在生涯決策時關鍵性的考慮因素包含了性向和興趣，因此學校常用性向測驗和興趣測驗進行生涯輔導輔導工作。本研究採用個人中心取向（person-centered）方法，透過潛在剖面分析（Latent Profile Analysis, LPA）和潛在類別分析（Latent Class Analysis, LCA），將焦點放在辨識國中階段學生性向組型、興趣組型和性向-興趣整合組型的潛在類別。研究一主要目的在分析國中階段學生在八種性向可以形成那些性向組型，並且比較不同性別在組型上是否有所差異。蒐集了18,950名國中三年級學生資料，測量工具為「電腦化適性職涯性向測驗」（宋曜廷，2011）。研究一結果主要有兩項，第一，透過潛在剖面分析可以辨識出24種的性向組型，可歸納為三大類，分別為正向組型、負向組型和未分化組型。約有七成五的學生為正向組型，此代表多數學生都具有一項以上適合發展的潛能。在負向組型和未分化組型中有約二成五的學生。第二，針對24種的性向組型進行性別上的檢定，可以發現男生在空間、邏輯推理、科學能力等組型及負向組型的比例高於女生；女生則是在語文、觀察、美感及未分化組型的比例高於男生。研究二主要在探討國中階段學生在Holland六種興趣類型可以形成那些興趣組型，並同時檢驗Holland（1997）環狀結構、Gati（1979, 1991）的興趣群組和Rounds和Tracey（1996）所提出的三分群模式是否能與本研究所辨識出的組型相呼應。此外，並比較不同性別的興趣組型是否有所差異。最後，比較Holland固定的三碼組型（three-letter code）和本研究所辨識出之組型，何者較為貼近Iachan（1984）所提出的辨識組型的方式。蒐集了32,221名國中三年級學生資料，測量工具為「電腦化情境式職涯興趣測驗」（Sung et al., 2015）。研究二結果主要有三項，透過潛在剖面分析可以辨識出72種的興趣組型，可歸納為三大類，分別為正向組型、負向組型和未分化組型。有高達五成的學生為未分化的組型。部分組型現與有相關理論及研究有一致性的發現。第二，針對各組在性別上的分配進行檢定，研究者發現，男生在實用型（R）和研究型（I）相關的組型上其比例高於女生；女生在藝術型（A）和社會型（S）的組型其比例顯著高於男生。第三，根據LPA所區分出的生涯興趣組型和Holland固定三碼進行比較，透過潛在剖面分析的組型辨識，在個人特徵的辨識度上可達九成的正確率。研究三所欲探討的問題，根據研究一和研究二之研究結果，在取得24組的性向組型和72組興趣組型，可以建構出那些整合組型呢?並進一步探討國中階段學生在15個高職之職群及高中，在性向高低和興趣高低的分布情形。本研究蒐集了研究一和研究二樣本中同時完成了兩個測驗之18,942位國三學生的資料。研究三結果主要有兩項，第一，為能更有效的去解釋及應用組型的特徵，本研究針對研究一和研究二所發現之組型加以分類，透過LCA的分析取得六項組型，其中以「性向為正向組型和興趣為未分化組型」這一個組別的人數最多，占整體樣本超過四成。第二，約有一成五的學生在群科的發展中，有性向和興趣的交集，代表可以選擇到同時具有性向和興趣的群科進行發展，另有八成五的學生在高中及高職群科其興趣與性向是沒有交集的。進一步的分析發現，在無交集的學生中，有近五成八的學生僅具有高性向或是高興趣，代表性向和興趣間產生鴻溝（discrepancy）的現象。此外，有將近一成五的學生在高中及所有群科其所需具備之能力偏低且並不感興趣，未有適合發展的方向，可視為待探索型。綜合三項研究結果，本研究將以國中階段學生之性向組型、興趣組型以及性向-興趣整合組型，提供理論和實務上之建議，期望給予教育學者及諮商人員輔導學生時有所參考。

In countries where streaming takes place in the early or middle stages of education, students have to decide at a young age how to direct their future careers. Since key factors in the making of career choices include aptitude and personal interest, school career counseling programs often use aptitude tests and interest tests. Adopting a person-centered approach, this study uses latent profile analysis（LPA）and latent class analysis（LCA）to identify the latent categories of aptitude profiles, interest profiles, and combined aptitude-interest profiles among students in junior high school. In study 1, this study mainly analyzes the aptitude profiles that can be formed from eight types of aptitude, and seeks to discover whether students of different genders also differ in their prevailing aptitude profiles. The responses of 18,590 jounior high school students from Grade 9 were collected using Sung（2012）Computerized Adaptive Career Aptitude Test（CACAT）. The CACAT consisted of eight sub-tests: Verbal, Numerical, Spatial, Logical Reasoning, Scientific Reasoning, Observation, Aesthetics, and Creativity. Study 1 yields two main results: First, the results show that the data best fit a 24-classes model, 24 aptitude profiles in three categories were identified: positive, negative, and undifferentiated. Positive profiles were divided into those with a single-letter code, double-letter code, three-letter code, and five-letter code or more. Approximately 75% of the students had positive profiles. Second, the proportions of females and males differed significantly among the profiles. The proportion of males was higher for spatial, logical reasoning, and scientific reasoning profiles and negative profiles than of female proportions. The proportion of female was higher for verbal, observation, and aesthetics profiles than of males proportions. In study 2, this study discusses what interest patterns can be formed from Holland’s six interest types. It also examines whether the profiles identified in this study correspond to Holland’s circular order（1997）, Gati’s hierarchical model（1979,1991）, and Rounds and Tracey’s three-class partition model（1996）, and whether students of different