color preference tests thus have similar potential advantages to Cattell's classical culture fair tests (CFIT, Cattell, 1949), i.e., they promise to eliminate any social or cultural advantages, or disadvantages, that a person may have due to their upbringing or cultural background. Also, the test can be administered regardless of the test taker's native language, thus obviating the need for translation, recalibrating, renorming, etc. Likewise, issues of socially desirable responding should be less of a concern than in standard paper-and-pencil tests, thereby decreasing response bias.

At the most basic level, color has been shown to affect our mood, thereby affecting the way we interact with our environment. A growing body of research in environmental psychology has shown that the color of a room or work setting can have profound effects on individual enjoyment and performance on a variety of tasks. For instance, Stone (2003) showed that task performance varied as a function of the color of the room in which the task was performed. In another study by Stone (2001), positive mood tended to be higher when individuals worked in a blue carrel compared to a red carrel. Performance is also affected because individuals read slower and comprehended less when performing a reading task in a red environment. This study thus provides direct evidence that color has an effect on cognitive performance, suggesting that the cognitive impairments produced by color could be driven by physiological arousal. Indeed, Stone's (2003) findings indicate that the color red increased individuals' levels of arousal, which when paired with a stimulating task, caused deficits in cognitive performance.

More importantly, the preceding raises the possibility that the effects of color on performance can have differential effects, depending on one's preferences or aversions for particular colors. For example, Eysenck (1967, 1970) postulated that introverted individuals are high in internal arousal (i.e., they are preoccupied with their thoughts and feelings more than are Extraverts), and therefore prefer social environments (e.g., where they are alone) that allow them to reduce or maintain their optimum level of arousal. Thus, when Introverts are with other people their level of arousal might rise to the point that they feel uncomfortable and overwhelmed. The preceding work on the effects of color on arousal therefore suggests that color preferences and personality might be related. Specifically, individuals high in internal arousal (i.e., Introverts) might prefer "calm" colors like blue to reduce their level of arousal, whereas individuals low in internal arousal (i.e., Extraverts) might prefer "exciting" colors like red to increase their level of internal arousal (Luscher, 1971).

Perhaps the most prominent theorist arguing that color preferences and personality are linked is Luscher (1971), who proposed that individuals with similar color preferences should also possess similar personality characteristics. According to Luscher, the physiological reactions that individuals experience while viewing primary colors (blue, red, yellow, and green) reflect basic psychological needs of the individuals. When a primary color is not liked, for example, this dislike is considered to reflect a deficit or unmet physiological and psychological need. For instance, if an individual has a particularly strong dislike for the color red, this is believed to reflect unconscious anxiety within that individual.

Whereas Luscher (1971) regarded color preferences as a reflection of unconscious drives within individuals, contemporary perspectives on the color-personality relationship view color preferences as a reflection of conscious (i.e., reportable) motives, drives, and values. For instance, French and Alexander (1972) found that individuals preferring the color blue were calmer, while preferring yellow was related to "positive" feelings (e.g., happiness). However, the hypothesis that red reflects "negative" feelings (e.g., tension) was not supported. Additionally, Seefeldt (1979) investigated sex differences in color preference, and found that yellow was preferred more highly by women than men. Yet, Stimpson and Stimpson (1979) found no sex differences in color preferences, nor did they observe a relationship between color preferences and personality. Finally, Picco and Dzindolet (1994) failed to show that color preferences are related to self-descriptions, even when controlling for social desirability (e.g., participants favoring green and blue were not more introverted than those favoring red or yellow). Taken together then, support for Luscher's theory about the correlates of color preference is mixed at best.

The following addresses topics similar to the above, with two major differences. Firstly, a different color test is used that derives from the Dewey Color System (Sadka, 2000), which is currently in commercial use (cf., Sadka, 2004). This Dewey Color System Test uses distinctive spectrum divided color hues. In particular, starting with yellow, blue and red, they are mixed to visually create shades of green, purple and orange with no visible characteristics of the primary shades. Likewise, the intermediates are fabricated from a primary and a secondary color. Black, white, and brown are also added. Although the Dewey Color System Test also covers preferences among primary, secondary, achromatic colors, and intermediate colors, the major task to be performed in this test is the ordering of 15 colors according to respondent's preferences. In fact, it is the preference order of these fifteen choices that constitutes the basic predictor variable in this study. These colors can be described as teal, purple, brown, red-orange, yellow, magenta, orange, white, lime green, blue, gold, green, black, indigo, and red. The entire test can be seen in color at the website

