Rogers and RIC PORON(r) Comparisons_Chinese
密度 in pcf
9.4 pcf 12.5 pcf 15 pcf 20 pcf 25 pcf 30 pcf 43.8 pcf
4790-92-25
• Ex2:
SR – S – 40 P –
厚度 (metric)
0.5t – 0.5 mm thick
0.5t
厚度 (inch)
0.021
4790-92-25021-04 P
軟
25
40 15 20 50 15 20 MHML-
40
24 32 24 32
LTL-
40
24 32
H-
24 32
30
60 15 20 25
48
48
HH-
48
FHMX-
48
48HF
硬
PORON 対照表
Rogers Code 4790-92 15 20 25 SR-S RIC SR-S Grades 15P 24P 32P 40P
SS-24P/32P：超软等级，各種高.低温度對應性更強
Very Popular for Handheld Electronics Products !!
PORON 對照表
Rogers Code 4701 30 15 20 LO/FR Grade MS24 32 RIC Original Grade LE20
4790-92
• Ex2:
SR – S –
密度 (metric)
15 – 150 kg/m3 20 – 200 kg/m3 24 – 240 kg/m3 32 – 320 kg/m3 40 – 400 kg/m3 48 – 480 kg/m3 70 – 700 kg/m3
40 P – 0.5t
P: 附 PET 基材
• LO/FR （阻燃）規格:
MS(30), ML(40), MH(50)
• 原配方: HH , FH , MO, MX, ASRS, SRU, SS, SSA, LF/HF Coating
RIC PORON – 機脚系列
等級 :
(M)H-32 適合用以一般緩沖材用途 (M)H-48(C) 適合用以標準脚塾 HH- 48(C) FH- 48(C) MO-48(C) 能負荷重的機械不変形 能耐更高的負荷 和 HH48C 的硬度相等、及取得 UL94 HBF
50 (RIC Original) 60
(RIC Original) (RIC Original)
• Ex1:
ML –
密度 (metric)
15 – 150 kg/m3 20 – 200 kg/m3 24 – 240 kg/m3 32 – 320 kg/m3 40 – 400 kg/m3 48 – 480 kg/m3 70 – 700 kg/m3
配方
密度 厚度
4701
工業用
40
20
059
04
標準顔色
• Ex2:
SR – S – 40 P – 0C Original) (RIC Original) (RIC Original)
SR-S: 慢回弾 ASRS: 防静電 SRS SR-U: SR UL94HBF規格 SS: 超軟
RIC 産品 産品介紹
• 例: ML-32-1.5t SR-S-40P-0.5t
• Ex1:
ML – 32 – 1.5t
HPF 產品對應
30 40
配方
LE: Low Modulus with Elongation MS: Soft 軟 ML – Low Modulus (M = Mega material) MH – High Modulus HH – Harder than H-formulation FH – Far Hard MX – EXtra Hard
RIC 的介紹
誰是 RIC?
50% 50%
Who is RIC?
•1984 成立 •工場位置：名古屋 (#1), 三重(#2) – 日本, 及 蘇州(#3) – 中国 (RIS Rogers Inoac Suzhou Corp) •員工：80人 (其中. 32名 臨時員工) •生産線：
• 取得認証: ISO-9001 & ISO-14001
Popular for Electronics & Handsets Products !!
RIC PORON – PET 基材系列
等级：
SR-S(92) – 15P/24P/32P/40P/48P/70P：慢回弹，柔软 ASRS – 24PA/32PA/40PA/48PA：SR-S 具防静电功能 SR-U-40P：SR-S 符合 UL94 HBF
• Ex2:
P: PET 基材
SR – S – 40 P – 0.5t
配方
密度 厚度
4790
工業用
92
25
021
04
標準顔色
RIC PORON – 各種規格
軟
SS<SRS<LE < L(U) < H< HH (MO) < FH<MX 有各種規格供選択
硬
• 標準規格:
L(01), H(05), U, LE/LT
MX- 48(HF) 適合以脚塾、最硬型、表面特別高摩擦処理
RIC PORON – LO/FR 系列
等級 :
MS(30) - 24/32/40 ML(40) - 24/32 MH(50) - 24/32/48
• 低揮発気体 • 耐燃型、不含鹵素、無代奥辛材料 (UL94 HBF)等級。
•厚度 : 0.3 ~ 10.0 mm
32 – 1.5t
密度 in pcf
9.4 pcf 12.5 pcf 15 pcf 20 pcf 25 pcf 30 pcf 43.8 pcf
• Ex1:
ML – 32 –
1.5t
厚度 (inch)
0.059
厚度 (metric)
1.5t – 1.5 mm thick
• Ex1:
ML – 32 – 1.5t
厚度 :
最低従 0.2mm
~ 3.0 mm
30
-
48P
70P
• 提供不同種類產品以增加你們的銷售量 • 羅傑斯亞洲區所有辦事處可提供所有 RIC 產品 (包括報價及交貨期) • 沒有限制你提供模切或銷售RIC材料給任何客戶 • 相配產品包括 : 腳垫, 更薄 (SRS) 空間填充材料 • 謝謝!