genders have different interest profiles. In addition, Holland’s three-letter code and the profiles identified in this study are compared in order to determine which is closer to Iachan’s index. The sample consisits of 32,221 jounior high school students from Grade 9 were collected using Sung et al.’s (2015) Situation-based Career Interest Assessment (SCIA). SCIA is based on the interest theory of Holland (1997). A latent profile analysis on six interest types revealed several career interest profiles. Results show that: First, the results show that the data best fit a 72-classes model, 72 interest profiles in three categories were identified: positive, negative, and undifferentiated. Positive profiles could be divided into single-letter, double-letter, three-letter, and four-letter codes, while negative profiles could be divided into double-letter, four-letter, and five-letter codes. This study identified three undifferentiated groups: “like all,” “moderate for all,” and “dislike all”. It is noteworthy that a large percentage (53.05%) of junior-high-school students fell into the undifferentiated group. Some profiles corresponded to Holland’s circular order, Gati’s hierarchical model, and Rounds and Tracey’s three-class partition model. Second, a higher proportion of male had R (Realistic) and I (Investigative) profiles than female, while a significantly higher proportion of female had A (Artistic) and S (Social) profiles than male. A significantly higher percentage of male had undifferentiated profiles than female. Third, a comparison between interest profiles identified by the LPA and Holland’s three-letter code revealed that interest profiles based on codes consisting of a fixed number of holland code cannot truly capture a person’s interest characteristics. By contrast, profiles identified through the LPA had a 90% accuracy rate in capturing an individual’s characteristics. This shows that the profiles identified in the present study can highlight students’ interest profiles while avoiding overproduction of categories, which leads to difficulty in presentation or interpretation. In study 3,the study seeks to answer is based on the results of the first and second parts; that is, after obtaining 24 aptitude profiles and 72 interest profiles, what combined profiles can be formed? This part of the research also looks at the distribution of junior high school students among 15 vocational clusters of vocational high schools and academically oriented high schools as well as the four quadrants of aptitude-interest combinations. Date were collected from 18,942 samples from the Study 1 and Study 2. In order to better interpret and use the characteristics of the profiles, this study combined the profiles identified in Study 1 and Study 2 and using the LCA .The results show that the data best fit a 6-classes model. Of the six profiles, the combination of positive aptitude profiles with undifferentiated interest profiles made up the biggest number, accounting for over 40% of the total samples. About 15% of the students saw intersections of aptitude and interests in specific clusters of subjects, indicating that they could choose to study these subject clusters. 85% of the students did not see any intersections of aptitude and interests for either academically oriented high schools or subject clusters of vocational high schools. Findings highlight the importance of career counseling practitioners’ attention to the individual differences in aptitude profiles, career interest profiles and and combined aptitude-interest profiles .On the basis of these three sets of results, this study provides theory-and practice-based recommendations about career profiles. Implications for career practices and future research are proposed.