Secondly, Luscher's own research, as well as later research inspired by his work, relied exclusively on standard statistical tests and correlations. Thus, hypotheses were tested mainly based on piece-meal analyses of counts and average preferences. Data were then analyzed according to the analytical options provided by standard statistical methodology, which treats each choice as the basic unit of analysis. By contrast, we hypothesize that the identification of global patterns of color preferences is far more informative than a series of piece-meal analyses. In particular, individual choices should be combined into patterns that simultaneously encompass respondents' likes and dislikes, while not giving undue weight to the evaluation of a particular color sample. In other words, a contextual approach is needed that identifies and captures specific color combinations, while ignoring irrelevant variation in isolated choices.

Additionally, we hypothesize that the failure of conventional statistical techniques derives from that the fact that similar personality characteristics--as well as sex differences--may be reflected in different patterns of color preferences. Thus, methods are needed that explicitly allow for the possibility that prediction is many-to-one. For instance, high creativity might be characterized by preferring, say, yellow, first, or by preferring red first and blue last. Accordingly, it should be possible to identify all color preference configurations that are characteristic of a particular personality simultaneously rather than identifying such configurations one at-a-time.

As we have shown elsewhere (Lange, 1996), neural nets (c.f., Galant, 1994) provide such an approach, as neural nets are capable of inferring (nearly) identical outputs based on different as well as incomplete inputs. Also, while other methods (e.g., catastrophe models, see, Lange, Oliva, & McDade, 2001) also possess the desired many-to-one properties, their use requires far greater insights into the nature of the relation between color choices and personality than we currently possess.

Neural nets can identify predictive color preference configurations by estimating a set of parameters from empirical data sets. Unfortunately, the many-to-one nature of the relation between nets inputs and outputs makes it extremely difficult to provide a unique and compelling interpretation of these parameters. Efforts to do so have mostly failed (for an overview, see, e.g., Galant, 1994). To make matters worse, these parameters are not necessarily unique. For this reason, in this research we treat neural nets mainly as a "black-box" prediction method. The procedures for establishing the many-to-one properties of the predictive powers of color choices are described in detail below in the method section to Study I.

The data analyzed in Studies I, II, and III below were gathered by a commercial testing center headed by a former human resource director at a leading aerospace company. This individual was charged with administering the Strong Interest Inventory, Cattell's 16PF, or both tests, to volunteers who participated in a series of Career Transition Clinics provided by an Atlanta, GA church. As part of a battery of occupational tests, four professional psychologists administered Strong's Interest Inventory together with a paper-and-pencil version of the Dewey Color System Test (n = 885). Also, 1010 individuals completed Cattell's 16PF and the Color Test. These psychologists processed and evaluated the answers, and respondents returned to the Clinic after two weeks to learn about the results. Upon completion of all data gathering, Dewey Color System Test results were available for 1245 individuals.


The latest version of the Strong Vocational Interest Blanks--called the Strong Interest Inventory (Harmon, Hansen, Borgen, and Hammer, 1994)--is a questionnaire consisting of 317 questions which inquire about respondents' interest in a wide range of items associated with occupations, occupational activities, hobbies, leisure activities, school subjects, and preferred types of people. While the Strong Interest Inventory provides a variety of other information as well, this research focused on respondents' general orientation to work as reflected by six separate factors, dubbed Basic Interest Scales (BIS). In particular, a distinction is made (Harmon, et al., 1994, pp. 70-78) among Realistic BIS (including agriculture, nature, military, athletics, and mechanical activities), Investigative BIS (science, mathematics, and medical science), Artistic (music/ dramatics, art, applied arts, writing, and culinary arts), Social BIS (teaching, social and medical service, and religious activities), Enterprising (public speaking, law/politics, merchandising, sales, and organizational management), and Conventional BIS (data management, computer activities, and office services).


Subjects. The sample of 885 potential career changers who completed both the Strong and the Dewey Color Test consisted of 524 women and 359 men, and 2 persons with unknown sex. The respondents' average age was 32.1 years (Median = 29.0, SD = 12.9 years) with missing age information for 28 individuals.


