外文翻譯---內(nèi)隱和外顯職業(yè)性別刻板印象

1、<p>　　Implicit and Explicit Occupation-sex stereotypes</p><p>　　Abstract This study was designed to compare implicit and explicit occupation-sex stereotypes for three occupations (engineer, accountant,

2、and elementary school teacher). These occupations represented the end points and middle of a masculine-feminine continuum of explicit occupation-sex stereotypes. Implicit stereotypes were assessed using the Implicit Asso

3、ciation Test (IAT), which is believed to minimize self-presentational biases common with explicit measures of occupation-sex stereotypes. IAT </p><p>　　Keywords Occupation-sex stereotypes .Implicit stereotyp

4、es .Stereotypes .Implicit Association Test</p><p>　　Popular beliefs have long held that because of their stereotyped traits and temperaments men and women are suited for different kinds of occupations. One o

5、f the earliest empirical examinations of these occupation-sex stereotypes was conducted by Shinar (1975) who showed that college students thought that some occupations required masculine traits, while others required fem

6、inine traits. The method that Shinar (1975) and others (Beggs & Doolittlo, 1993; Whito, Kruczok, Brown,&Whito, 1989) used to </p><p>　　Persons acquire stereotypes, in part, through personal experienc

7、e. But because stereotypes are part of the beliefs and shared assumptions that societies have about different types of people and groups, they are also part of the society's collective knowledge. In order for a socie

8、ty to socialize its members, these stereotypes must be explicitly, even if subtlety, taught (0tangor&ghallor, 1996). Whether stereotypes are individual or cultural in origin, the emphasis on explicit beliefs is not s

9、urpr</p><p>　　It is increasingly clear that implicit processes are important in stereotyping. Greenwald and Banaji (1995, p. 15) have defined implicit stereotypes "as the introspectively unidentified (o

10、r inaccurately identified) traces of past experience that mediate attributions of qualities to members of a social category." Implicit stereotypes and other implicit cognitive forms reflect the continuing influence

11、of past experience and learned associations. They are the remaining influence of explicit beliefs</p><p>　　A strategy for describing implicit stereotypes and other implicit cognitions is provided by the Impl

12、icit Association Test (IAT; Greenwald, McGhee,&Schwartz, 1998). The IAT assesses implicit stereotypes by measuring their underlying automatic associations with other concepts. This is done by first establishing the s

13、peed with which responses can be made to a computer-presented target-concept and an associated attribute. Although the IAT procedure will be explained later in greater detail, consider</p><p>　　Explicit trai

14、ts for occupational gender stereotyping are, by contrast, assessed using familiar Likert-type rating scales. The most common explicit traits measured by these scales involve whether an occupation should be considered mas

15、culine, neutral, or feminine. Nursing, for example, has been consistently rated as a "feminine" occupation (White et al., 1989). Essentially two explanations for these explicit stereotypes have been made. One i

16、s that certain jobs require personality traits more likel</p><p>　　Have the explicit occupational stereotypes that Shinar (1975) identified 30 years ago changed since her study? Results have been mixed. Ster

17、eotypes attached to some occupations appear to have become more gender-neutral. This is especially true of occupations where the ratio of male to female practitioners has become more balanced. Other occupations, usually

18、those with skewed sex ratios, remain gender-typed (e.g., Beggs&Doolittle, 1993; Cejka &Eagly, 1999; White et al., 1989). Yet, methods used i</p><p>　　In the present study we considered implicit occup

19、ational stereotypes for three occupations (i.e., engineer, accountant, and elementary school teacher). These occupations represent the middle and end points of a masculine-feminine continuum of explicit occupational ge

20、nder stereotypes identified by White et al. (1989) in their replication and extension of Shinar's (1975) original work. In order to allow comparisons with implicit stereotypes, measures of explicit stereotypes for t

21、hese occupation</p><p>　　Materials and Methods</p><p>　　Participants</p><p>　　A total of 156 students from two colleges within the university participated voluntarily. Most of the s

22、tudents (66 men, 55 women) were business majors. The rest (12 men, 23 women) studied education. The mean age was 21.8 LSD一4.7). Students reported their ethnicity as follows: Caucasian American (85.9%), African American

23、(9.6%), Asian American (1.3%), Native American (.6%), Hispanic American (1.9%), and Other (.6%). They indicated their class standing to be: freshman (3.2%), sophomore (25%), jun</p><p><b>　　Procedure&l