毛國楠、盧雪梅（2003）。中學多元性向測驗。臺北市：心理。
王玉珍（2015）。優勢中心生涯諮商對國中學生幸福感與生涯發展之影響研究。教育心理學報，46（3），311-332。
王詩婷（2008）。多層次多因子潛在類別分析應用於空間能力之分類研究（碩士論文）。輔仁大學，新北市。
田秀蘭（2012）。十二年國民教育與青少年之生涯發展。教師天地，177，10-16。
吳佩璇、張正芬（2012）。亞斯柏格症學生在魏氏兒童智力量表─第四版（WISC-IV）的表現。特殊教育研究學刊，37（2），85-110。
吳武典（2010）。擺脫壓力，增進創意。北縣教育，70，16-18。
吳武典（2011）。形塑孩子優質學習環境與品格教養。廈門教育，6，34-37。
吳武典、簡茂發、洪冬桂、舒琮慧、鄒小蘭、張芝萱、吳道愉（2010）。高中學生生涯發展組型建構及其在升學與生涯輔導上的意義。教育科學研究期刊，55（2），29-72。
吳道愉、吳武典（2010）。高中學生多元智能組型探索研究。測驗學刊，57（2），269-292。
宋曜廷（2011）。電腦化適性性向測驗。民國108年12月24日，取自http://career.ntnu.edu.tw。
宋曜廷、田秀蘭、鄭育文（2012）。國中與高中職階段生涯測驗使用現況之分析研究。教育心理學報，43（4），875-898。
李佩隃（2011）。潛在類別分析與二階段群集分析分群效果之比較研究（碩士論文）。國立臺灣師範大學，臺北市。
李慧賢（2018）。高中輔導老師看學生選擇大學的考量因素-師大附中觀點。評鑑雙月刊，75，34-37。
兒福聯盟（2014）。台灣國中生未來志向調查研究報告。取自https://www.children.org.tw/news/advocacy_detail/1290
周玉秀（2016）。追求品質的奧地利職業教育。臺灣教育評論月刊，5（2），153-155。
林麗芳（2009）。高中職學生未來時間觀與課業學習動機調整策略關係之研究（碩士論文）。國立彰化師範大學，彰化縣。
邱皓政（2008）。潛在類別模式：原理與技術。臺北市：五南圖書公司。
金樹人（1991）。職業興趣與人格之關聯性研究。教育心理學報，24，91-115。
洪兆祥（2010）。混合分配的分群方法比較研究：二階段集群法、潛在類別模式及自組織映射圖的模擬與實證分析（碩士論文）。輔仁大學，新北市。
唐思涵（2016）。從十二年國教適性輔導看國中生涯輔導困境與因應之道。臺灣教育評論月刊，5（5），88-91。
徐昊杲、武曉霞、徐美鈴、羅珮瑜、虞邦敏、楊惠娟（2012）。心理測驗工具應用在國中學生選習技藝教育課程之研究。測驗學刊，56（1），219-245。
國家教育研究院（2013）。十二年國民基本教育綜合活動領域綱要內容之前導研究。計畫編號：NAER-102-06-A-1-02-08-1-17。新北市：國家教育研究院。
張雨霖（2015）。不同發展階段的創造性潛能與傾向潛在組型及創意自我效能中介效果（博士論文）。國立臺灣師範大學，臺北市。
張郁雯、林文瑛、王震武（2013）。科學表現的兩性差異縮小了嗎？─國際科學表現評量資料之探究。教育心理學報，44，459-476。
張德聰、田秀蘭、林蔚芳（2010）。國中階段生涯輔導工作實務現況分析與多元進路觀點生涯輔導策略發展之探討。教育部訓育委員會委託專題研究報告（編號：0980108600）。臺北市：教育部。
張潔婷、張敏強（2009）。青少年推理能力的潛在類別分析。第十二屆全國心理學學術大會論文摘要集。
張靖卿、吳武典、鄒小蘭、張芝萱、吳道愉（2016）。國中學生生涯發展組型之探討。教育心理學報，47(3)，417-448。
郭生玉（2004）。教育測驗與評量。臺北市：精華。
陳心怡、張正芬、楊宗仁（2004）。自閉症兒童的 WISC-III 智能組型研究。特殊教育研究學刊, 26, 127-151。
陳世雄（2012）。UCAN 職業興趣探索量表之潛在結構的探索－以國立中山大學學生為例。取自http://140.117.153.69/ctdr/files/2158_3459.pdf
陳怡君（2016）。淺談空間能力的性別差異與科學、科技、工程及數學類型的職業選擇。科學教育月刊，392，47-55。
陳柏熹（2010）。多元智慧電腦化適性測驗指導手冊。民國108年12月24日，取自http://140.122.69.221/rcfit/。
陳清溪（2013）。十二年國民基本教育政策之探討。教育資料與研究，109。53-77。
陳榮華、吳明雄、陳心怡（2011）。國中新編多元性向測驗指導手冊。臺北市：中國行為科學社。
黃素菲（2011）。生涯的評估與發展概念架構—大學生涯諮商工作藍圖。輔導季刊，47（2），62-73。
路君約、簡茂發、陳榮華（1999）。區分性向測驗（DAT-V）。臺北市：中國行為科學社。
趙子揚（2018）。中學生「不確定性考試壓力」模式之驗證（博士論文）。國立臺灣師範大學，臺北市。
鄭育文、陳柏熹、宋曜廷、陳信豪、蕭孟莛（2014）。電腦化適性職涯性向測驗編製研究。教育心理學報，46（2），271-288。
簡茂發（1988）。兒童及青少年的學業輔導。國教輔導，27（2），7-13。
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
Anastasi, A., & Urbina, S. (1997). Psychological Testing (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Armstrong, P. I., Fouad, N. A., Rounds, J., & Hubert, L. (2010). Quantifying and interpreting group differences in interest profiles. Journal of Career Assessment, 18(2), 115-132.