Neural Net The results reported here derive exclusively from analyses based on standard neural nets using backpropagation based on standard logistic squashing, momentum, and varying learning rates (cf. Gallant, 1994). The software used was a Delphi 6 implementation of the earlier software described in Lange (1996). Throughout, one intermediate layer (i.e., in addition to the input and output layers) was used. To facilitate later use of the resulting neural net, the Delphi software also produced a standalone Pascal program that can be compiled into an efficient implementation of the forward pass of the neural net for use as a .NET web component.


The six panels of Figure 1 are scatter plots of the relation between the actual--but squashed--Strong BIS (X-axis) and the squashed BIS values as predicted by the neural net (Y-axis) from the color test inputs. Visual inspection reveals that prediction is quite successful for each of the six BIS. As is shown in Table 1, the finding of sizeable correlations between the actual and predicted squashed BIS values supports this interpretation. For instance, the median Pearson correlation across all BIS is 0.68, and the lowest correlation, i.e., for Social BIS, is 0.54. Further, given the very nature of the color test, it is at least suggestive to note that the highest correlation (r = 0.73) obtains for the Artistic BIS.

We conclude therefore that respondents' color preferences as assessed by the Dewey Color System Test are indeed powerful predictors of all Basic Interest Scales of the Strong Interest Inventory. These findings thus open the exciting possibility that people's vocational interests can be inferred quite accurately from their color preferences.

Many-to-One We hypothesized earlier that neural nets provide a contextual approach that identifies and captures the predictive value of specific color combinations, while ignoring irrelevant variation in isolated choices. In the present case, this is expected to produce a many-to-one type of relation between color choice and other variables like the Strong's BIS. That is, similar BIS patterns may occur together with different color choice patterns--and, vice versa, dissimilar BIS patterns may occur for rather similar color preferences.

The preceding hypotheses were tested by creating maximally different BIS patterns using SPSS 12's (2003) Two-Step clustering method. This method yielded two different clusters of similar individuals, with 324 and 561 cases, respectively, which were compared in terms of their most and least favorite color choices. As predicted, least favorite color choice is not related to cluster membership ([chi square](14) = 14.02, p > .20), whereas the relation between cluster membership and most favorite color is marginally significant ([chi square](14) = 25.04, p < .05). However, color choice hardly predicts cluster membership, as is indicated by the low and significant asymmetric lambda values (in both cases, [lambda] = 0.01, p > 0.09). Thus, in support of the many-to-one hypothesis, even when maximally different Strong configurations are constructed (i.e., via the cluster analysis) highly similar color preferences continue to prevail.


The Sixteen Personality Factor questionnaire (16PF) is a 185-item instrument that comprises 16 personality primary factor scales originally identified by Raymond Cattell (cf., Catell, Eber, & Tatsuoka, 1970). Historically, letters only identified these factors, but it has become customary to add descriptive labels also (Russel & Karol, 2002). The complete list of factor designation is: Factor A (Warmth), Factor B (Reasoning), Factor C (Emotional Stability), Factor E (Dominance), Factor F (Liveliness), Factor G (Rule-Consciousness), Factor H (Social Boldness), Factor I (Sensitivity), Factor L (Vigilance), Factor M (Abstractedness), Factor N (Privateness), Factor O (Apprehension), Factor Q1 (Openness to Change), Factor Q2 (Self-Reliance), Factor Q3 (Perfectionism), and Factor Q4 (Tension).

From these sixteen factors, as well as from particular combinations of low or high factor scores, it is possible to derive other personal characteristics as well. However, because such combinations can be always derived once the sixteen primary factors are known, this topic is not further pursued here.

Procedure The 16PF was administered in the same context as the Strong, and details are therefore not repeated. A subset of 1010 volunteers, which partially overlapped with those taking the Strong Interest Inventory in Study I, completed both the 16PF and the Dewey Color System Test. This sample consisted of 602 women and 403 men, and 5 individuals with unknown sex. The average age was 32.7 years (Median = 30.0, SD = 13.6 years)--but with missing age information concerning 27 individuals.