24、t;/b></p><p>　　An experimenter greeted participants and explained that the study examined associations between words and occupations. Participants learned that all responses would be made on a desktop com

25、puter. After giving their informed consent to participate, participants followed directions shown on the computer screen. The program first assessed implicit stereotypes, followed by explicit stereotypes, and demographic

26、 information.</p><p>　　The first step in creating the IAT scores involved having the participants discriminate between two occupation targets (e.g., engineer-elementary school teacher) and between the conce

27、pts associated with them. In one version, "Engineer" appeared on the left side of the computer screen and "Elementary School Teacher" appeared on the right. Centered below the two targets was a random

28、ly selected concept that pilot testing had shown was associated with one of the targets. The student's task was to p</p><p>　　In a similar manner, participants discriminated between the attribute male or

29、 female. In one</p><p>　　version, "Male" appeared on the left side of the screen and "Female" appeared on the right with one of ten randomly chosen names centered below. Assuming the fir

30、st name was "Matthew," the correct response would be the left (f) key. As in step one, correct responses were followed by the next trial; incorrect responses received an error message for 400 ms, followed by th

31、e next trial. The third step combined steps one and two such that a response key was shared. In the current example, either the </p><p>　　The fourth step reversed the positional association for male and fema

32、le. On this step and in this example, the word "Female" appeared on the left of the screen and "Male" on the right for a total of ten trials. In contrast to step two, a correct response to a masculine

33、 name such as "John" would require pressing the right (j) key. Correct responses and errors were treated as in step one. The fifth step was similar to step three, yet included the target with the reversed attri

34、butes (e.g., engineer</p><p>　　After participants had completed the IAT they completed the Likert rating scales for the three target occupations: engineer, accountant, and elementary school teacher. Each occ

35、upation appeared individually on the screen. Participants indicated their ratings for each occupation by clicking the appropriate scale points with the computer mouse. Participants' final tasks were to enter their se

36、x, ethnicity, class standing, and age when prompted by the program. The last screen of the program contained</p><p>　　Stereotypes are inferred from relative response speeds to the IAT’s tasks. The quicker re

37、sponses that are anticipated to step three's stereotypically congruent engineer-male pairs (and elementary school teacher-female pairs) than to step five's stereotypically incongruent engineer-female pairs (and e

38、lementary school teacher-male pair) would imply that engineer-male is more strongly associated and readily retrieved than engineer-female. Responses to word pairs that are not congruent with existing</p><p>

39、　　The IAT procedure we used resulted in 20 trials for the combined tasks. Although many researchers have used larger numbers of trials (e.g., 40 trials), our decision to do so was occasioned by the requirement that uniqu

40、e words generated by our pilot study participants be used for each occupation. Pre-testing had showed overlap across occupations when ten words were requested; hence, we requested five words from our participants. Greenw

41、ald et al. (1998) noted that IAT magnitudes were unchanged whe</p><p><b>　　Results</b></p><p>　　The improved scoring algorithm recommended by Greenwald, Nosek, and Banaji (2003) was

42、used to calculate D for each participant's IAT responses. D is similar to Cohen's (1992) effect size, d, in that the differences between IAT test steps or blocks are standardized by their pooled standard deviatio

43、n. All responses in the two test blocks were considered for these calculations. Trials with latencies greater than 10,000 ms and participants with more than 10% of responses 300 ms or less were eliminat</p><p&

44、gt;　　The resulting D values are reported in Table 1 .These data are grouped by three target occupation comparisons (e.g., engineer vs. accountant). Each target occupation is further defined by the gender presentation ord

45、er of the job target (e.g., male engineer vs. female accountant contrasted with female engineer vs. male accountant).The influence of these variables (target occupation pairs ,gender stereotype congruency presentation or

46、der) was examined in a two-way ANOVA with D serving as the depend</p><p>　　Explicit Stereotypes</p><p>　　Ratings from the three studies for elementary school teacher do not statistically differ

47、(5.6, 5.5, and 5.6, respectively). Explicit ratings for the three target occupations manifest stereotyped perceptions and are shown in Table 2. On the 7-point rating scale (1=masculine, 4=neutral, 7= feminine), mean rati

48、ngs for engineer were the most masculine (2.3), accountant was rated as nearly neutral (3.6), and elementary school teacher was rated as the most feminine (5.6). It is possible to place these</p><p>　　Implic