经济理论中的福利国家
经济理论中的福利国家Ξ郑秉文本文试图用经济学的基本原理来解释建立社会保障的理论依据,并以此来诠释西方福利国家存在的理性缘由,以期在主流经济学和社会保障及福利国家之间建立一个对话渠道。
在这个基础之上,试图运用经济学的理论框架来论证社会保障的建立和福利国家的存在不仅仅是由于“社会正义”的需要,更重要的,它还是由于“经济效率”的原因。
本文首先对战后西方世界正式宣布确立福利国家以来半个世纪中西方经济学研究社会保障和福利国家的学说史做一个概要性的回顾,对每个阶段的研究特点和热点问题予以分析讨论;然后着重对20世纪80年代至今西方经济学研究福利国家的成果和现状设立单独专题进行评析,即分别从宏观经济学、信息经济学、公共物品和公共选择理论等几个方面就社会保障和福利国家的效率功能进行私人保险市场与社会保障制度之间的效率比较,以期对失业、医疗健康、教育和养老等领域的社会保障和社会福利的存在根据给出结论。
关键词 经济学 社会保障 福利国家作者郑秉文,1955年生,中国社会科学院欧洲研究所教授(北京 100732)。
一、经济学研究福利国家的历史回顾虽然福利国家发端于英国、普及于欧洲,并几乎扩散于整个西方世界,但是,随着战后“大英帝国”的衰落和法、德等欧洲大陆国家的百废待兴,世界的经济中心早已转移到美国,于是,在1936年凯恩斯发表《就业、利息和货币通论》以后,作为一门解释社会经济现象的科学的经济学,其研究的中心也随之从英国移至美国。
于是,美国开始逐渐成为研究福利国家理论最活跃的发源地,成为以获得诺贝尔经济学奖为标志的世界级经济学大师辈出的摇篮,成为对社会保障和福利国家理论研究贡献最显著的地方。
战后以来,经济学或经济学家对福利国家研究的历史大致可以分为三个时期,即第一个阶段是20世纪60年代中期之前,第二个阶段是60年代中期至80年代初期,80年代初至今为第三个阶段。
20世纪60年代中期之前可以称为第一个阶段。
在这个时期,除英、法等一些老牌资本主义国家以外,社会保障在其它国家刚刚起步不久,福利制度发展态势逼人,运行状况良好,没有出现任何经济问题和财政障碍。
泡棉材料知识
• 硅弹性体是一种高交联的硅氧烷（醇）聚合物，化学名 称叫做Dimethicone Crosspolymer（聚二甲基硅氧 烷交联聚合物），属于硅橡胶的一种，即 Silicone Elastomers（硅胶弹性体），简称弹性体。
• 打个不恰当的比喻，就是一碗面条，如果煮的太久，面 条之间就会粘在一起（交联了），就会形成凝胶弹性体， 如果继续煮，水发少了烧糊了就变成固体的面块了（树 脂）
70年代末80年代初，聚氨酯合成材料的品种已经向系列化发展。并且开 发出反映注射成型的聚氨酯(RIM)
PORON的基本特性
• 弹性体 • 热固性 • 中等至高密度
– 190 – 480 kg/m3
• 片状涂覆
– 0.25 mm – 12.7 mm – 较薄材料需要PET基材
Silicones 硅泡棉
PORON 聚胺酯
聚氨酯发展简介
1937年德国化学家Otto Bayer发现了异氰酸酯与活泼氢化合物的聚合反 应，从而建立了聚氨酯化学的基础。
40年代德国人制取了聚氨酯粘合剂，50年代德国人亚当合成出聚氨酯混 炼胶(MPUR)和聚氨酯浇注橡胶(CPUR)，商品名Vulkallan。
60年代以来聚氨酯聚氨酯浇注橡胶、热塑胶、泡沫塑料、涂料、粘合剂 等相继形成了工业化生产。
性质：应力松弛
10 psi
5 psi
时间
初始条件： 22ºC 及 50% 相對濕度
在夹具中压缩 聚合物
测量到达 50% 初始负荷的时间
重复于：
重复于： 70ºC
40ºC、55ºC、70ºC、85ºC、100ºC、125ºC、150ºC 100% 相對濕度 及 40ºC、55ºC、
The Pros and Cons of Globalization
The Pros and Cons of Globalization Globalization has been a hotly debated topic for decades, with proponents and opponents passionately arguing their respective positions. The concept of globalization refers to the interconnectedness and interdependence of countriesand their economies, facilitated by the rapid advancements in technology, communication, and transportation. While globalization has undeniably broughtabout numerous benefits, it has also sparked intense criticism and raised concerns about its potential drawbacks. In this essay, we will explore the pros and cons of globalization from various perspectives. One of the most significant advantagesof globalization is the unprecedented economic growth and development it has facilitated. By opening up markets and encouraging free trade, globalization has allowed countries to specialize in the production of goods and services in which they have a comparative advantage. This has led to increased efficiency, lower prices for consumers, and higher profits for businesses. Moreover, globalization has enabled the flow of foreign direct investment (FDI) and the transfer of technology, which has contributed to the economic development of many developing countries. In addition to economic benefits, globalization has also played a pivotal role in promoting cultural exchange and understanding. The interconnectedness of the world has allowed for the dissemination of ideas, values, and traditions across borders. This has led to a more diverse and interconnected global society, where individuals have the opportunity to learn from andappreciate different cultures. Furthermore, globalization has facilitated the spread of information and knowledge, leading to advancements in education, science, and technology on a global scale. On the other hand, globalization has been met with staunch criticism and opposition, particularly in relation to its impact on labor and employment. Critics argue that globalization has led to the outsourcing of jobs to countries with lower labor costs, resulting in job displacement and wage stagnation in developed countries. Moreover, the race to the bottom phenomenon, where multinational corporations seek the lowest production costs, has led to exploitation of workers in developing countries and the violation of labor rights. Another contentious issue surrounding globalization is its environmental impact. The increased interconnectedness of economies has led to a surge in globalproduction and consumption, resulting in heightened environmental degradation and the depletion of natural resources. The reliance on fossil fuels fortransportation and the production of goods has contributed to climate change and pollution, posing significant threats to the planet and future generations. Furthermore, globalization has been criticized for exacerbating income inequality within and between countries. While some regions and social groups have reaped the benefits of globalization, others have been left behind, facing economic marginalization and social exclusion. This has given rise to social unrest and political instability in various parts of the world, as marginalized communities feel disenfranchised and neglected by the forces of globalization. In conclusion, the debate surrounding the pros and cons of globalization is complex and multifaceted. While globalization has undoubtedly fostered economic growth, cultural exchange, and technological advancements, it has also raised significant concerns regarding labor rights, environmental sustainability, and income inequality. As the world continues to grapple with the implications of globalization, it is crucial to strike a balance between reaping its benefits and mitigating its adverse effects. Only through thoughtful and collaborative efforts can we harness the potential of globalization while addressing its challenges in a sustainable and equitable manner.。
PORON与泡棉的特性解读
PORON 常用型號一覽表
.20/.24/.32/.40/.48/.70 g/cm3 密度
• • • • • • • • • • • • (M) S Soft 柔軟 L Low modulus 中性 H Hard 硬 HH Hard-Hard 硬度雙倍 FH Far Hard 超硬度 X infinity 無限硬度 SR-S Slow Recovery and Soft 柔軟慢速回彈型 LE Low modulus with Elongation 柔軟彈力型 LT Low modulus and Thin 柔軟輕薄型 U/MO UL 認證(現由LO/FR--M系列替代) C/HF C 型號與現有 High Friction 高摩擦系數型 P PET (Mylar) 含塑膠薄膜
Special Coating made on the material surface
1) 高 抓 地 性
2) 零 污 染
® PORON
Coating Technology Development
<<流 離 物 質 測 試>>
1kg/cm2 Loaded
PORON Adherend
接觸表面
・ ・ ・ ・ ABS (塑膠) Acrylic (壓克力) Formica (麗光板-合成樹酯) Steel (鋼鐵)
UL-94 HBF 標準
現已經成為 OA (辦公室自動化設備) 與 PC/NB (電腦) 工業必備條件
輕, 薄, 短, 小
先進 精密儀器, 通訊器材, 設計趨勢
跟進 21 世紀市場需求 !!!