Armstrong, P., & Anthoney, S. (2009). Personality facets and RIASEC interests: An integrated model. Journal of Vocational Behavior, 75, 346-359.
Arulmani, G. (2014). Assessment of interest and aptitude: A methodologically integrated approach. In G. Arulmani, A. J. Bakshi, F. T. L. Leong, & A. G. Watts (Eds.), International and cultural psychology. Handbook of career development: International perspectives (pp. 609-629). New York, NY: Springer.
Asendorpf, J. B. (2015). Person-oriented approaches whithin multi-level perspective. Journal of Person-Oriented Research, 1(1-2), 48-55.
Bachter, J., Wenzig, K., & Vogler, M. (2004). SPSS TwoStep Cluster - A First Evaluation. Lehrstuhlfür Soziologie. Arbeits- und Diskussionspapier 2004-2. Nürnberg: FAU
Bartholomew, D. J., Steele, F., Moustaki, I., & Galbraith, J. I. (2002). The Analysis and Interpretation of Multivariate Data for Social Scientists. Boca Raton: Chapman & Hall.
Baumert, J., Cortina, K. S., & Leschinsky, A. (2003). Grundlegende Entwicklungen und Strukturprobleme im all gemeinbildenden Schulwesen. In: K. S. Cortina, J. Baumert, A. Leschinsky, K. U. Mayer, & L. Trommer (Eds.), Das Bildungswesen in der Bundesrepublik Deutschland. Strukturen und Entwicklungen im Überblick (pp. 52-147). Reinbek bei Hamburg: Rowohlt.
being and becoming. Journal of Employment
Betz, N. E. (1999). Getting clients to act on their interests: Self-efficacy as a mediator of the implementation of vocational interests. In M. L. Savickas & A. R. Spokane (Eds.), Vocational interests: Meaning, measurement, and counseling use (pp. 327-344). Palo Alto, CA: Davies-Black.
Brown, D. (2007). Career Information, Career Counseling, and Career Development: Testing and Assessment in Career Development. Boston: Allyn & Bacon.
Buboltz, W. C. Jr., & Woller, K. M. P. (1998). Various indices of differentiation and psychological maladjustment. Counselling Psychology Quarterly, 11, 79-86.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
Career Planning and Adult Development Journal,23,
Carragher, N., Adamson, G., Bunting, B., & McCann, S. (2009). Subtypes of depression in a nationally representative sample. Journal of Affective Disorders, 113, 88-99.
Carson, A. D. (1998). The integration of interests, aptitudes and personality traits: A test of Lowman's Matrix. Journal of Career Assessment, 6(1), 83-105.
Carson, A. D. (1999). Use of a Kohonen self-organizing map to classify career clients on the basis of aptitudes. Journal of Career Assessment, 7(4), 357-380.
Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral and Health Sciences. Wiley, New York.
Correl, S. J. (2001). Gender and the career choice process: The role of biased self-assessments. The American Journal of Sociology, 106, 1691-1730.
Counseling,14, 126–134
Creed, P. A., Prideaux, L., & Patton, W. (2005). Antecedents and consequences of career decisional states in adolescence. Journal of Vocational Behavior, 67, 397-412.
Cronbach, L. J., & Gleser, G. C. (1953). Assessing similarity between profiles. Psychological Bulletin, 50(6), 456-473.
Curran, P.J., & Willoughby, M.T. (2003). Implications of latent trajectory models for the study of developmental psychopathology. Development and psychopathology, 15(3), 581-612.
Dempster. A., Laird. N., & Rubin. D. (1977). Maximum likelihood from incomplete data
via the EM-alogrithm. Journal of the Royal Statistical Society, 39, 1-38.
Development,12, 12–20.
distress, and depression. Journal of Youth and Adolescence, 29(2), 249-271.
Donnay, D. A. C., Morris, M. L., Schaubhut, N. A., & Thompson, R. C. (2004). Strong interest inventory manual (rev. ed.). Mountain View, CA: CPP.
Einarsdóttir, S., Rounds, J. B., & Su, R. (2010). Holland in Iceland revisited: an emic approach to evaluating U.S. vocational interest models. Journal of Counseling Psychology, 57, 361-367.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Enders, C. K., & Tofighi, D. (2008). The impact of misspecifying class-specific residual variances in growth mixture models. Structural Equation Modeling, 15(1), 75-95.