Analogous to the procedure followed for the Strong, respondents' scores on each of the sixteen primary factors were transformed to z-scores, and then squashed using a logistic function. These sixteen quantities were then predicted from respondents' color choices on the Dewey Color System Test. The values of the correlation between the actual and predicted values of these factors are listed in Table 2. The associated sixteen scatter plots qualitatively resemble those in Figure 1. However, to conserve space, these plots are not included here.

It will be clear that respondents' color preferences provide powerful predictors of their personalities. The highest correlation is obtained for Factor H (Social Boldness), and the magnitude of this correlation (r = 0.68) is comparable to the highest value obtained in Study I for the Strong Interest Inventory. Also, the median correlation in this study (0.51) is similar to that found earlier. However, the correlation of the worst performing variable--Factor O (Apprehension) with r = 0.33--falls well below the worst performing BIS (i.e., Social) of the Strong Interest Inventory. Nevertheless, given the results for the Strong Interest Inventory, the findings for the 16PF clearly reinforce the notion that color preferences as assessed by the Dewey Color System Test are indeed valid indicators of personality.

Many-to-One Applying SPSS' two-step clustering method to respondents' 16 primary factors yielded three distinguishable clusters of similar individuals, with 403, 365, and 242 cases, respectively. Respondents in the three clusters were then compared in terms of their most and least favorite color choices. As in Study I, patterns of the three groups' preferences were highly similar with respect to their most favorite colors ([chi square](28)= 31.70, p > .20). However, the groups showed a marginally significant difference with respect to their least favorite colors ([chi square](28)= 44.97, p < .05). In either case, group membership could not be predicted from color choice, as is indicated by the low and significant asymmetric lambda values (in both cases, [lambda] = 0.04, p > 0.09). In other words, respondents' most important color choices (i.e., their most and least favorable colors) in the three groups are highly similar while showing negligible predictive power. Yet, the neural net findings indicated that the color preferences predict the 16 primary factors with considerable accuracy. In support of the many-to-one hypothesis it thus appears that when maximally different 16PF configurations are constructed (i.e., via the cluster analysis) highly similar color preferences continue to prevail.


The data of Studies I and II were combined to investigate possible sex differences between men and women. The resulting available sample consisted of 1245 individuals with an average age of 31.49 years (Median = 29.00, & SD = 13.46 years). Six people with unknown sex were excluded, leaving a total of 1239 individuals for study (747 women and 492 men).


Preliminaries. To provide an overview of the data, Table 3 shows the distributions of the fifteen colors (rows) by sex (upper vs. lower panel) and ordinal choice number (columns). Please note that to conserve space only columns 1 through 3 and columns 13 through 15 are shown because these represent the three most and least popular color choices, respectively. Column 1 of this table indicates that women's favorite color is Green (but closely followed by Yellow and Brown). Men ranked Purple first, but their favorite choice is closely followed by Green. Also, as was already observed by Seefeld (1973), women prefer yellow far more often than do men ([chi square](1) = 41.28, p < .001) relative to the other colors that were presented. The last column (Column 15) shows that men and women agree that Indigo is their least favorite color.

Chi-square tests over the cross tables defined by the top and bottom parts of each column in Table 3 indicate that women and men have different color preferences. In particular, the boldface values in the next to last row reveal that men and women showed statistically significant different color preference distributions in each of the 6 columns shown (p < .001). (Note: The distributions in fact differed in all columns, except 9 and 10.) Unfortunately, the extremely low asymmetric [lambda] values in the bottom row--which reflect the increase in predictive power when sex is taken into account relative to the predictive power afforded by the marginals alone--indicate that the statistical differences are so weak as to be of little practical relevance in predicting which colors will be liked more by men vs. women.

Neural Net Approach The approach followed here is completely analogous to that of Studies I and II, except that sex was dummy-coded as "low" (men) vs. "high" (women). Specifically, the values -0.95 vs. 0.95 were used without squashing (cf., Galant, 1994). The bi-serial correlation obtained between these dummy-codes and their predicted values is low, but statistically significant (r = 0.38, p < .001).