49、it and Explicit Measures</p><p>　　Correlations among implicit and explicit measures are shown in Table 3. All of these correlations are based on difference scores. In the case of the implicit measures, these

50、 are the scores originally shown in Table 1 .A positive value reflects a preference for the gender stereotypic comparison pair, e.g., male engineer and female elementary school teacher. Explicit scores reflect the absolu

51、te value of the difference between each of the three pairs on the masculinity-femininity scale. A higher sc</p><p>　　Correlations among explicit scores indicate that participants who stereotyped engineers an

52、d elementary school teachers also stereotyped accountants and elementary school teachers, 0.76, p<.O1.There was a similar positive correlation between scores on the engineer-elementary school teacher and the engineer-

53、accountant comparisons, 0.46, p<.O1.In contrast, stereotyping scores on the engineer-accountant comparison were inversely associated with stereotyping scores on the accountant elementary school</p><p><

54、;b>　　.</b></p><p>　　Discussion</p><p>　　The occupation of accounting presents an interesting example of how assessment of implicit processes may add to understanding occupation-sex ster

55、eotypes. Explicit ratings for this occupation have shown it to be increasingly and consistently rated as a "neutral" occupation (Beggs& Doolittle, 1993; White et al., 1989). Further, the number of women who

56、 are now accountants exceeds those who are men. Given these ratings and the high percentage of women accountants, one might assume that the 1970s ste</p><p>　　In contrast, the largest IAT effect is in keepin

57、g with the explicit stereotype results: respondents were able to identify male engineers and female elementary school teachers more quickly than when the job occupants were of the other sex. These two occupations thus ap

58、pear to be strongly gender stereotyped. Further, the nature of these stereotypes is in keeping with the explicit ratings. Engineering is stereotyped as a masculine occupation and elementary school teaching is stereotyped

59、 as a femini</p><p>　　內(nèi)隱和外顯職業(yè)性別刻板印象</p><p>　　摘要 本研究的目的在于比較三種職業(yè)(工程師、會計師、小學教師)的內(nèi)隱和外顯職業(yè)性別刻板印象。這些職業(yè)是比較有代表性的說明男性女性外顯職業(yè)性別刻板印象的。內(nèi)隱刻板印象是通過內(nèi)隱聯(lián)想測驗（IAT）來進行鑒定的。內(nèi)隱聯(lián)想測驗是能夠將自我觀念偏見減到最少，使之與外顯方法測到的職業(yè)性別刻板印象一樣。內(nèi)隱

60、聯(lián)想測驗結果呈現(xiàn)為大多數(shù)性別刻板的職業(yè),工程師(男性)和小學教師(女性),可以與外顯的等級進行比較。過去幾乎沒有就刻板印象對比方面的一致意見。結果表明,比起外顯測量到的結果顯示，內(nèi)隱方面，會計師更被視作較男性的職業(yè),并對這些職業(yè)性別刻板印象遞減的報告表示質疑。</p><p>　　關鍵詞 職業(yè)性別刻板印象 內(nèi)隱刻板印象 刻板印象 內(nèi)隱聯(lián)想測驗</p><p>　　長期以來的民間信仰

61、認為，由于人們的模式化特點和氣質，男人和女人適合不同種類的職業(yè)。最早期的經(jīng)驗主義的關于職業(yè)性別刻板印象的測驗是由展示了大學生認為有些職業(yè)需要男性的特質，另外一些則需要女性的特質的Shinar（1975）所指揮。Shinar（1975）和其他人（Beggs和Doolittlo，1993;Whito,Kruczok,Brown和Whito,1989）用于研究職業(yè)刻板印象的方法是測量所有特質下的刻板印象這種傳統(tǒng)的方法。事實上，這種方法最早是由

62、Katz和Braly（1935）用于他們非常早期的關于國民刻板印象的工作中。這種方法對刻板印象的治療就像一些特質或者是觀點能夠使被調查者有意識的和外顯的同不同小組的其他成員聯(lián)系在一起。大多數(shù)概念性治療的刻板印象,所有流行的帳戶,都強調這些外顯的過程及其內(nèi)容。</p><p>　　人的刻板印象,在某種情況下,通過個人經(jīng)歷而獲得。但由于刻板印象是部分對信仰和共享的假設，即社會上擁有不同類型的人和小組，他們也是社會集體

