Citizen science programs provide opportunities for students to help professional scientists while fostering science achievement and motivation. Instruments which measure the effects of this type of programs on student motivational beliefs are limited. The purpose of this study was to describe the process of examining the reliability and validity of The Citizen Science Self-Efficacy Scale (CSSES) designed to measure the effectiveness of citizen science programs on student self-efficacy for scientific observation skills. Fifteen (n =15) field experts and 248 (n = 248) eighth grade students participated in three studies. The results suggest that the psychometric properties of this scale are sufficient. Implications for the development and utility of self-efficacy scales in a variety of citizen science contexts are discussed. The aim of the present study is twofold: (a) to establish the psychometric properties of a scale developed to measure student self-efficacy beliefs for scientific observations in citizen science programs and (b) to describe the process in the validation of a self-efficacy scale to support researchers who want to create their own scales for similar citizen science programs. Three studies were conducted to develop the Citizen Science Scale (CSSES) and evaluate its psychometric properties. The purpose of the CSSES was to develop a measure suitable for analysis within a social cognitive career framework and informal natural science contexts. The findings in the present study found that the measure had an acceptable unitary factorial structure and high internal reliability of .89 for the CSSES. The purpose of the Citizen Science Self-Efficacy Scale (CSSES) is to assess individual’s beliefs about their capabilities for scientific observational skills. This scale is applicable to measuring individual’s self-efficacy in outdoor learning contexts (e.g., horseshoe crab citizen science context). Given that self-efficacy is a strong predictor of academic achievement and motivation, self-efficacy scales like the CSSES may provide a way for stakeholders involved in outdoor education to measure student gains and to substantiate program effectiveness. From a methods standpoint, the contribution of this work is to serve as a guide of how to develop a self-efficacy scale.

Bandura, A. (1997). The exercise of control. New York, NY: W. H. Freeman and Company.

Barlow, K. E., Briggs, P. A., Haysom, K. A., Hutson, A. M., Lechiara, N. L., Racey, P. A., Walsh A. L., & Langton, S. D. (2015). Citizen science reveals trends in bat populations: The National Bat Programme in Great Britain. Biological Conservation 182, 14-26. doi: 10.1016/j.biocon.2014.11.022 

Bembenutty, H., Clearly, T., & Kitsantas, A., (2013). Self-regulated learning applied across diverse disciplines. A Tribute to Barry J. Zimmerman. Information Age Publishing.

Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching 43(5), 485-499. doi: 10.1002/tea.20131

Cannon, A. R., Chamberlain, D. E., Tomas, M. P., Hatchwell, B. J., C& Gaston, K. J. (2005). Trends in the use of private gardens by wildbirds in Great Britain, 1995-2002. Journal of Applied Ecology 42, 659-671. doi: 10.1111/j.1365-2664.2005.01050.x

Cartwright, N. (1989). Nature’s capacities and their measurements. Oxford, UK: Clarendon Press.

Cleary, T. J., & Labuhn, A. S. (2013) In Bembenutty, Cleary, & Kitsantas’ (Eds.) Applications of self-regulated learning across diverse disciplines: A tribute to Barry Zimmerman. Charlotte, NC: Information Age Publishing, Inc.

Corbin, J. & Strauss, A. (2007). Basics of qualitative research: Techniques and procedures for       developing grounded theory (3rd ed.). Thousand Oaks, CA: Sage

Crall, A. W., Jarnevich, C. S., Young, N. E., Panke, B. J., Renze, M., & Stohlgren (2015). Citizen science contributes to our knowledge of invasive plant species distributions. Biological Invasions. doi: 10.1007/s10530-015-0885-4

Crall, A. W., Newman, G. J., Stohlgren, T. J. Holfelder, K. A., Graham, J., & Waller, D. M. (2011). Assessing citizen science; data quality: An invasive species case. Conservation Letters 4(6), 433-442. doi: 10.1111/j.1755-263X.2011.00196.x

Cronje, R., Rohlinger, S., Crall, A., & Newman, G. (2011). Does participation in citizen science improve scientific literacy? A study to compare assessment methods. Applied Environmental Education and Communication 10, 135-145. doi: 10.1080/1533015X.2011.603611

Day, R., & Allen, T. D. (2002). The relationship between career motivation and self-efficacy with protégé career success. Journal of Vocational Behavior 64, 72-91. doi: 10.1016/S0001-8791(03)00036-8

DiBenedetto, M. K., & Zimmerman, B. J. (2013). Construct and predictive validity of microanalytic measures of students' self-regulation of science learning. Learnng and Individual Differences 26, 30-41: doi: 10.1016/j.lindif.2013.04.004

Dimitrov, D. M. (2012). Statistical methods for validation of assessment scale data in counseling and related fields. Alexandria: American Counseling Association.