Entwistle, D. R., Alexander, K. L., & Olson, L. S. (1994). The gender gap in math: Its possible origins in neighborhood effects. American Sociological Review, 59(6), 822-838.
Erwin, T. D. (1987). The construct validity of Holland's differentiation concept. Measurement and Evaluation in Counseling and Development, 20(3), 106-112.
Feingold, A. (1992). Sex differences in variability in intellectual abilities: A new look at an old controversy. Review of Educational Research, 62, 61-84.
Fouad, N. A., & Mohler, C. J. (2004). Cultural validity of Holland's theory and the Strong interest inventory for five racial/ethnic groups. Journal of Career Assessment, 12, 423-439.
Frantz, T. T., & Walsh, E. P. (1972). Exploration of Holland's theory of vocational choice in graduate school environments. Journal of Vocational Behavior, 2(3), 223-232.
Friedman, A. I. (1991). Areas of concern and sources of advice for Israeli adolescents. Adolescents, 26, 967-976.
Gati, I. (1979). A hierarchical model for the structure of vocational interests. Journal of Vocational Behavior, 15, 90-106.
Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309-324.
Gati, I., & Saka, N. (2001). High school students' career-related decision-making difficulties. Journal of Counseling & Development, 79(3), 331-340.
Gati, I., Saka, N., & Krausz, M. (2001). Should I use a computer-assisted career guidance system? It depends on where our career decision-making difficulties lie. British Journal of Guidance and Counselling, 29, 301-321.
Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24(3), 229-252.
Gjesme, T. (1979). Future time orientation as a function of achievement motives, ability, delay of gratification, and sex. The Journal of Psychology: Interdisciplinary and Applied, 101(2), 173-188.
Gottfredson, L. S. (2002). Gottfredson's theory of circumscription, compromise, and self-creation. In D. Brown (Ed.), Career choice and development (4th ed.). San Francisco: Jossey-Bass.
Haladyna, T. M. (1997). Writing test items to evaluate higher order thinking. Needham Heights: Allyn & Bacon.
Halpern, D. F., & LaMay, M. L. (2000). The smarter sex: A critical review of sex differences in intelligence. Educational Psychology Review, 12(2), 229-246.
Hansen, J. C. (2005). Assessment of Interests. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 281-304). John Wiley & Sons Inc.
Hansen, J. C., & Campbell, D. P. (1985). Manual for the SVIB-SCII (4th ed.). Palo Alto, CA: Stanford University Press.
Harmon, L. W., Hansen, J. C., Borgen, F. H., & Hammer, A. L. (1994). Strong interest inventory applications and technical guide. Palo Alto, CA: Consulting Psychologists Press.
Herr, E. L., & Cramer, S. H. (1984). Career Guidance & Counseling Through the Life Span (2nd ed.). Boston: Little, Brown & Company.
Herr, E. L., & Cramer, S. H. (1996). Career guidance and counseling through life span: systematic approaches (5th ed.). New York: Harper Collins.
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60(4), 549-571.
Hirschi, A. (2009). Development and criterion validity of differentiated and elevated interests in adolescence. Journal of Career Assessment, 17(4), 384-401.
Hirschi, A., & Läge, D. (2007). Holland’s secondary constructs of vocational interests and career choice readiness of secondary students: measures for related but different constructs. Journal of Individual Differences, 28, 205-218.
Hirschi, A., & Vondracek, F. W. (2009). Adaptation of career goals to self and opportunities in early adolescence. Journal of Vocational Behavior, 75(2), 120-128.
Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources, Inc.
Holland, J. L., Fritzsche, B. A., & Powell, A. B. (1997). The self-directed search technical manual. Odessa, FL: Psychological Assessment Resources.
Hood, A. B., & Johnson, R. W. (2007). Assessment in Counseling: A Guide to the Use of Psychological Assessment Procedures. Alexandria, VA: American Counseling Association.
Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34(2), 113-125.
Iachan, R. (1984). A family of differentiation indices. Psychometrika, 49(2), 217-222.
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists
Jackson, D. N. (1977). Jackson vocational interest survey manual. Port Huron, MI: Research Psychologists Press.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Comput Surv, 31(3), 264-323.
Johnson, W., & Bouchard, T. J. (2009). Linking abilities, interests, and gender via latent class analysis. Journal of Career Assessment, 17, 3-38.
Kaufman, L., & Rousseeuw, P. J. (1990). Partitioning around Medoids (Program PAM). In: L. Kaufman, P. J. Rousseeuw, John Wiley, & Sons, Hoboken (Eds.), Finding Groups in Data: An Introduction to Cluster Analysis (pp. 68-125). Hoboken: John Wiley & Sons.