63、知識的一部分。為了使一個社會社會化，這些刻板印象必須被明確的，即使是微妙的傳授(0tangor和ghallor, 1996)。不論刻板印象是個體的還是文化的起源，對明確信念的強調并不令人驚訝，刻板印象的內(nèi)容對于使用刻板印象和以他為目標都有很大的內(nèi)在興趣。甚至當客觀的是錯誤的，刻板印象還是可以簡化社會認知和對社會交往提供指南。越來越清晰的表明內(nèi)隱過程對于刻板印象的重要性。Greenwald and Banaji (1995, p.15)對

64、內(nèi)隱刻板印象定義為好反省的、未識別的，或者是不準確的辨認的過去經(jīng)驗的痕跡，他會間接影響社會范疇成員的特性。內(nèi)隱刻板印象和其他內(nèi)隱認知形式反映了過去經(jīng)驗和習得的相關的持續(xù)影響。即使有意識的拋棄或者拒絕，他們是仍然影響那些持續(xù)影響認識和直覺的明確信念。這種影響經(jīng)常介于有意識的控制和可能被叫喚或者待處理的短暫呈現(xiàn)的刺激之間(cf., Fazio,Sanbonmatsu,Powell,&Kardes, 1986)。甚至在那些明確<

65、/p><p>　　一個用于描述內(nèi)隱刻板印象和其他內(nèi)隱認知的策略是由內(nèi)隱聯(lián)想測驗所提供(IAT; Greenwald, McGhee,&Schwartz, 1998)。內(nèi)因聯(lián)想測驗評估內(nèi)隱刻板印象是通過測量他們內(nèi)在自動地和其他一些概念聯(lián)系起來。首先第一步是設定對呈現(xiàn)目標概念和相關屬性的計算機作出反映的速度。雖然內(nèi)隱聯(lián)想測驗的數(shù)據(jù)可以由之后大量的細節(jié)來解釋，考慮到現(xiàn)在對于目標概念“護士”作出反應，其屬性和“女性”

66、配對。這是一種常見到刻板印象聯(lián)想，要求快速作出反應。這是因為“護士”和“女性”之間強大的聯(lián)系能夠促進快速檢索和認知過程。然后IAT程序顛倒視覺圖像以使目標概念“護士”和屬性詞“男性”產(chǎn)生配對。這不是固有的聯(lián)想，排除被試最佳的意識作用，應該歸結于緩慢反應。優(yōu)先內(nèi)隱聯(lián)想或是刻板印象都會影響被試的回答。更進一步說，這種作用的強度會受到先前存在的刻板印象強度的影響。如果這種刻板印象很強大或被很好的建立，這種作用將會更大。如果這種刻板印象是比較弱

67、得，那么因為不存在先前的聯(lián)想，作用就會減小或者不存在。</p><p>　　相比之下，職業(yè)性別刻板印象明顯的特征是用熟悉的李克特量表進行評估。用該量表最明顯的特征包括是否一個職業(yè)該被認為是男性的、中性的，或是女性的。比如，護士一貫被列入女性職業(yè)中(White et al., 1989)。本質上，對外顯刻板印象有兩種解釋。一種是某些工作需要更多的是某一種性別上的人格特質。比如說，如果一個好的護士應該懂得關愛，那么女

68、性就比男性更多的被視為有關愛的能力，然后就形成女性能夠較男性更好的適合護士這個工作(Spence& Helmreich, 1978)。第二種解釋為在職業(yè)中性別很普遍。盡管男性護士的人數(shù)在不斷上升，絕大多是的護士還是女的。經(jīng)觀察確認，女性在護士行業(yè)中占主導。從而形成女性主導護士這個行業(yè)的刻板印象。</p><p>　　自從她的研究開始，希納爾三十年前鑒定的外顯職業(yè)刻板印象到現(xiàn)在有變化嗎?結果被整合了?？贪逵?/p>

69、象隸屬的一些職業(yè)好像變得性別中性化。有更多的男性從事過去女性的工作，在這一點上特別明顯。其他的過去常常存在曲解的性別比例的的職業(yè)，仍舊保持原有的性別典型(e.g., Beggs&Doolittle, 1993; Cejka &Eagly, 1999; White et al., 1989)。但是，用于先前研究的方法現(xiàn)在都集中于明確的刻板印象有益于社會期望和自我表象作用。即使那些自覺接受職業(yè)性別刻板印象的人也可能比較猶豫的