什麼是 PORON SR ?
SR PORON = Slow Rebound
慢速回彈
模切材料基础培训(新)(1)
应用图解
应用领域
原材料特性及使用范围
2 .胶粘系列
包括3M，NITTO ，SONY，TESA，DIC,寺冈,罗曼,四维,力王等品牌的 双面胶及各式单面胶等 单/双面胶带的结构:
胶带结构分类
无基材双面胶
亚克力胶膜—亚克力胶水成膜，厚度0.05~0.15mm
棉纸双面胶—以棉纸或无纺布为基材，两面涂布胶水而 成，厚度0.10~0.20mm
@ @ Õ Å è @ Ò ¥ À Û S Ú H
@ @À Û S Ú
@ @ No.501K
@ No.501M
@ ] ü º ê @ Â â è H
@ @ z v S Ú @@ Y õ ¼ H
CR 泡棉 (氯丁橡胶) 橡胶泡棉 NR 泡棉 (丁腈橡胶) SBR 泡棉 (丁苯橡胶) EPDM 泡棉 (三元乙丙橡胶)
按发泡结构分类
质地柔软，吸水率高，压缩率高，适用 于吸音密封应用，如耳机的压缩海棉， 空调密封条等
开孔泡棉
闭孔泡棉
独立发泡，压缩残留率低，吸水率低， 隔热和缓冲性能良好，防尘好，适用于 电子产品密封防尘，防水减震等
PE 、PP离形膜等 ）
原材料特性及使用范围
1. 背光模组系列 包含黑白双面胶，增光膜，扩散膜，反射膜，双面胶等,品牌有 YUPO,TORAY,KIMOTO, TEIJIN(帝人),SKC,KEIWA(惠和),3M等等.
黑白双面胶: 用于遮光,固定作用,除了黑白胶,还有黑黑胶,黑银胶等 增光膜: 使用在光源背后,增加光感,提升整体亮度 扩散片: 扩散膜的作用是将线光源(CCFL)和点光源(LED)通过光的折射、反射和散射转化 为一均匀的面光源，对于背光源组的结构，可将2、3张扩散膜重叠使用，从而达到更高的 正面亮度效果 反射膜: 反射膜的作用是将导光板背面或光源背面透射出来的光反射回组件中，将光源发 出来的光的损耗限制在最小程度，从而提高光的利用率，增加正面亮度。
Empirical processes of dependent random variables
2
Preliminaries
n i=1
from R to R. The centered G -indexed empirical process is given by (P n − P )g = 1 n
n
the marginal and empirical distribution functions. Let G be a class of measurabrocesses that have been discussed include linear processes and Gaussian processes; see Dehling and Taqqu (1989) and Cs¨ org˝ o and Mielniczuk (1996) for long and short-range dependent subordinated Gaussian processes and Ho and Hsing (1996) and Wu (2003a) for long-range dependent linear processes. A collection of recent results is presented in Dehling, Mikosch and Sorensen (2002). In that collection Dedecker and Louhichi (2002) made an important generalization of Ossiander’s (1987) result. Here we investigate the empirical central limit problem for dependent random variables from another angle that avoids strong mixing conditions. In particular, we apply a martingale method and establish a weak convergence theory for stationary, causal processes. Our results are comparable with the theory for independent random variables in that the imposed moment conditions are optimal or almost optimal. We show that, if the process is short-range dependent in a certain sense, then the limiting behavior is similar to that of iid random variables in that the limiting distribution is a Gaussian process and the norming √ sequence is n. For long-range dependent linear processes, one needs to apply asymptotic √ expansions to obtain n-norming limit theorems (Section 6.2.2). The paper is structured as follows. In Section 2 we introduce some mathematical preliminaries necessary for the weak convergence theory and illustrate the essence of our approach. Two types of empirical central limit theorems are established. Empirical processes indexed by indicators of left half lines, absolutely continuous functions, and piecewise differentiable functions are discussed in Sections 3, 4 and 5 respectively. Applications to linear processes and iterated random functions are made in Section 6. Section 7 presents some integral and maximal inequalities that may be of independent interest. Some proofs are given in Sections 8 and 9.