Kimura, D. (1992). Sex differences in the brain. Scientific American, 267, 119-125
Klein, A. G., & Muthén, B. O. (2007). Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects. Multivariate Behavioral Research, 42(4), 647-673.
Lanza, S. T., Rhoades, B. L., Greenberg, M. T., Cox, M. J., & the Family Life Project Key Investigators. (2011). Modeling multiple risks during infancy: Contributions of a person-centered approach. Infant Behavior and Development, 34(3), 390-406.
Lee, M., & Larson, R. (2000). The Korean ‘examination hell’: Long hours of studying,
Lee, M., & Larson, R. (2000). The Korean ‘examination hell’: Long hours of studying, distress, and depression. Journal of Youth and Adolescence, 29(2), 249-271.
Lemos, G. C., Abad, F. J., Almeida, L. S., & Colom, R. (2013). Sex differences on g and non-g intellectual performance reveal potential sources of STEM discrepancies. Intelligence, 41(1), 11-18.
Leung, S. A., Conoley, C. W., Scheel, M. J., & Sonnenberg, R. T. (1992). An examination of the relation between vocational identity, consistency, and differentiation. Journal of Vocational Behavior, 40(1), 95-107.
Leuty, M. E., Hansen, J.-I. C., & Speaks, S. Z. (2016). Vocational and leisure interests: A profile-level approach to examining interests. Journal of Career Assessment, 24(2), 215-239.
Linn, R. L., & Gronlund, N. E. (2000). Measurement and assessment in teaching (8th ed.). Upper Saddle River, NJ: Prentice Hall.
Lowman, R. L. (1991). The clinical practice of career assessment: Interests, abilities, and personality. American Psychological Association.
Lubke, G., & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14(1), 26-47.
Lynn, R., & Mikk, J. (2009). Sex differences in reading achievement. TRAMES, 13, 3-13.
Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: a comparison with K-means. Canadian Journal of Marketing Research, 20(1), 36-43.
Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal, 16, 191-225.
Martel, M. M., Goth-Owens, T., Martinez-Torteya, C., & Nigg, J. T. (2010). A person-centered personality approach to heterogeneity in Attention-Deficit/Hyperactivity Disorder (ADHD). Journal of Abnormal Psychology, 119(1), 186-196.
Mathis, E. L. (2019). Profiles of Interest in Holland's Theory in Relation to Personality and Sex (dissertation).
Mcclintock, M. K., Dale, W., Laumann, E.O., & Waite, L. J. (2016). Empirical redefinition of comprehensive health and well-being in the older adults of the United States. Proceedings of the National Academy of Sciences of the United States of America, 113(22), E3071-3080.
McKay, D. A., & Tokar, D. M. (2012). The HEXACO and five-factor models of personality in relation to RIASEC vocational interests. Journal of Vocational Behavior, 81, 138-149.
McLachlan, G. J., & Peel, D. (2000). Mixtures of Factor Analyzers, in Langley (Ed.): Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, 599-606.
McLarnon, M. J., Carswell, J. J., & Schneider, T. J. (2015). A case of mistaken identity? Latent profiles in vocational interests. Journal of Career Assessment, 23(1), 166-185.
Merritt, R. D. (2008). Aptitude testing. Research Starters: Academic Topic Overviews (pp. 1-7). EBSCO Publishing Inc.
Moir, A., & Jessel, D. (1993). Brain sex: The real difference between men and women. New York: Bantam Dell Publishing Group.
Mokros, A., Hare, R. D., Neumann, C. S., Santtila, P., Habermeyer, E., & Nitschke, J. (2015). Variants of psychopathy in adult male offenders: A latent profile analysis. Journal of Abnormal Psychology, 124(2), 372-386.
Morin, A. J. S., Morizot, J., Boudrias, J., & Madore, I. (2011). A multifoci person-centered perspective on workplace affective commitment: A latent profile/factor mixture analysis. Organizational Research Methods, 14, 58-90.
Murphy, K. R., & Davidshofer, C. O. (2005). Psychological testing: Principles and applications (6th ed.). Prentice-Hall, Inc.
Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.
Muthén, L.K., & Muthén, B. O. (1998-2010). Mplus User’s Guide (6th ed.). Los Angeles, CA: Muthén & Muthén.
Nowell, A., & Hedges, L.V. (1998). Trends in gender differences in academic achievement from 1960 to 1994: An analysis of differences in mean, variance and extreme scores. Sex Roles, 39, 21-43.
O'Neil, J. M. (1977). Holland's theoretical signs of consistency and differentiation and their relationship to academic potential and achievement. Journal of Vocational Behavior, 11(2), 166-173.