70、表達他們(cf. Yoder &Schleicher, 1996)。這些刻板印象經(jīng)常不被社會認可，至少在美國，會被認為是潛在的性別歧視(e.g., Civil Rights Act of 1964, Title VII)。而且，因為內(nèi)隱刻板印象可能在不會再被明確接受后持續(xù)較長時間，職業(yè)性別刻板印象在那些有意識否認中可能仍舊存在。使用外顯刻板印象方法的研究者可能因此低估職業(yè)性別刻板印象。</p><p>　

71、　在現(xiàn)今的研究中，我們主要考慮對三種職業(yè)（工程師、會計師和小學老師）的內(nèi)隱職業(yè)刻板印象。這些職業(yè)代表男性女性外顯職業(yè)性別刻板印象的終點和具有連續(xù)性的中點。為了比較內(nèi)隱刻板印象，對職業(yè)的外顯測量方法也被找到。那就是假定一種職業(yè)在明確的性別刻板印象上存在與之配對的最顯著的不同，那么他將會有持續(xù)較長時間的延遲，并且產(chǎn)生一個比其他疼痛都強大的內(nèi)隱刻板印象。</p><p><b>　　材料和方法</b&g

72、t;</p><p><b>　　被試</b></p><p>　　一共有一百五十六兩所大學的學生在大學自愿參加。絕大多數(shù)的學生（66個男性，55個女性）都是貿(mào)易專業(yè)。剩下的（12個男性，23個女性）學習的是教育專業(yè)。平均年齡是21.8歲。同學們表達了他們下列的種族劃分觀念：白種美國人（85.9%）、非洲美國人（9.6%）、亞洲美國人（1.3%）、本土美國人（0.6%

73、）、拉美美國人（1.9%）和其他（0.6%）。他們表示他們代表的階級分別是：大一新生（3.2%）、大二（25%）、大三（43.7%）、大四（26.3%）、畢業(yè)生（2.6%）以及其他（1.3%）。學生能夠掙得額外的學分，根據(jù)計劃參與研究的學分由他們分別的進程老師來核準。</p><p><b>　　程序</b></p><p>　　主試歡迎被試并解釋這個研究測試的是詞語

74、和職業(yè)之間的聯(lián)系。被試學習所有的反應都應該在桌子上的計算機中完成。在交給他們參與的同意書后，被試照著計算機上的說明書開始操作。這個程序首先評估的是內(nèi)隱刻板印象，之后是外顯刻板印象和人口信息。</p><p>　　創(chuàng)建IAT分數(shù)的第一步包括讓參與者區(qū)別兩種目標職業(yè)（比如工程師—小學老師）和區(qū)別他們兩者之間的概念聯(lián)系。有一種版本是，“工程師”出現(xiàn)在計算機屏幕的左邊，“小學老師”出現(xiàn)在右邊。在兩個目標的中間的下面是隨機

75、選擇的和其中一個被展示目標有聯(lián)系的引導測試一般的概念。學生的任務就是最快地對看到展示概念和左邊出現(xiàn)的目標有聯(lián)系，按左鍵；看到展示概念和右邊出現(xiàn)的目標有聯(lián)系，按右鍵。比如：如果出現(xiàn)“藍圖”，正確的反應就該是按左鍵，因為他是和目標概念“工程師”相聯(lián)系。實驗在同學們十次正確的嘗試后開始。如果反應是錯誤的，在下一個測試目標出現(xiàn)之前，“錯誤”這個詞就會在屏幕上出現(xiàn)400毫秒。</p><p>　　在一種相似的方式下，被試對

76、男女性別的屬性進行區(qū)別。有一種版本是：“男性”出現(xiàn)在屏幕的右邊，“女性”出現(xiàn)在屏幕的左邊，十分之一的隨機選擇的姓名出現(xiàn)在屏幕中間的下面。假設第一個名字是“馬修”，正確的回答是按左鍵。為了同步性，正確的反應之后緊跟著下一個測試；不正確的反應會收到一個400毫秒的錯誤信息，之后緊接著下一個測試。第三步是將第一和第二步結合起來，從而使得一個反應鍵能夠分享。在當前的例子中，要么是“工程師”或者“男性”出現(xiàn)在左邊，要么是“小學教師”或者“女性”出