罗杰斯整机框贴泡棉选型资料(LCD Gasket Selection Guide-E
PORON ® is registered by Rogers and RICCompression force(N/mm²), Deflection↘Original thicknessFinal thickness －No adhesive (mm)PORON ®Grade(mm)0.140.150.180.200.250.300.400.500.600.700.800.90 1.0015-06021 P 0.530.069,74%0.048,72%0.021,66%0.014,62%15-06030 P 0.750.059,73%0.024,67%92-09020 P 0.500.069,64%0.042,60%0.017,50%0.010,40%92-09030 P 0.750.063,67%0.032,60%92-09039 P 1.000.070,70%0.027,60%92-12020 P 0.500.070,60%0.029,50%0.017,40%92-12030 P 0.750.054,60%0.022,47%92-12039 P 1.000.040,60%0.021,50%92-12049 P 1.250.060,68%0.028,60%0.016,52%92-12059 P 1.500.051,67%0.032,60%0.022,53%0.018,47%92-15039 P 1.000.042,60%0.019,50%0.012,40%92-15120 P 3.050.075,74%0.051,71%0.037,67%92-25021 P 0.530.152,43%92-25031 P 0.790.157,49%0.091,36%92-25041 P 1.040.082,33%ShockSeal™ Foams-Extreme impact protection79-09021 P 0.530.060,62%0.028,53%0.017,43%79-09030 P 0.750.081,67%0.043,60%0.020,47%79-09039 P 1.000.082,70%0.034,60%0.020,50%79-12021 P 0.530.137,62%0.055,53%0.031,43%0.017,25%79-12030 P 0.750.087,60%0.038,47%0.025,33%79-12039 P1.000.076,60%0.043,50%0.031,40%0.025,30%ThinStik™ Self-adhesive MaterialsCompression force (N/mm²), Deflection↘Original thicknessPORON® Series↓(mm)0.100.120.150.180.200.250.3015TS1-060210.530.160,81%0.082,77%0.037,72%0.020,66%15TS1-060300.750.169,80.3%0.069,76%0.045,73.7%0.019,66.7%0.010,60.5%92TS1-090200.500.360,80%0.167,76%0.066,71%0.031,65%0.021,60%92TS1-120200.500.068,64%0.046,60%0.021,50%79TS1-090210.530.211,77%0.092,72%0.051,66%0.035,62%* The final thickness should include the tolerance Nomenclature:15 TS1 06 021 04 P ThinStikThickness 012-500mils PETDensity 6-30pcf Color 04-Black ，90-GrayFomulation 15-Soft Seal,92-Extra Soft (Slow Rebound ）Instruction ：0.069,74%Compression force is 0.069N/mm²,deflection is 74%4.From the column under the final thickness 0.25mm, 15-06030P,92-09020P,92-09030P and 92-12020P are available.3.T1-T2=0.25mm It is the real filling thickness for foam.Higher forceMiddle forceLower forcePORON ®Urethane Foam － LCD Gasket selection guide（Compression Force< 10psi or 0.069N/mm²）2.Make sure the adhesive thickness ，ex. T2=0.05mm.Final thickness －with adhesive (mm)The information contained in this data sheet is intended to assist you in designing with Rogers’ High Performance Foam Materi als. It is not intended to and does not create anywarranties, express or implied, including any warranty of merchantability or fitness for a particular purpose or that the results shown on the data sheet will be achieved by a user for a particular purpose. The user should determine the suitability of Rogers’ High Performance Foam Materials for each applicati on.The Rogers logo, The world runs better with Rogers., PORON, ThinStik, and ShockSeal are licensed trademarks of Rogers Corporation. © 2010 Rogers Corporation, All rights reserved. Printed in U.S.A.,。
formative assessment and the design of instructional systems
120 linkages are then identified. These in turn are shown to have implications for the design of instructional systems which are intended to develop the ability of students to exercise executive control over their own productive activities, and eventually to become independent and fully self-monitoring.
Introduction
This article is about the nature and function of formative assessment in the development of expertise. It is relevant to a wide variety of instructional systems in which student outcomes are appraised qualitatively using multiple criteria. The focus is on judgments about the quality of studentts, how they are made, how they may be refined, and how they may be put to use in bringing about improvement. The article is prompted by two overlapping concerns. The first is with the lack of a general theory of feedback and formative assessment in complex learning settings. The second concern follows from the common but puzzling observation that even when teachers provide students with valid and reliable judgments about the quality of their work, improvement does not necessarily follow. Students often show little or no growth or development despite regular, accurate feedback. The concern itself is with whether some learners fail to acquire expertise because of specific deficiencies in the instructional system associated with formative assessment. The discussion begins with definitions of feedback, formative assessment and qualitative judgments. This is followed by an analysis of certain patterns in teacher-student assessment interactions. A number of causal and conditional
journal of personality and social psychology
Men and Women Are From Earth: Examining the Latent Structure of GenderBobbi J.Carothers Washington University in St.LouisHarry T.Reis University of RochesterTaxometric methods enable determination of whether the latent structure of a construct is dimensional or taxonic(nonarbitrary categories).Although sex as a biological category is taxonic,psychological gender differences have not been examined in this way.The taxometric methods of mean above minus below a cut,maximum eigenvalue,and latent mode were used to investigate whether gender is taxonic or dimensional.Behavioral measures of stereotyped hobbies and physiological characteristics(physical strength,anthropometric measurements)were examined for validation purposes,and were taxonic by sex. Psychological indicators included sexuality and mating(sexual attitudes and behaviors,mate selectivity, sociosexual orientation),interpersonal orientation(empathy,relational-interdependent self-construal), gender-related dispositions(masculinity,femininity,care orientation,unmitigated communion,fear of success,science inclination,Big Five personality),and intimacy(intimacy prototypes and stages,social provisions,intimacy with best friend).Constructs were with few exceptions dimensional,speaking to Spence’s(1993)gender identity theory.Average differences between men and women are not under dispute,but the dimensionality of gender indicates that these differences are inappropriate for diagnosing gender-typical psychological variables on the basis of sex.Keywords:gender,taxometric,latent structure,personality,sex differencesSupplemental materials:/10.1037/a0030437.suppIf you ain’t wrong,you’re right;If it ain’t day,it’s night...If it ain’t dry,it’s wet...Gotta be this or that.