The preceding does not mean that gender differences such as the above are very meaningful in actual practice. For instance, sex was dichotomized using 0 as the cutoff (i.e., predicted values < 0 were designated as "men" and values [greater than or equal to] 0 were designated as "women"). The resulting combinations of actual vs. predicted classifications are listed in Table 4 which illustrates the significant dependency between the actual and predicted values ([chi square](1) = 183.25, p < .001). Yet, in this table one cannot reliably infer respondents' sex from the neural net's predictions ([lambda] = 0.0).

Also, the neural net also erroneously implies that most cases should be "men" (i.e., the least frequently occurring group), and this causes its performance to be at the chance level. In particular, simply declaring all persons to be of the most frequently occurring subgroup (i.e., women) yields 747 of 1239 (= 60.3%) correct classifications, while using the neural net produces just three additional correct classifications (i.e., 750 of the 1239 cases = 60.5% are on the diagonal of Table 4).

To investigate whether these findings result from the particular coding of men vs. women or the number of intermediate layers being used, additional runs were performed which varied these factors. However, no substantial differences were observed.


As was anticipated, the present findings clearly indicate that people's color preferences, as assessed via the Dewey Color System Test, indeed provide meaningful information about their personalities, interpersonal styles, and behaviors. In particular, the data indicated that this test predicted with considerable precision all six of the Basic Interest Scales of Strong's Interest Inventory (Harmon, et al., 1994), and nearly all of the sixteen Primary Factors of Cattell's 16PF (Russel & Karol, 2002). As such, the present findings show far greater consistency than do those obtained in earlier research inspired by Luscher's (1971) work (cf., French & Alexander, 1972; Picco & Dzindolet, 1994; Seefeldt, 1979; Stimpson & Stimpson, 1979; Stone, 2001, 2003).

As was already found earlier by Seefeld (1973), men prefer yellow far less often than do women. Despite the finding of several statistically significant differences between men and women, our neural net approach proved of little use in inferring respondents' color preferences from their sex or vice-versa. It appears that the statistically significant associations are simply too weak to yield clearly identifiable effects. Note that this situation may well explain the inconsistent pattern of sex differences observed in earlier research (cf., Seefeldt, 1979 vs. Stimpson & Stimpson, 1979). By contrast, the findings provided strong support for the many-to-one hypothesis. Specifically, even when maximally different respondent configurations are constructed via the cluster analysis, highly similar color preferences continue to prevail. Note that our test of this hypothesis essentially relies on non-rejection of [H.sub.0]. However, the observed [lambda] statistics were so low as to be meaningless, and there is little reason to believe that these low values are the result of sampling biases.

A number of additional issues remain. For instance, it is not clear at this point whether our findings regarding personality must be attributed to the differences between the colors contained in the Dewey Color System Test and Luscher's color test, whether neural nets simply provide a superior method of analysis relative to the simpler statistical methods employed in previous research, or whether the particular test and analysis combination used here is responsible. Also, while neural nets form a holistic oriented approach in which choices are combined into patterns that simultaneously encompass respondents' likes and dislikes, neural nets have the disadvantage of being a black box only, i.e., they must be used "as is." In particular, neural net's weights are difficult to interpret directly (Gallant, 1994), and short of actually running the neural net even knowing all its weights provides little guidance in identifying which patterns of color preferences are associated with which particular personality traits.

Note that we have essentially bridged the relation between two types of tests, i.e., color preferences and the personality related variables assessed by the Strong and the 16PF. However, we have not yet established that the Dewey Color System Test actually predicts the behaviors for which these personality tests are typically used. Thus, more extensive validation should consider using color preferences directly to predict variables such as job satisfaction, leadership potential, etc. Given the promising findings, we believe that the present research shows that (at least some forms of) personality assessment can be augmented--and possibly be replaced--by a short and simple color preferences test together with a neural net to interpret these preferences. The further promise that such tests are also largely culture and language free justifies the cost and effort involved in conducting further study in this area. We recommend, however, that such research be informed by insights gained in related areas of research, as is illustrated by the discussions in a recent issue of the APA Monitor (American Psychological Association, 2003).

Finally, the present findings may have implications beyond standard personality assessment as well. For instance, observers can (and do) infer characteristics of people based on such cues as physical appearance, clothing, nonverbal behavior, facial features, appearance of bedrooms and offices (Gosling, Ko, Mannarelli, & Morris, 2002), websites (Vazire & Gosling, 2004), and music preferences (Rentfrow & Gosling, 2003). Thus, to the extent that people's theories about others' use of color play similar roles, the study of color preferences could be expanded to areas such as advertising, web-design, on-line dating, and interpersonal perception in general.