77、現(xiàn)在右邊。從之前兩個概念和屬性列表中選擇的詞語出現(xiàn)在中間的下面，但是隨機的組成20條口令。假設隨機選擇出現(xiàn)詞語“阿曼達”，正確的反應是應該按右鍵（J），這樣的話錯誤信息就不會影響被試的反應，錯誤信息就不會在這回合中出現(xiàn)，在被試作出反應之后，下一個測試開始。</p><p>　　第四步是將男性和女性的聯(lián)系顛倒過來。在這一步，舉例來說，“女性”出現(xiàn)在屏幕的左邊，“男性”出現(xiàn)在屏幕的右邊，并做10次測試。為了和步驟二形

78、成對比，對男性名字例如“約翰”的正確反應就需要按右鍵（J）。正確反應和錯誤信息和在第一步中一樣的處理。第五步和第三步比較類似，但是包括了顛倒屬性的目標（比如：男性或工程師在左邊）。再次，單個的概念和屬性以隨機順序排列，做20次測試。就像步驟三一樣，錯誤信息不會出現(xiàn)。</p><p>　　在被試完成了內(nèi)隱聯(lián)想測驗之后，他們完成針對三種目標職業(yè)：工程師、會計師和小學老師的李克特量表。每一個職業(yè)都個別的在屏幕上出現(xiàn)，被

79、試通過用鼠標點擊適當?shù)谋壤謹?shù)來表明他們對每一個職業(yè)的</p><p>　　評估。被試的最后任務是根據(jù)程序的提示輸入他們的性別、種族、年級和年齡。該程序最后的屏幕是一句感謝你們的幫忙的話語，實驗者詢問被試測驗情況，感謝他們，給他們參與研究的課程學分。</p><p>　　從推論上，刻板印象和內(nèi)隱聯(lián)想測驗的反應速度有關。呈現(xiàn)合適的刻板的工程師-男性組合（小學老師-女性組合）比呈現(xiàn)刻板的不一致

80、的工程師-女性組合（小學老師-男性組合）有更快的反應，這意味著工程師-男性組合比工程師-女性組合有更強的聯(lián)系和更容易取回。對詞組的反應和現(xiàn)有的聯(lián)系之間有不一致需要更多時間和認知的努力而不是找到適合現(xiàn)有聯(lián)系的組合。</p><p>　　我們使用的內(nèi)隱聯(lián)想測驗程序導致了20次對結合任務的嘗試。即使很多調查者使用大量的嘗試數(shù)據(jù)（例如：40次嘗試），我們的決定，這樣做是由需要引起產(chǎn)生于我們初步研究的參加者過去常被用于某個

81、職業(yè)的唯一的單詞。之前的測試已經(jīng)表明當有10個詞被請求的時候，出現(xiàn)職業(yè)重疊，因此，我們我們的被試5個詞。Greenwald et al. (1998) 提出內(nèi)因聯(lián)想測驗數(shù)量是不改變的，即使在對每個種類至少使用5個樣本的情況下。Nosek, Greenwald, and Banaji (2005)最近報道內(nèi)隱聯(lián)想測驗對每個種類至少影響各種不同的少于8個、4個或2個樣本。</p><p><b>　　結果&

82、lt;/b></p><p>　　被Greenwald, Nosek, and Banaji (2003)推薦的改良的得分演算過去常常用于計算D，每個被試的內(nèi)隱聯(lián)想測驗反應。D就是類似于Cohen's (1992)的影響規(guī)格。D，就像內(nèi)隱聯(lián)想測驗步驟和被他們合并的標準誤差的不同一樣。在這兩個測驗中的所有反應都被考慮進計算中。帶有潛在因素的嘗試大于10000毫秒并且被試有多余300毫秒的10%的反應將

83、被測量。塊意味著剩余的測驗反應潛伏期和潛在測試塊得標準差將被計算。這些方法加上600毫秒，就能夠代替錯誤的潛在因素。替換錯誤和組快的區(qū)別再除以標準差。</p><p>　　結果D的價值標準在文本框一中顯示，這些數(shù)據(jù)有三個目標職業(yè)的對比組成（比如：工程師和會計師的對比）。每一個目標職業(yè)都進一步由目標職業(yè)的性別描述所規(guī)定（比如：男性工程師和女性會計師與女生工程師和男性會計師的對比）。這些變量的影響（目標職業(yè)配對、一致