—Sunny Skylar Sex is the most pervasive method of categorizing people.We are more likely to categorize people based on gender than race(Stan-gor,Lynch,Duan,&Glass,1992).People use gender to sort individuals into categorical“natural kinds”more than they use20 other kinds of social categories(Haslam,Rothschild,&Ernst, 2000).Parents of newborns are usually first asked the sex of their child,even before questions about the child’s and mother’s health (Intons-Peterson&Reddel,1984).Because nearly all animal spe-cies come in only two forms,male and female,sex is an easy target for categorization.According to gender identity theory(Spence, 1984,1993),from infancy onward,whether a person is male or female pervades almost all human activities and experiences.Chil-dren use information about sex to aid in normal cognitive and social functioning(Kohlberg,1966;Martin&Halverson,1981). Infants as young as3months process images of male and female faces differently(Ramsey-Rennels&Langlois,2006).People au-tomatically identify an unfamiliar person’s sex without conscious effort(Brewer,1988),using the distinctiveness of male and female body shapes and gaits,and can determine a person’s sex seeing only how they walk in as little as2.7s(Barclay,Cutting,& Kozlowski,1978;Johnson&Tassinary,2005).Given the obvious evolutionary significance of a person’s sex,it may not be surprising that sex is used pervasively for socialBobbi J.Carothers,Center for Public Health Systems Science,Wash-ington University in St.Louis;Harry T.Reis,Department of Clinical and Social Sciences in Psychology,University of Rochester.This article is based on the doctoral dissertation of the first author while at University of Rochester.Portions of this research were pre-sented at meetings of the Society for Personality and Social Psychology and the International Association for Relationships Research.Data for this research were generously donated by Arthur Aron,Jennifer Aubé, Mark Barnett,David Buss,Mark Davis,William Graziano,Stephen Quackenbush,Harry Reis,Todd Shackelford,Brad Sheese,and Jeffry Simpson.We thank Chantal Levesque-Bristol and Bradley Bodenhamer for their valuable time in collecting the Midwestern Sample data set and John Ruscio for consultation on taxometric methods.Science inclina-tion data were made available by the Organisation for Economic Co-operation and Development.Anthropometric and National Health and Social Life Survey(NHSLS)data were made available by the Inter-University Consortium for Political and Social Research.Data for the National Health and Nutrition Examination Survey III were originally collected and prepared by the Centers for Disease Control and Preven-tion.Data for the NHSLS were originally collected by the University of Chicago and the National Opinion Research Center.Neither the collec-tors of the original data nor the consortium bears any responsibility for the analyses or interpretations presented here.Correspondence concerning this article should be addressed to Bobbi J. Carothers,Center for Public Health Systems Science,George Warren Brown School of Social Work,Washington University in St.Louis,Cam-pus Box1009,St.Louis,MO63112.E-mail:bcarothers@Journal of Personality and Social Psychology©2012American Psychological Association0022-3514/12/$12.00DOI:10.1037/a00304371categorization.In this light,Hyde(2005)posed a key question: Are men and women generally the same or essentially different?In lay conceptions,men and women tend to be seen as categorically different.When asked if men and women were“basically differ-ent”in1989,58%of a sample agreed(40%said they were similar, and2%had no opinion;Gallup,1991).Moreover,lay beliefs about the characteristics of one sex tend to be negatively correlated with beliefs about the characteristics of the other sex(e.g.,Foushee, Helmreich,&Spence,1979;Kahn&Yoder,1989).Because we categorize objects in order to simplify complex information,it has been suggested that people categorize men and women on the basis of sex to simplify a complex social world(Fiske,2010;Taylor, Fiske,Etcoff,&Ruderman,1978).In other words,if men and women differ,grouping people into sex categories makes sense as a simple and fast judgment rather than dealing with each individual as a unique case.That men and women do in fact differ in many important behaviors,at least some of which have meaningful effect sizes,reinforces this tendency(Eagly,1995;Hyde,2005).From these and other findings,it has been argued that sex may be the strongest example of essentialism in lay social cognition: the belief that categories possess distinctive“deep,hidden,and unchanging properties that make their members what they are”(Prentice&Miller,2007,p.202;see also Haslam et al.,2000). Furthermore,lay essentialist beliefs about sex differences are often grounded in genetic explanations(e.g.,Cole,Jayaratne,Cecchi, Feldbaum,&Petty,2007),endowing them with a sense of natu-ralness,presumed importance,and acceptability(Haslam et al., 2000;Prentice&Miller,2007;Wood&Eagly,2012).It may be no surprise,then,that popular models putting men and women into fundamentally distinct categories,such as the argument that men and women come from different planets(Gray,1992),often strike a resonant chord with lay audiences.Interestingly,despite many thousands of studies on sex differ-ences—in2011alone,PsycINFO reported3,370articles under the keyword human sex differences—psychological science has not explicitly demonstrated whether human sex differences in psycho-logical characteristics reflect categorical differences between men and women or whether they are a matter of degree.In all likeli-hood,this is because research has not directly considered the question.The present research adopted an empirical perspective for determining whether gender differences are best characterized as qualitative or as a matter of degree.As we explain,the preva-lence and magnitude of gender differences as they have been studied so far are uninformative with regard to this distinction, because differences per se do not tell us whether a taxon exists—that is,a categorical variable in which members form nonarbitrary (meaningful and naturally occurring)distinctive classes(Meehl, 1992;Meehl&Golden,1982;Ruscio&Ruscio,2008;Waller& Meehl,1998).We used taxometric methods to determine whether gender differences are taxonic(i.e.,representing the existence of distinct categories)or dimensional(i.e.,reflecting differences of degree).Existing Perspectives on the Taxon–DimensionDistinctionThe prevailing view among scholars working in this area is that most gender differences are dimensional.Arguably the clearest examples of this belief derive from the results of meta-analyses,which are often conducted to establish the extent to which men and women differ on a given variable(e.g.,Hyde,Fennema,&Lamon, 1990;Hyde&Linn,1988;Oliver&Hyde,1993).For example, Hyde(2005,2007)reviewed46meta-analyses of sex differences spanning diverse cognitive,social,and personality domains.The observed magnitude and pattern of these results led Hyde to conclude that men and women are substantially more similar than they are different,a conclusion that implies(but does not demon-strate)dimensionality.Another dimensionalist position is that gen-der differences reflect underlying continuous attributes,such as personality(e.g.,communion and agency;Spence&Helmreich, 