Osborn, D. S., & Zunker, V. C. (2006). Using Assessment Results for Career Development (7th ed.). Pacific Grove, CA: Brooks/ Cole.
Pässler, K., Beinicke, A., & Hell, B. (2015). Interests and intelligence: A meta-analysis. Intelligence, 50, 30-51.
Patrick, L., Care, E., & Ainley, M. (2011). The relationship between vocational interests, self-efficacy, and achievement in the prediction of educational pathways. Journal of Career Assessment, 19(1) 61-74.
Peiser, C., & Meir, E. I. (1978). Congruency, consistency, and differentiation of vocational interests as predictors of vocational satisfaction and preference stability. Journal of Vocational Behavior, 12(3), 270-278.
Perera, H. N., & McIlveen, P. (2018). Vocational interest profiles: Profile replicability and relations with the STEM major choice and the Big-Five. Journal of Vocational Behavior, 106, 84-100.
Prediger, D. J. (1981). Mapping occupations and interests: A graphic aid for vocational guidance and research. Vocational Guidance Quarterly, 30, 21-36.
Prediger, D. J. (2002). Abilities, interests, and values: Their assessment and their integration via the world-of-work map. Journal of Career Assessment, 10(2), 209-232.
Prediger, D. J., & Swaney, K. B. (2004). Work task dimensions underlying the world of work: Research results for diverse occupational databases. Journal of Career Assessment, 12(4), 440-459.
Proyer, R. T. (2006). The relationship between vocational interests and intelligence: Do findings generalize across different assessment methods? Psychology Science, 48(4), 463-476.
Pryor, R. G. L. (2007). Assessing complexity: Integrating
Pryor, R. G. L., & Bright, J. E. H. (2003). The chaostheory of careers. Australian Journal of Career Development, 12, 12-20.
Pryor, R. G. L., & Bright, J. E. H. (2003a). The chaos
Pryor, R. G. L., & Bright, J. E. H. (2007). The chaostheory of careers: Theory, practice and process. Career Planning and Adult Development Journal, 23, 30-45.
Pryor, R. G. L., & Bright, J. E. H. (2007a). The Chaos
Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103-124 ·
Reuterfors, D. L., Schneider, L. J., & Overton, T. D. (1979). Academic achievement: An examination of Holland's congruency, consistency, and differentiation predictions. Journal of Vocational Behavior, 14(2), 181-189.
Rounds, J, & Tracey, J. (1996). Cross-cultural structural equivalence of RIASEC models and measures. Journal of Counseling Psychology, 43, 310-329.
Rounds, J. (1995). Vocational interests: evaluation of structural hypotheses. In: D. Lubinski, & R. V. Dawis (Eds.), Assessing Individual Differences in Human Behavior: New Concepts, Methods, and Findings (pp. 177-232). Palo Alto, CA: Consulting Psychologists Press.
Sand, W. A., Water, B. K., & McBride, J. R. (Eds.) (1997). Computerized adaptive testing: from inquiry to operation. Washington, DC: American Psychological Association.
Savickas, M. L. (1999). The psychology of interests. In M. L. Savickas & A. R. Spokane (Eds.), Vocational interests: Meaning, measurement, and counseling use (pp. 19-56). Davies-Black Publishing.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333-343.
Segal, H. G., DeMeis, D. K., Wood, G. A., & Smith, H. L. (2001). Assessing future possible selves by gender and socioeconomic status using the anticipated life history measure. Journal of personality, 69(1), 57-87.
Slot, E. M., Bronkhorst, L. H., Akkerman, S. F., & Wubbels, T. (2020). Vocational interest profiles in secondary school: Accounting for multiplicity and exploring associations with future-oriented choices. Journal of Educational Psychology. Advance online publication.
Spokane, A. R. (1985). A review of research on person-environment congruence in Holland’s theory of careers. Journal of Vocational Behavior, 26, 306-343.
Spokane, A. R., & Jacob, E. J. (1996). Career and Vocational Assessment 1993 -1994: A Biennial Review. Journal of Career Assessment, 4(1), 1-32.
Spokane, A. R., & Walsh, W. B. (1978). Occupational level and Holland's theory for employed men and women. Journal of Vocational Behavior, 12(2), 145-154.
Stoet, G., & Geary, D. C. (2015). Sex differences in academic achievement are not related to political, economic, or social equality. Intelligence, 48, 137-151.
Strand, S., Deary, I. J., & Smith, P. (2006). Sex differences in cognitive ability test scores: A UK national picture. British Journal of Educational Psychology, 76, 463-480.
Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135(6), 859-884.
Sung, Y. T., & Chao, T. Y. (2015). Construction of the examination stress scale for adolescent students. Measurement and Evaluation in Counseling and Development, 48(1), 44-58.
