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Is Science Me? Exploring Middle School Students’ STE-M Career Aspirations

Aschbacher, Pamela R., Ing, Marsha, Tsai, Sherry M.·2014·Journal of Science Education and Technology
DOI 10.1007/s10956-014-9504-x
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Research works printed but not linked: 51

  1. Archer L, DeWitt J, Osborne J, Dillon J, Willis B, Wong B (2012) Science aspirations, capital, and family habitus: how families shape children’s engagement and identification with science. Am Educ Res J 49(5):881–908 · DOI
  2. Aschbach PR, Ing M, Tsai S (2013) Boosting student interest in science. Phi Delta Kappan 95(2):47–51 · DOI
  3. Aschbacher PR, Li E, Roth EJ (2010) Is science me? high school students’ identities, participation and aspirations in science, engineering, and medicine. J Res Sci Teach 47(5):564–582 · DOI · Paper page
  4. Asparouhov T, Muthén BO (2012) Using Mplus TECH11 and TECH14 to test the number of latent classes (Mplus web notes: no. 14). Mplus, Los Angeles
  5. Atwater MM, Wiggins J, Gardner CM (1995) A study of urban middle school student with high and low attitudes toward science. J Res Sci Teach 32(6):665–677 · DOI
  6. Baker D, Leary R (1995) Letting girls speak out about science. J Res Sci Teach 32:3–27 · DOI
  7. Bakk Z, Oberski DL, Vermunt JK (2014) Relating latent class assignments to external variables: standard errors for correct inference. Polit Anal. doi: 10.1093/pan/mpu003 · DOI
  8. Bartholomew DJ (1987) Latent variable models and factor analysis. Oxford University Press, New York
  9. Brickhouse NW, Lowery P, Schultz K (2000) What kind of girl does science? The construction of school science identities. J Res Sci Teach 37(5):441–458 · DOI
  10. Calabrese Barton A, Kang H, Tan E, O’Neill TB, Bautista-Guerra J, Caitlin B (2013) Crafting a future in science: tracing middle school girls’ identity work over time and space. Am Educ Res J 50:3–75 · DOI
  11. Carlone HB (2003) Innovative science within and against a culture of “achievement”. Sci Educ 87(3):307–328 · DOI
  12. Catsambis S (1995) Gender, race, ethnicity and science education in the middle grades. J Res Sci Teach 32(3):243–257 · DOI
  13. Collins LM, Lanza ST (2010) Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences. Wiley, New York
  14. Committee on Science, Engineering, and Public Policy (2007) Rising above the gathering storm: Energizing and employing America for a brighter economic future. The National Academies Press, Washington, DC
  15. Dewitt J, Archer L, Osborne J, Dillon J, Willis B, Wong B (2010) High aspirations but low progression: the science aspirations–careers paradox amongst minority ethnic students. Int J Sci Math Educ 9(2):243–271 · DOI
  16. Eccles JS (1984) Sex differences in achievement patterns. In: Sonderegger T (ed) Nebraska symposium on motivation, vol 32. University of Nebraska Press, Lincoln, pp 97–132
  17. Eccles JS, Harold RD (1991) Gender differences in sport involvement: applying the Eccles’ expectancy-value model. J Appl Sport Psychol 3:7–35 · DOI
  18. Eccles JS, Wigfield A (1995) In the mind of the achiever: the structure of adolescents’ academic achievement related-beliefs and self-perceptions. Pers Soc Psychol Bull 21:215–225 · DOI · Paper page
  19. Eccles JS, Adler TF, Futterman R, Goff SB, Kaczala CM, Meece JL, Midgley C (1983) Expectancies, values, and academic behaviors. In: Spence JT (ed) Achievement and achievement motivation. W. H. Freeman, San Francisco, pp 74–146
  20. Eccles JS, Wigfield A, Flanagan C, Miller C, Reuman D, Yee D (1989) Self-concepts, domain values and self-esteem: relations and changes in early adolescence. J Pers 57(2):283–310 · DOI
  21. Furman M, Calabrese Barton A (2006) Capturing urban student voices in the creation of a science mini-documentary. J Res Sci Teach 43(7):667–694 · DOI
  22. Gilmartin SK, Li E, Aschbacher P (2006) The relationship between interest in physical science/engineering, science class experiences, and family contexts: variations by gender and race/ethnicity among secondary students. J Women Minor Sci and Eng 12(2–3):179–207 · DOI
  23. Goodman LA (1974) Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61:215–231 · DOI
  24. Hagenaars JA, McCutcheon AL (eds) (2002) Applied latent class analysis. Cambridge University Press, Cambridge
  25. Hanson SL (1996) Lost talent: women in the sciences. Temple University Press, Philadelphia
  26. Heinen T (1996) Latent class and discrete latent trait models: similarities and differences. Sage, Thousand Oaks