1978),temperament(Brody,2000),and hormonal differences (Thornhill&Gangestad,2008).Researchers who emphasize the impact of culturally derived experiences similarly seem to adopt a dimensionalist orientation,by describing how specific experiences can shape the degree to which an individual develops specific traits or competencies(e.g.,Eagly,1987;Halpern,2012;Wood&Ea-gly,2002,2012).The dimensionalist position is also popular in social neuroscience,in which arguments about the plasticity and “deep intricacy”(Eliot,2011,p.897)of the human brain are cited to explain why reliable sex differences in brain structure and function may have relatively little behavioral impact(e.g.,Eliot, 2009,2011;Halpern,2012).Nevertheless,perhaps because the taxon–dimension distinction has not received explicit empirical attention,some researchers appear to favor a more categorical interpretation of gender differ-ences.For example,many evolutionarily oriented researchers view men’s and women’s social behavior as categorically distinct,re-flecting the different adaptive tasks implied by biological differ-ences between men and women(e.g.,Geary,2010).These distinc-tions are reflected in sexually dimorphic brain structures,such as men’s larger and more lateralized brains(see Ellis et al.,2008,for recent meta-analyses)or neural regions that show differential elaboration in response to activation by sex hormones(see Geary, 2010,for a review).Likewise,researchers who theorize about sexuality and mating-related behaviors from an evolutionary per-spective typically favor categorical accounts in their conceptual-ization of gender differences(e.g.,Buss,1995;Buss&Schmitt, 1993;Schmitt,2002).More generally,Zahn-Waxler and Polanichka(2004)reviewed several theoretical models positing qualitative differences in the expression and etiology of antisocial behavior in boys and girls. Another example is the“tend-and-befriend model,”which pro-poses that men’s and women’s responses to stress are fundamen-tally different(Taylor et al.,2000),linking these behaviors to the influence of oxytocin.Indeed,the fact that researchers sometimes analyze data from men and women separately(Kashy&Kenny, 2000),and the insistence by some editors and reviewers that researchers routinely analyze their data for gender differences(see Baumeister,1988;Eagly,1987;and McHugh,Koeske,&Frieze, 1986,for varied positions in this debate),implies belief in at least the possibility that men’s and women’s behavior may be categor-ically distinct.If,as mentioned above,most scholars are skeptical about the general idea that in terms of social behavior men and women represent natural kinds,why does categorical thinking persist?One reason,we propose,is that no existing research has examined explicitly the distinction between categorical and dimensional models.As described in the next section,documentation of gender2CAROTHERS AND REISdifferences in and of themselves is insufficient to conclusively establish that gender differences are dimensional,or whether they might be taxonic.The research described in this article was de-signed to provide such evidence.Understanding Dimensions and TaxaOne reason why the underlying nature of gender differences has been difficult to address is that although biological sex is clearly a categorical variable,the variables commonly of interest to re-searchers and laypersons alike tend to be dimensional(e.g.,mas-culinity,femininity,school achievement,depression,aggression), varying along a continuum.The statement that men are more aggressive than women,for example,implicitly assumes that there is one group of people who are high in aggression(men)and another group of people who are low in aggression(women).This assumption treats an observed mean difference between men and women as a special kind of category called a taxon.Examples of taxa include animal species(gophers vs.chipmunks),certain phys-ical illnesses(e.g.,one either has meningitis or not),and biological sex.To distinguish a taxon from a mere category,Waller and Meehl (1998)used the example of students receiving an A on an exam. Though A students are in a different category from B students, they are not a taxon because the dividing line between an A and a B is determined by the grader’s cutoff on a dimensional scale of performance.Knowing a person’s grade in one subject is relatively uninformative about other attributes.However,when individuals are members of a taxon,they are more likely possess traits that are characteristic of that taxon than nonmembers.For example,know-ing that Person A has two X chromosomes allows one to accu-rately conclude that Person A will develop breasts,ovulate,have little facial hair,and exhibit all other characteristics associated with being female.Like many taxa,sex has a genetic basis;with few exceptions,a person is either XX or XY.The indicators for sex(i.e.,genetic makeup,anatomy,physiology)are nearly infal-lible.As Meehl(1995)put it,“There are gophers,there are chipmunks,but there are no gophmunks”(p.268).When applied to sex,“between day and night there is dusk.But between male and female there is...essentially nothing”(Myers,2008,p.164).1 We sought to empirically determine whether standard gender differences are better conceived as taxonic or dimensional.Al-though men and women may differ on average in myriad ways, these differences may be dimensional,reflecting different amounts of a given attribute assessed along a single dimension,or qualita-tive,sorted into fundamentally distinct categories.Taxometric analysis is concerned not only with the magnitude of differences but also with the pattern and distribution of differences across multiple variables.As we will show,this difference has consider-able importance for understanding the fundamental nature of gen-der differences.What exactly is a taxon?In the simplest sense,a taxon means that a set of variables is essentially uncorrelated within groups due to a tendency toward restriction of range.However,because mean differences exist on each variable,correlations appear when the groups are combined in a single analysis.This is shown in Figure 1.For example,let the variables in Figure1be hair length and height;Group1is men and Group2is women.Within each group, there is no correlation.But when the two groups are combined in a single analysis,because the sex difference introduces additional variability for each variable,the correlation can be large(Ruscio, Haslam,&Ruscio,2006;Ruscio&Ruscio,2008).