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Rense Lange

Integrated Knowledge Systems, Springfield, IL

Jason Rentfrow

University of Cambridge, UK

Author info: Correspondence should be sent to: Dr. Rense Lange, 107 Carefree Drive, Chatham, IL 62629. e-mail:
TABLE 1 Pearson Correlations Between Actual and Predicted BIS
Values (Study I)

Strong Basic Interest Scale (BIS)  Pearson Correlation

Realistic                                  0.70
Investigative                              0.64
Artistic                                   0.73
Social                                     0.54
Enterprising                               0.66
Conventional                               0.70

Note: All correlations shown are statistically
significant at p < .001.

TABLE 2 Pearson Correlations Between Actual and
Predicted Primary Scale Values (Study II)

16 PF Primary Factor Scale        Correlation

Factor A (Warmth)                     0.57
Factor B (Reasoning)                  0.50
Factor C (Emotional Stability)        0.59
Factor E (Dominance)                  0.51
Factor F (Liveliness)                 0.65
Factor G (Rule-Consciousness)         0.52
Factor H (Social Boldness)            0.68
Factor I (Sensitivity)                0.50
Factor L (Vigilance)                  0.57
Factor M (Abstractedness)             0.63
Factor N (Privateness)                0.50
Factor O (Apprehension)               0.33
Factor Q1 (Openness to Change)        0.49
Factor Q2 (Self-Reliance)             0.63
Factor Q3 (Perfectionism)             0.49
Factor Q4 (Tension).                  0.47

Note: All correlations shown are statistically
significant at p < .001

TABLE 3 Distribution of Color Preferences for Women and Men
for Extreme Color Choices (Study III)

                  Choice order (1 = first, 15 = last)

             Color           1        2       3       ...

Women        teal                61      42      39
(n = 747)    purple              82      82      71
             brown              108      73      64
             red-orange          33      36      48
             yellow             108      82      96
             magenta             12      21      21
             orange              52      78      59
             white               61      58      80
             lime green           8      21      25
             blue                28      41      42
             gold                 8      29      16
             green              110     109      98
             black               54      39      48
             indigo              10      14      15
             red                 12      22      25

Men          teal                27      27      29
(n = 492)    purple             113      92      64
             brown               86      69      65
             red-orange          27      29      39
             yellow              22      21      38
             magenta              5      11      23
             orange              10      19      20
             white               23      31      37
             lime green           5       7      11
             blue                 6      12      12
             gold                 2      15      19
             green              105      99      74
             black               40      33      33
             indigo               5       5      13
             red                 16      22      15

[chi square] (14) (a)        101.36   71.13   43.52
[lambda] (Color dependent)     0.01    0.00    0.00   ...

               Choice order (1 = first, 15 = last)

             Color           13       14      15

Women        teal               59      61      39
(n = 747)    purple             17       8       4
             brown              24      13      14
             red-orange         30      28      15
             yellow             15      16      10
             magenta            87      91      52
             orange             37      48      36
             white              42      45      30
             lime green         90      81      66
             blue               55      76     121
             gold               76      53      34
             green               6       3       6
             black              53      62      66
             indigo             74     106     177
             red                82      56      77

Men          teal               35      36      31
(n = 492)    purple              5       4       2
             brown               7       3       5
             red-orange         14      10       1
             yellow             33      29      13
             magenta            38      32       5
             orange             52      52      55
             white              31      36      19
             lime green         60      77      36
             blue               53      41     113
             gold               33      13      13
             green               3       1       1
             black              34      36      43
             indigo             61      81     130
             red                33      41      25
[chi square] (14) (a)        58.22   53.95   74.98
[lambda] (Color dependent)    0.00    0.00    0.00

(a)  Values in boldface are statistically significant at p < .001.

TABLE 4  Respondents' Actual vs. Predicted Sex (Study III)


Predicted      Women        Men         Total

Women           275         17           292
Men             472         475          947
Total           747         492         1239
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Author:Lange, Rense; Rentfrow, Jason
Publication:North American Journal of Psychology
Article Type:Report
Date:Dec 1, 2007
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