  27. Kiran D, Sungur S (2012) Middle school students’ science self-efficacy and its sources: examination of gender difference. J Sci Educ Technol 21(5):619–630 · DOI
  28. Kisiel JF (2003) Teachers, museums, and worksheets: a closer look at a learning experience. J Sci Teach Educ 14:3–21 · DOI
  29. Lazarsfeld PF, Henry NW (1968) Latent structure analysis. Houghton Mifflin, New York
  30. Maltese AV, Tai RH (2010) Eyeballs in the fridge: sources of early interest in science. Int J Sci Educ 32(5):669–685 · DOI
  31. Masnick AM, Valenti SS, Cox BD, Osman CJ (2010) A multidimensional scaling analysis of students’ attitudes about science careers. Int J Sci Educ 32(5):653–667 · DOI
  32. Masyn K (2013) Latent class analysis and finite mixture modeling. In: Little T (ed) The Oxford handbook of quantitative methods in psychology, vol 2. Oxford University Press, Oxford, pp 375–393
  33. McCoach DB, Siegle D (2003) The school attitude assessment survey-revised: a new instrument to identify academically able students who underachieve. Educ Psychol Meas 63(3):414–429 · DOI
  34. Muthén BO (1992) Latent variable modeling in epidemiology. Alcohol Health Res World 16:286–292
  35. Muthén BO (2001) Latent variable mixture modeling. In: Marcoulides GA, Schumacker RE (eds) New development and techniques in structural equation modeling. Lawrence Erlbaum Associates, Mahwah, pp 1–33
  36. NGSS Lead States (2013) Next generation science standards: for states, by states. National Academies Press, Washington, DC
  37. Oakes J (1990) Opportunities, achievement, and choice: women and minority students in science and mathematics. Rev Res Educ 16:153–222
  38. Osborne J, Simon S, Collins S (2003) Attitudes towards science: a review of the literature and its implications. Int J Sci Educ 25(9):1049–1079 · DOI
  39. Raved L, Zvi Assaraf OB (2011) Attitudes toward science learning among 10th grade students: a qualitative look. Int J Sci Educ 33(9):1219–1243 · DOI
  40. Sadker M, Sadker D, Klein S (1991) The issue of gender in elementary and secondary education. Rev Res Educ 17:269–334
  41. Schwartz G (1978) Estimating the dimensions of a model. Ann Stat 6(2):461–464 · DOI · Paper page
  42. Simpson RD, Oliver JS (1990) A summary of major influences on attitude toward and achievement in science among adolescent students. Sci Educ 74:1–18 · DOI
  43. StataCorp (2009) Stata statistical software (release 11). StataCorp LP, College Station
  44. Tai RH, Liu CQ, Maltese AV, Fan X (2006) Planning early for careers in science. Science 312(5777):1143–1144 · DOI
  45. Tan E, Calabrese Barton A (2008) Unpacking science for all through the lens of identities-in-practice: the stories of Amelia and Ginny. Cult Sci Educ 3(1):43–71 · DOI
  46. Vermunt JK (2010) Latent class modeling with covariates: two improved three-step approaches. Polit Anal 18:450–469 · DOI
  47. Vuong Q (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57:307–333 · DOI · Paper page
  48. Wigfield A, Eccles J (2000) Expectancy-value theory of achievement motivation. Contemp Educ Psychol 25:68–81 · DOI · Paper page
  49. Wigfield A, Eccles J, Mac Iver D, Reuman D, Midgley C (1991) Transitions at early adolescence: changes in children’s domain-specific self-perceptions and general self-esteem across the transition to junior high school. Dev Psychol 27(4):552–565 · DOI · Paper page
  50. Wigfield A, Eccles JS, Yoon KS, Harold RD, Arbreton A, Freedman-Doan K, Blumenfeld PC (1997) Changes in children’s competence beliefs and subjective task values across the elementary school years: a three year study. J Educ Psychol 89(3):451–469 · DOI
  51. Xu J, Coats LT, Davidson ML (2012) Promoting student interest in science: the perspective of exemplary African American teachers. Am Educ Res J 49:124–154 · DOI

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Metadata

Authors
Aschbacher, P. R., Ing, M., & Tsai, S. M.
Venue
Journal of Science Education and Technology
Publisher
Springer Science and Business Media LLC
Published
2014-07-06
Volume
23
Issue
6
Pages
735-743
ISSN
  • 1059-0145
  • 1573-1839
DOI
10.1007/s10956-014-9504-x
Cited by
79
References
6
Language
en
Licence
www.springer.com/tdm (tdm)

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CoChat. (2026). Citation check: Is Science Me? Exploring Middle School Students’ STE-M Career Aspirations. https://app.cochat.ai/papers/10.1007/s10956-014-9504-x
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Aschbacher, P. R., Ing, M., & Tsai, S. M. (2014). Is Science Me? Exploring Middle School Students’ STE-M Career Aspirations. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-014-9504-x