The Mathematical Resilience Scale measures students’ attitudes toward studying mathematics, using three correlated factors: Value, Struggle, and Growth. The Mathematical Resilience Scale was developed and validated using exploratory and confirmatory factor analyses across three samples. Results provide a new approach to gauge the likelihood of student participation and persistence in mathematics.
AutinF.CroizetJ. (2012). Improving working memory efficiency by reframing metacognitive interpretation of task difficulty. Journal of Experimental Psychology: General, 141, 610–618.
4.
BanduraA. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175–1184.
5.
BanduraA. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9, 75–78.
6.
BlackwellL.TrzesniewskiK.DweckC. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263.
7.
BorkoH.LivingstonC. (1989). Cognition and improvisation: Differences in mathematics instruction by expert and novice teachers. American Educational Research Journal, 26, 473–498.
8.
BuffA.ReusserK.RakoczyK.PauliC. (2011). Activating positive affective experiences in the classroom: “Nice to have” or something more?Learning and Instruction, 21, 452–466.
ChamberlinS. (2010). A review of instruments to assess affect in mathematics. Journal of Mathematics Education, 3, 167–182.
11.
ChenX. (2013). STEM attrition: College students’ paths into and out of STEM fields (NCES 2014-001). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
12.
ChiuD.BeruY.WatleyE.WubuS.SimsonE.KessingerR.. . . WigfieldA. (2008). Influences of math tracking on seventh-grade students’ self-beliefs and social comparisons. Journal of Educational Research, 102, 125–136.
ClarkL. M.DePiperJ. N.FrankT. J.NishioM.CampbellP. F.SmithT. M.. . . ChoiY. (2014). Teacher characteristics associated with mathematics teachers’ beliefs and awareness of their students’ mathematical dispositions. Journal for Research in Mathematics Education, 45, 246–284.
15.
ComreyA. (1988). Factor-analytic methods of scale development in personality and clinical psychology. Journal of Consulting and Clinical Psychology, 56, 754.
16.
DeciE.VallerandR.PelletierL.RyanR. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26, 325–346.
DuckworthA. L.PetersonC.MatthewsM. D.KellyD. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087.
19.
DweckC. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048.
20.
DweckC. (2000). Self-theories: Their role in motivation, personality and development. Lillington, NC: Psychology Press.
21.
DweckC. (2006). Mindset: The new psychology of success. New York, NY: Random House Digital.
22.
DweckC. (2007). Is math a gift? In CeciS.WilliamsW. (Eds.), Why aren’t more women in science? Top researchers debate the evidence (pp. 47–55). Washington, DC: American Psychological Association.
23.
EcclesJ. (1983). Expectancies values and academic behaviors. In SpenceJ. (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–145). San Francisco, CA: W. H. Freeman.
24.
EinsteinA. (2010). The ultimate quotable Einstein. Princeton, NJ: Princeton University Press.
25.
Else-QuestN. M.MineoC. C.HigginsA. (2013). Math and science attitudes and achievement in the intersection of gender and ethnicity. Psychology of Women Quarterly, 37, 293–309.
26.
EthingtonC. A.WolfeL. M. (1986). A structural model of mathematics achievement for men and women. American Educational Research Journal, 23, 65–75.
27.
HembreeR. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21, 33–46.
28.
HuL.BentlerP. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
KarairmakO. (2010). Establishing the psychometric qualities of the Connor-Davidson resilience scale (CD_RISC) using exploratory and confirmatory factor analysis in a trauma survivor sample. Psychiatry Research, 179, 350–356.
31.
KlineR. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
32.
LackeyN.WingateA. (1998). The pilot study: One key to research success. In BrinkP. J.WoodM. J. (Eds.), Advanced design in nursing research (2nd ed., pp. 375–386). Thousand Oaks, CA: Sage.
33.
LichtB.DweckC. (1984). Determinants of academic achievement: The interaction of children’s achievement orientations with skill area. Developmental Psychology, 20, 618–636.
34.
LutharS. (2006). Resilience in development: A synthesis of research across five decades. In CicchettiD.CohenD. J. (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder and adaptation (2nd ed., pp. 739–795). Hoboken, NJ: Wiley.
35.
LutharS.CicchettiD.BeckerB. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71, 543–562.
36.
MaX. (2006). Cognitive and affective changes as determinants for taking advanced mathematics courses in high school. American Journal of Education, 113, 123–149.
37.
MaX.KishorN. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for the Research in Mathematics Education, 28, 26–47.
38.
McCoachD.MaduraJ.GableR. (2013). Instrument development in the affective domain: School and corporate applications. New York, NY: Springer.
39.
MastenA. (2001). Ordinary magic: Resilience process in development. American Psychologist, 56, 227–238.
40.
McKenzieJ.WoodM.KoteckiJ.ClarkJ.BreyR. (1999). Establishing content validity: using qualitative and quantitative steps. American Journal of Human Behavior, 23, 311–318.
41.
NardiE.StewardS. (2003). Is mathematics T.I.R.E.D? A profile of quiet disaffection in the secondary mathematics classroom. British Educational Research, 29, 345-367.
O’ConnorB. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instrumentation, and Computers, 32, 396–402.
44.
OlssonC.BondL.BurnsJ.Vella-BrodrickD.SawyerS. (2003). Adolescent resilience: A concept analysis. Journal of Adolescence, 26, 1–11.
45.
OsborneJ.FitzpatrickD. (2012). Replication analysis in exploratory factor analysis: What it is and why it makes your analysis better. Practical Assessment, Research & Evaluation, 17(15). Retrieved from http://pareonline.net/getvn.asp?v=17&n=15
PerezT.CromleyJ.KaplanA. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106, 315–329.
48.
PettM.LackeyN.SullivanJ. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.
49.
ReumanD. A. (1989). How social comparison mediates the relation between ability-grouping practices and students’ achievement expectancies in mathematics. Journal of Educational Psychology, 81, 178–189.
50.
RhemtullaM.Brosseau-LiardP. E.SavaleiV. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 17, 354.
51.
RichardsonF.SuinnR. (1972). The mathematics anxiety rating scale: Psychometric data. Journal of Counseling Psychology, 19, 551–554.
52.
RiveraH.WaxmanH. (2011). Resilient and nonresilient Hispanic English language learners’ attitudes towards their classroom learning environment in mathematics. Journal for Education for Students Placed at Risk, 16, 185–200.
53.
RyanR.DeciE. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development and well-being. American Psychologist, 55, 68–78.
TabachnickB.FidellL. (1996). Using multivariate statistics (6th ed.). New York, NY: HarperCollins.
57.
ThompsonB. (2010). Exploratory and confirmatory factor analysis. Washington, DC: American Psychological Association.
58.
TobiasS. (1978). Overcoming math anxiety. New York, NY: Norton.
59.
ToughP. (2012). How children succeed: Grit, curiosity, and the hidden power of character. Boston, MA: Houghton Mifflin Harcourt.
60.
YeagerD.DweckC. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47, 302–314.
61.
YuanK.-H. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115–148.
62.
ZimmermanB. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166–183.