84、表達的性別刻板印象）將采用以D充當因變量的兩元方差分析進行測試。這樣對目標職業(yè)的主要影響是產(chǎn)生F（2,150）=8.552，p<.001。就像預期的，工程師和會計師的內(nèi)隱聯(lián)想測試對比顯著的小于(N1=0.226, SD=0.477)其他兩種基于Tukey's HSD事后考驗的對比。工程師和小學老師(N1=0.602, SD=0.422)與會計師和小學老師(Al=0.494, SD=0.526)的對比并沒有不同。不存在對性別

85、刻板印象一致性描述的主要影響或者是有效的交互作用。</p><p><b>　　外顯刻板印象</b></p><p>　　小學老師的三項研究等級在統(tǒng)計學上(5.6, 5.5, and 5.6,respectively)。外顯的對三種目標職業(yè)刻板印象的評定等級被呈現(xiàn)在第二個文本框中。在7等級評定量表上(1=男性, 4=中立, 7= 女性)意味這對工程師的評定是最具男性的

86、特征（2.3），會計師被評定為幾乎是中立的（3.6），小學老師被評定為最具女性的特征（5.6）。可能是將這些等級置于歷史的環(huán)境中并且從另外兩個研究中使用其手段和標準差。和使用Z檢驗對比的評定等級也列在文本框2中。希納爾（1975）參與評定7等級量表中工程師為1.9，然而在White et al. (1989)或者是其他現(xiàn)有研究中被試都差不多評定為2.3。在1975年，會計師被評定為2.5,1989年為3.4,2003年為3.6，每一種手

87、段測量都有顯著不同。</p><p><b>　　內(nèi)隱和外顯測量</b></p><p>　　對內(nèi)隱和外顯的相關分析都呈現(xiàn)在文本框3中，這些相關分析都是不同分數(shù)來的。在內(nèi)隱方法測量地情況下，最初的分數(shù)是來自文本框1。</p><p>　　正值反應出對性別刻板印象比較組的偏愛。比如男性工程師和女性小學老師。外顯的分數(shù)反應了每三組男性女性比例的不同

88、的絕對值，較高的分數(shù)意味著對兩種職業(yè)較大的性別刻板印象。</p><p>　　對外顯分數(shù)的統(tǒng)計表明對工程師和小學老師有刻板印象的被試同樣對會計師和小學老師存在刻板印象（0.76，P<.01）。工程師-小學老師對照組的分數(shù)與工程師-會計師對照組的分數(shù)存在正相關(0.46,p<.01)。相反的，工程師-會計師對照組的刻板印象分數(shù)和會計師-小學老師對照組的刻板印象分數(shù)存在負相關（-0.16，P<.01

89、）。和記憶的一樣，不同職業(yè)對照組的內(nèi)隱分數(shù)是從不同的被試組中提取的。對不同內(nèi)隱聯(lián)想測驗對比的統(tǒng)計都不太可能。盡管如此，文本框2向我們展示了每個被試組的內(nèi)隱職業(yè)對比和三種外顯的職業(yè)配對。特別的值的關注的在于內(nèi)隱聯(lián)想測驗和對相同職業(yè)的外顯測量的三個統(tǒng)計。在內(nèi)隱聯(lián)想測驗中，工程師-小學老師性別刻板印象和相應的外顯對比之間的相關是0.16。內(nèi)隱聯(lián)想測驗和外顯的工程師-會計師之間的相關是0.28，p<.01。最后，內(nèi)隱聯(lián)想測驗和外顯會計師-

90、小學老師之間的相關是0.07。沒有其他的對比組是有效地。</p><p><b>　　討論</b></p><p>　　會計這個職業(yè)顯示出一個有趣的例子，就是如何評估內(nèi)隱加工過程，這可能會增強我們對職業(yè)性別刻板印象的理解。這個職業(yè)的外顯等級經(jīng)常被展示為中性職業(yè)(Beggs& Doolittle, 1993; White,1989)。進一步說，現(xiàn)在是會計的女性的

91、數(shù)量要比男性的多。把這些評定等級和女會計師的高百分比給出來，那么可能會認為20世紀70年代關于男性會計師的刻板印象已經(jīng)不存在了。內(nèi)隱刻板印象結果暗示我們這絕對是不可能的。盡管，會計師刻板印象并不是像工程師那樣顯著，工程師和會計師存在最少的內(nèi)隱聯(lián)想測驗影響。正如早前的，影響越小，越少的不同刻板印象將被內(nèi)隱到。</p><p>　　相反的，內(nèi)隱聯(lián)想測驗最大的影響是保持和外顯刻板印象結果的一致:當出現(xiàn)工程師和小學老師是