Eberbach, C., & Crowley, K. (2009). From everyday to scientific observation: How children learn to observe the biologist’s world. Review of Educational Research 79(1), 39-68. doi: 10.3102/003465430832589

Fouad, N., Smith, P. L., & Enoch, L. (1997). Reliability and validity evidence for the middle school self-efficacy scale. Measurement and Evaluation in Counseling and Development 30(1), 17-31. doi: 10.1037/t20421-000 

Fowler, A., Whyatt, J. D., Davies, G., Ellis, R.: How Reliable are Citizen-Derived Scientific Data? Assessing the Quality of Contrail Observations Made by the General Public. Transactions in GIS, 17(4): 488–506 (2013) doi:10.1111/tgis.12034

Gardiner, M. M., Alle, L. L., Brown, P. M. J., Losey, J. E., Roy, H. E., & Smyth, R. R.(2012). Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Frontiers in Ecology and the Environment. doi:10.1890/110185

Harwell, M., Guzey, S. S., Moreno, M., Moore, T. J., Phillips, A., & Roehrig, G. H. (2015). A study of STEM asssessments in engineering, science, and mathematics for elementary and middle school students. School Science and Mathematics 115(2). doi: 10.1111/ssm.12105 

Hiller, S. E., & Kitsantas, A. (2015). Fostering student metacognition and motivation in STEM through citizen science programs. In A. Peña-Ayala’s (Ed.) Metacognition: Fundaments, Applications, and Trends. Cham, Switzerland: Springer.

Hiller, S. E., & Kitsantas, A. (2014). The effect of a horseshoe crab citizen science program on student science performance and STEM career motivation. School Science and Mathematics Journal 114(6), 302-311. doi: 10.1111/ssm.12081

Hiller, S. E., & Reybold, L. E. (2011, October). Field expert’s perceptions of scientific observations in the natural world. Presentation at the annual research symposium of the North American Association for Environmental Education, Raleigh, NC.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling 6, 1-55. doi: 10.1080/10705519909540118 

Jeanpierre, B., Oberhauser, K., & Freeman, C. (2005). Characteristics of professional development that effect change in secondary science teachers’ classroom practices. Journal of Research in Science Teaching 42(6), 668-690. doi: 10.1002/tea.20069

Jensen, M., Scherer, R., & Schroeders, U. (2015). Students’ self-concept and self-efficacy in the sciences: Differential relations to antecedents and educational outcomes. Contemporary Psychology 41, 13-24. doi: 10.1016/j.cedpsych.2014.11.002

Lent, R. W., Sheu, H-B., Singley, D., Schmidt, J. A., Schmidt, L. C., & Gloster, C. S. (2008). Longitudinal relations of self-efficacy to outcome expectations, interests, and major choice goals in engineering students. Journal of Vocational Behavior 73, 328-335. doi: 10.1016.j.jvb.2008.07.005

Mu, X. (1998, August). High school experience and career maturity in young adulthood. Paper presented at the 24th international congress of Applied Psychology. San Francisco, CA.

Navarro, R.L., Flores, L. Y., & Worthington, R. L. (2007). Mexican American middle school students’ goal intentions in mathematics and science: A test of social cognitive career theory. Journal of Counseling Psychology 54(3), 320-335. doi: 10.1037/0022-0167.54.3.320

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 (2011) doi: 10.1177/1069072710382615

Patton, M. Q. (2002).  Qualitative research and evaluation methods (3rd ed.). Newbury Park, CA: Sage.

Pocock, M. J. O., & Evans, D. M. (2014). The success of the Horse-Chestnut Leaf Miner, Cameraria ohridella, in the UK reveladed with hypothesis-led citizen science. PLoS ONE, 9(1), 1-9. doi: 0.1371/journal.pone.0086226

Rogers, M. E., & Creed, P. A. (2011). A longitudinal examination of adolescent career planning and exploration using a social cognitive career theory framework. Journal of Adolescence 34, 163-172. doi: 10.1016/j.adolescence.2009.12.010

Sako, K. (2015). Citizen science inaugural conference: Updates from the field. Retrieved from the National Center for Advancement of Informal Science Education website: http://www.informalscience.org/news-views/2015-citizen-science-association-inaugural-conference-updates-field

Schunk, D. H., & Pajares, F. (2005). Competence perceptions and academic functioning. In Elliott and Dweck’s (Eds.), Handbook of competence motivation. New York, NY: The Guilford Press.

Snäll, T., Kindvall, O., Nilsson, J., & Pärt, T. (2011). Evaluating citizen-based presence data for bird monitoring. Biological conservation, 144, 804-810. doi: 10.1016/j.biocon.2010.11.010.

Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research 25, 173-180. doi: 10.1207/s15327906mbr2502_4 

Sutton, S. (2009). Increasing science literacy among English language learners through plant phenological monitoring: A citizen science program at Sequoia and Kings Canyon National Parks. (Master’s thesis). Proquest Information and Learning Company, UMI Number, 1473523

Trumbull, D. J., Bonney, R., & Grudens-Schuck, N. (2005). Developing materials to promote inquiry: Lessons learned. Science Education 89, 879-900. doi: 10.1002/sce.20081

Tucker, L. R. & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38, 1-10. doi: 10.1007/bf02291170 

Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path, Educational Psychologist 48(3), 135-147 (2013) doi: 10.1080/00461520.2013.794676

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekart, P. R. Pintrich, and Zeidner, M. (Eds.) Handbook of Self-Regulation. New York, NY: Academic Press.

Zimmerman, B. J., & Kitsantas, A. (2007). Reliability and validity of Self-Efficacy for Learning Form (SELF) scores of college students. Journal of Psychology 215(3), 157- 163. doi: 10.1027/0044-3409.215.3.157 

Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: Shifting from process to outcome self-regulatory goals. Journal of Educational Psychology 91, 1-10. doi: 10.1037//0022-0663.91.2.241 

Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and stategy use. Journal of Educational Psychology 82, 51-59. doi: 10.1037/0022-0663.82.1.51 

Zimmerman, B. J., & Schunk, D. H. (2009). Motivation: An essential dimension of self-regulated learning. In Zimmerman & Schunk’s (Eds.), Motivation and Self-Regulated Learning. New York: Routledge.