Sung, Y. T., & Wu, J. S. (2018). The Visual Analogue Scale for Rating, Ranking and Paired-Comparison (VAS-RRP): A new technique for psychological measurement. Behavior Research Methods, 50(4), 1694-1715.
Sung, Y. T., Chao, T. Y., & Tseng, F. L. (2016). Reexamining the relationship between test anxiety and learning achievement: An individual- differences perspective. Contemporary Educational Psychology, 46, 241-252.
Sung, Y. T., Cheng, Y. W., & Hsueh, J. H. (2017). Identifying the career-interest profiles of junior-high-school students through latent profile analysis. Journal of Psychology, 151(3), 229-246.
Sung, Y. T., Cheng, Y. W., & Wu, J. S. (2015). Constructing a situation-based career interest assessment for junior-high-school students and examining their interest structure. Journal of Career Assessment, 24(2), 347-365.
Sung, Y. T., Huang, L. Y., Tseng, F. L., & Chang, K. E. (2014). The aspects and ability groups in which little fish perform worse than big fish: examining the big-fish-little-pond effect in the context of school tracking. Contemporary Educational Psychology, 39(3), 220-232.
Super, D. E. (1957). The Psychology of Careers. New York: Harper & Row.
Super, D. E. (1980). A life-span, life space approach to career development. Journal of Vocational Behavior, 13, 282-298.
Super, D. E. (1990). A life-span, life space approach to career development. In: D. Brown & L. Brooks (Eds.), Career Choice and Development: applying contemporary theories to practice. San Francisco: Josey-Bass.
Super, D. E., Savickas, M. L., & Super, C. M. (1996). A life-span, life-space approach to careers. In: D. Brown & L. Brooks (Eds.), Career Choice and Development (3rd ed.). San Francisco: Jossey-Bass.
Super, D. E., Osborne, L., Walsh, D., Brown, S., & Niles, S. G. (1992). Developmental career assessment in counseling: The C-DAC model. Journal of Counseling and Development, 71, 74-80.
Šverko, I., & Babarović, T. (2016). Integrating personality and career adaptability into vocational interest space. Journal of Vocational Behavior, 94, 89-103.
Swain, R. (1984). Easing the transition: A career planning course for college students. Personnel and Guidance Journal, 62(9), 529-532.
Swanson, J. L., & Hansen, J. C. (1986). A clarification of Holland’s construct of differentiation: The importance of elevation. Journal of Vocational Behavior, 28(2), 163-173.
Tak, J. (2004). Structure of vocational interests for Korean college students. Journal of Vocational Assessment, 12, 298-311.
Tang, M. (2009). Examining the application of Holland’s theory to vocational interests and choices of Chinese college students. Journal of Vocational Assessment, 17, 86-98.
Taveira, M. D. C., Silva, M. C., Rodriguez, M. L., & Maia, J. (1998). Individual characteristics and career exploration in adolescents. British Journal of Guidance and Counselling, 26, 89-104.
theory of careers. Australian Journal of Career
Theory of Careers: Theory, practice and process.
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., & Williams, S. E. (2004). Extinction risk from climate change. Nature, 427(6970), 145-148.
Thomas, H. (1993). A theory explaining sex difference in high mathematical ability has been around for some time. Behavioral and Brain Sciences, 16(1), 187-215.
Tracey, T. J. G. (2001). The development of structure of interests in children: Setting the stage. Journal of Vocational Behavior, 59, 89-104.
Vermunt, J. K. (2004). Latent profile model. In M. S. Lewis-Beck, A. Bryman, & T. F. Liao (Eds.), The Sage encyclopedia of social sciences research methods (pp. 554-555). Thousand Oakes: Sage.
Vermunt, J. K., & Magidson, J. (2003). Latent class models for classification. Computational Statistics and Data Analysis, 41, 531-537.
Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250-270.
Wai, J., Cacchio, M., Putallaz, M., & Makel, M. C. (2010). Sex differences in the right tail of cognitive abilities: A 30-year examination. Intelligence 38, 412-423.
Wang, C. P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100(471), 1054-1076.
Wang, M., & Hanges, P. J. (2011). Latent Class Procedures: Applications to Organizational Research. Organizational Research Methods, 14(1), 24-31.
Whitfield, E. A., Feller, R. W., & Wood, C. (Eds.). (2009). A counselor’s guide to career assessment instruments (5th ed., pp. 13-25). Alexandria, VA: National Career Development Association.
Zimbardo, P. G., Keough, K. A., & Boyd, J. N. (1997). Present time perspective as a predictor of risky driving. Personality and Individual Differences, 23(6), 1007-1023.