(This is a version of the well-known aggregation fallacy identified by Ep-stein,1986.)Thus,understanding the pattern of within-and between-group correlations across multiple variables measured simultaneously in a single sample(rather than group distributions on variables from isolated samples)is necessary to examine the latent structure of gender.This is the novel contribution of the present research.Why Distinguish Dimensions and Taxa?There are several reasons why it is important to determine the latent structure of gender(Ruscio&Ruscio,2008).First,whereas dimensional variations may arise from multiple additive influ-ences,categorical differences require mechanisms capable of cre-ating a dichotomous(on/off)structure.Second,knowledge about the underlying structure of a construct can inform classification, that is,whether individuals vary along dimensions or belong to distinct groups.Third,whereas categories imply that group com-parisons are most appropriate for statistical analysis,dimensional variables are more appropriately analyzed by continuous correla-tional methods.Taxometric methods have been used profitably in several areas of psychological research.For example,although the Diagnostic and Statistical Manual of Mental Disorders“is a categorical classification that divides mental disorders into types based on criteria sets with defining features”(American Psychiatric Asso-ciation,2000,p.xxxi),Widiger and Trull(2007)concluded that existing data better support a dimensional model of personality disorder.Other constructs sometimes thought to be categorical but found to be dimensional include attachment(Fraley&Spieker, 2003;Fraley&Waller,1998;Roisman,Fraley,&Belsky,2007) and depression(Ruscio&Ruscio,2000;Whisman&Pinto,1997). On the other hand,constructs found to be taxonic include schizo-phrenia(Meehl,1962,1990),schizotypy(Korfine&Lenzen-weger,1995;Lenzenweger&Korfine,1992),self-monitoring (Gangestad&Snyder,1985,1991),and Type A behavior(Strube, 1989).In each instance,researchers have used this information to refine and advance theoretical models.Haslam and Kim(2002) provide a more extensive review of taxometric research.Dimensions and Taxa as Applied to GenderAs noted earlier,gender research has made little or no use of taxometric methods so far.Clearly,many gender differences are present in personality and social behavior(for reviews,see Dindia &Canary,2006;Ellis et al.,2008;Gilligan,1982;Halpern,2012; Maccoby&Jacklin,1974).Hyde’s2005(see also Hyde,2007) review of46meta-analyses of gender differences revealed numer-ous consistent and reliable differences between men and women. Some of the personality and social variables producing larger differences included interruptions,smiling,self-disclosure,aggres-sion,sexuality,anxiety,assertiveness,agreeableness,body-1Though the existence of intersex individuals may call this into ques-tion,the rarity of their occurrence casts them as the exceptions that prove the rule.3MEN AND WOMEN ARE FROM EARTHesteem,and self-efficacy about computer use.Hyde’s review did not include many other variables of interest to personality and social psychologists,such as sex differences in mating and rela-tional strategies (Buss &Barnes,1986;Buss,Larsen,Westen,&Semmelroth,1992;Buss &Schmitt,1993;Buss &Shackelford,1997;Kenrick,Sadalla,Groth,&Trost,1990)or in responses to stress (Taylor et al.,2000).(Other specific study variables are described later in this article.)What is important to note here is that each of these gender differences reflects a reliable difference between men and women.However,the existence of a gender difference in and of itself,and regardless of its magnitude,does not provide direct empirical support for its dimensionality or taxonic-ity.Rather,as explained above,multiple indicators,along with their intercorrelations,must be examined together in the same sample.We sought to establish that gender differences are better repre-sented as dimensional than as taxonic constructs in a more defin-itive way than prior research has done.The implications of this distinction are important.Should gender be taxonic,contrary to prevailing beliefs,men and women could legitimately be said to be qualitatively different in relevant domains.Knowing only that a person was male,we could also infer that he would be relatively aggressive,good in math,poor in verbal skills,primarily interested in short-term mating,less agreeable,and so on,to the same general degree of accuracy as inferring biological characteristics relevant to sex—for example,large waist-to-hip ratio and deeper voice.This is because if gender were taxonic,the relevant behaviors would co-occur in all members of the class.In other words,most men would score similarly to each other on all these indicators,as would women,with little overlap between the two groups.On the other hand,if gender is dimensional,as scholars commonly as-sume,a person’s gender-appropriate behavior on one variable would not imply being high on other gender-related variables.Should our results support dimensionality,scholars would have a stronger empirical basis for their belief.The Present ResearchAs discussed above,the underlying structure of gender differ-ences is implicit in many theoretical accounts of social behavior.Our research was designed to provide explicit evidence about a wide variety of psychological traits in which men and women differ.These analyses evaluated the likelihood that existing gender differences are better represented as dimensional or taxonic.This work differs from more exploratory taxometric research because the sex of participants in the samples is already known.If a taxon appears,we can determine whether it is based on sex (in line with what Meehl,1992,referred to as a real data pseudoproblem).In other words,participants’actual sex is the “gold standard”of prediction.Because the Bayesian distributions obtained from taxo-metric analyses graphically represent the probability that each individual is a member of the taxon group,one can determine whether whatever taxa emerge from these analyses accurately sort men into one group and women into the other.2Our analyses were conducted on a series of domains commonly studied by researchers interested in differences between women and men.These are detailed below.MethodData Set RecruitmentWe selected data sets on the basis of three major criteria:that they included variables that have been important in the sex-2One somewhat related approach in prior research is Lippa’s work on gender diagnosticity (e.g.,Lippa,1991;Lippa &Connelly,1990).That research used Bayesian methods to try to find traits that would accurately categorize men and women.Although the idea of gender diagnosticity implies that gender is taxonic with respect to certain indicators,it does not establish whether gender is taxonic ordimensional.Figure 1.Within-group versus full-sample correlation patterns underlying taxometric analysis.Within-group correlations tend to be small,but can be high over the full sample.Adapted from Multivariate Taxometric Procedures:Distinguishing Types From Continua (p.13,Figure 3.1),by N.G.Waller and P.E.Meehl,1998,Thousand Oaks,CA:Sage.Copyright 1998by Sage Publications.4CAROTHERS AND REISdifferences-in-social-behavior literature,that they came as close as possible to meeting statistical criteria for taxometric analyses,and that they were available to us.We began by performing a literature search and contacted several researchers regarding data from articles that preliminarily appeared to meet the analysis requirements.In order to meet the requirements of taxometric analysis,data sets were considered eligible if there were at least300participants,the gender balance in the sample was no worse that60:40,and there were at least three contin-uous and theoretically linked measures showing significant gender differences.Researchers who had collected these data sets were asked to share them with us.Other researchers with known experience in gender difference research were contacted and asked whether they had any data fitting the above criteria that they would be willing to share.Altogether,31researchers were contacted:Nine provided data;15reported having no data sets that fit the requirements or that requested data were no longer available(some referred us to second authors);seven did not reply.Four data sets were available online.We also collected further data on several relational,interper-sonal,and behavioral variables in one additional sample to exam-ine the taxonicity of gender across these constructs,sometimes repeating earlier measurements with ambiguous results for repli-cation purposes.This sample of109men and167women3with a mean age of21.15years(SDϭ7.68)was drawn from an introductory-level psychology class at a large midwestern univer-sity and is periodically mentioned through the rest of the article as the Midwestern Sample.Where a data set provided multiple scales along with the raw items for each scale,several analyses could be performed.First, analyses were performed including all mean scores of scales showing a significant sex difference.Second,when individual items were made available,analyses on the individual scales themselves could be performed.Taxometric ProceduresThree common techniques for taxometric analyses are mean above minus below a cut(MAMBAC),maximum eigenvalue (MAXEIG),and latent mode(L-Mode;Waller&Meehl,1998). For MAXEIG and L-Mode,at least three variables measuring the desired construct are submitted for analysis(MAMBAC can work with as few as two variables),and the shape of the obtained function is used to judge whether the construct is taxonic or dimensional.Taxonic data produce peaked MAMBAC and MAXEIG curves,and bimodal L-Mode curves. Dimensional data yield flat or U-shaped MAMBAC curves,flat MAXEIG curves,and unimodal L-Mode curves.MAXEIG re-sults can also be used to assign a Bayesian probability that each individual belongs to the taxon group.Taxonic data produce U-shaped histograms with clumps at the tails of the x-axis.With taxonic constructs,some individuals have a very high proba-bility of being a member of the taxon group,and everyone else has a very low probability,with few people in the middle “undecided”ranges.Dimensional constructs produce less dis-cernible Bayesian distributions,meaning that many cases can-not be classified into one group or the other with certainty.In the event that a taxon is identified,it can be used to determine whether men and women were accurately sorted.Dimensional data can also produce U-shaped distributions(Ruscio et al., 2006),so a taxonic-looking Bayesian distribution must be in-terpreted within the context of the other methods.A dimensional-looking Bayesian distribution,however,makes a strong case for dimensionality.Interpreting distributions produced by these three methods can be subjective.In the ideal case the plots obtained fit either of the alternatives mentioned above,but in practice these graphs are sometimes ambiguous.We used three methods to control for this ambiguity.First,we drew conclusions only from results that replicated consistently with all three methods.Each proce-dure tests for taxonicity in a different way,and because inter-pretation is largely visual,most researchers recommend that multiple methods be used to confirm one another(Waller& Meehl,1998).Second,simulation methods(Ruscio et al.,2006) help determine the suitability of data for taxometric analyses. On the basis of properties of the actual data(n,number of indicators,correlations between indicators,skew),it is possible to simulate what the data would look like if they were taxonic or dimensional.Data are considered appropriate for taxometric analysis if the simulations are distinguishable.Taxonic and dimensional simulations that are too similar to be distinguished from each other indicate that the data are unsuitable for that method.Third,ambiguous results can be compared with their parison curve fit indices(CCFIs;Ruscio et al., 2006)provide an objective measure of whether the curve gen-erated by the actual data is closer to its taxonic(CCFI of1)or dimensional(CCFI of0)simulation.Judgments based on visual inspection should supersede CCFI results when they disagree (Ruscio et al.,2006).The CCFI for each analysis is reported in Table1.In Figures 2–4and A1–A22(see Appendix,included in the supplemental materials),MAMBAC results are the upper left quadrant, MAXEIG in the upper right,L-Mode in the lower left,and Bayes-ian probabilities in the lower right.For MAMBAC and MAXEIG, two graphs are presented,one superimposing the actual data over simulated taxonic data(left panel)and the other superimposing the identical data over simulated dimensional data(right panel). L-Mode is presented in one panel,with actual data drawn in the heavy solid line,simulated taxonic data drawn in the lighter solid line,and simulated dimensional data drawn in the dotted line. Comparison of the degree to which actual results match these simulations was used to assign a result to each analysis.Numerical output is also given in the form of base rate estimates(i.e.,the proportion of the sample assigned to the taxon)and is provided in the supplemental materials(see Supplemental Table1).These estimates are unreliable in dimensional data for all three methods, for if there are no true taxon or complement groups,the concept of “base rate”is moot,though consistent estimates may indicate taxonicity.3Seven men and five women were deleted for indicating nonheterosexu-ality to prevent the possibility of sexual orientation from introducing an additional taxon or contributing additional unaccounted-for variance.Re-sults including these individuals were similar to those reported here.We do admit to a heterosexual bias in our treatment of gender indicators,and although the extent to which sexual orientation can influence the results of gender taxonicity is intriguing,it is beyond the scope of this article.5MEN AND WOMEN ARE FROM EARTH。
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