Past research suggests the ability to adapt to technological change by learning new technologies is a core feature of technological competence and consequential for inequality. Yet there exists no definition or measure of what people do to learn technologies that are new to them and empirically link this to inequality. To address this gap, I conducted studies involving over 2,000 adolescents to develop and validate a measure of what I call “digital adaptability,” the use of five habits that help individuals learn technologies that are new to them. The studies included observation and cognitive interviews to describe adaptability and develop an initial item pool, a pilot to narrow items using structural equation modeling, a full test with 897 eighth-grade students in Chicago with analysis of convergent and discriminant validity, and a replication study with 1,285 high school students near Boston. Finally, using Chicago and Boston area data, I find adaptability correlates with students’ educational plans and career aspirations in science, technology, engineering, and mathematics —linking digital adaptability to students’ futures. Overall, the digital adaptability measure provides a critical theoretical and empirical tool for digital inequality research, practice, and policy.
AcemogluD.AutorD. (2011). Skills, tasks and technologies: Implications for employment and earnings. In AshenfelterO.CardD. (Eds.), Handbook of labor economics (Vol. 4, pp. 1043–1171). Elsevier.
2.
AlexanderP. A.JudyJ. E. (1988). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research, 58(4), 375–404.
3.
AlexandrovA. (2010). Characteristics of single item measures in Likert scale format. The Electronic Journal of Business Research Methods, 8(1), 1–12.
4.
ArumR.RoksaJ. (2011). Academically adrift: Limited learning on college campuses. University of Chicago Press.
AttewellP. (1990). What is skill?. Work and Occupations, 17(4), 422–448.
7.
AutorD. H.DornD. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553–1597.
8.
BagozziR. P.YiY.PhillipsL. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458.
9.
BottiaM. C.StearnsE.MickelsonR. A.MollerS.ParkerA. D. (2015). The relationships among high school STEM learning experiences and students’ intent to declare and declaration of a STEM major in college. Teachers College Record, 117(3), 1–46.
10.
BransfordJ.BrownA. L.CockingR. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.
11.
BrynjolfssonE.KahinB. (2000). Understanding the digital economy: Data, tools, and research. MIT Press.
12.
CampellD. T.FiskeD. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.
13.
CarbonaroW. (2005). Tracking, student effort, and academic achievement. Sociology of Education, 78(1), 27–49.
14.
CharlesM. (2017). Venus, mars, and math: Gender, societal affluence, and eighth graders’ aspirations for STEM. Socius, 3, 1–16.
15.
CheryanS.PlautV. C.DaviesP. G.SteeleC. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060.
16.
CheryanS.ZieglerS. A.MontoyaA. K.JiangL. (2017). Why are some STEM fields more gender balanced than others?Psychological Bulletin, 143(1), 1–35.
CollinsR. (1979). The credential society: An historical sociology of education and stratification. Academic Press.
19.
CovayE.CarbonaroW. (2010). After the bell: Participation in extracurricular activities, classroom behavior, and academic achievement. Sociology of Education, 83(1), 20–45.
20.
CoxN. (2008). Speaking Stata: Correlation with confidence, or Fisher’s z revisited. The Stata Journal, 8(3), 413–439.
21.
CutlerD. M.Lleras-MuneyA.VoglT. (2011). Socio-economic status and health: Dimensions and mechanisms. In GliedS.SmithP. C. (Eds.), The Oxford handbook of health economics (pp. 124–163). Oxford University Press.
22.
DedeC. (2008). A seismic shift in epistemology. EDUCAUSE Review, 43(3), 80–81.
23.
DesimoneL. M.Le FlochK. C. (2004). Are we asking the right questions? Using cognitive interviews to improve surveys in education research. Educational Evaluation and Policy Analysis, 26(1), 1–22.
24.
DeVellisR. F. (2012). Scale development: Theory and applications (3rd ed.). Sage Publications.
25.
DiMaggioP.BonikowskiB. (2008). Make money surfing the web? The impact of internet use on the earnings of U.S. workers. American Sociological Review, 73, 227–250.
26.
DiMaggioP.HargittaiE.CelesteC.ShaferS. (2004). From unequal access to differentiated use: A literature review and agenda for research on digital inequality. In NeckermanK. (Ed.), Social inequality (pp. 355–400). Russell Sage Foundation.
27.
DobranskyK.HargittaiE. (2012). Inquiring minds acquiring wellness: Uses of online and offline sources for health information. Health Communication, 27(4), 331–343.
28.
DuckworthA. L.PetersonC.MatthewsM. D.KellyD. R. (2007). Grit: Perseverance and passion for long-term goal. Journal of Personality and Social Psychology, 92(6), 1087–1101.
29.
DuncanT. G.McKeachieW. J. (2005). The making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist, 40(2), 117–128.
30.
EnsmengerN. (2010). The computer boys take over: Computers, programmers, and the politics of technical expertise. MIT Press.
31.
ErtmerP. A.NewbyT. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24, 1–24.
32.
FarkasG.GrobeR.SheehanD.ShuanY. (1990). Cultural resources and school success: Gender, ethnicity, and poverty groups within an urban school district. American Sociological Review, 55(12-42).
33.
FlavellJ. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911.
34.
FraillonJ.AinleyJ.SchulzW.DuckworthD.FriedmanT. (2019). International Computer and Information Literacy Study 2018 Assessment Framework. https://research.acer.edu.au/ict_literacy/14
35.
GobetF. (2016). Understanding expertise: A multi-disciplinary approach. Palgrave.
36.
GoldinC.KatzL. F. (2008). The race between technology and education. Harvard University Press.
37.
GoosM.ManningA.SalomonsA. (2014). Explaining job polarization: Routine-biased technological change and offshoring. The American Economic Review, 104(8), 2509–2526.
38.
HanushekE.SchwerdtG.WiederholdS.WoessmannL. (2015). Returns to skills around the world: Evidence from PIAAC. European Economic Review, 73, 103–130.
39.
HargittaiE. (2005). Survey measures of web-oriented digital literacy. Social Science Computer Review, 23(3), 371–379.
40.
HargittaiE.HsiehY. P. (2012). Succinct survey measures of web-use skills. Social Science Computer Review, 30, 95–107.
41.
HobbsR.FrostR. (2003). Measuring the acquisition of media-literacy skills. Reading Research Quarterly, 38(3), 330–355.
42.
HuL. T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
43.
IrizarryY. (2015). Selling students short: Racial differences in teachers’ evaluations of high, average, and low performing students. Social Science Research, 52, 522–538.
44.
JenkinsH.ClintonK.PurushotmaR.RobinsonA. J.WeigelM. (2006). Confronting the challenges of participatory culture: Media education for the 21st century. The MacArthur Foundation.
45.
JohnO. P.RobinsR. W. (1994). Accuracy and bias in self-perception: Individual differences in self-enhancement and the role of narcissism. Journal of Personality and Social Psychology, 66(1), 206–219.
46.
KennyD. A.KaniskanB.McCoachD. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507.
47.
KingstonN. M.KramerL. B. (2013). High-stakes test construction and test use. In LittleT. D. (Ed.), The Oxford handbook of quantitative methods in psychology (Vol. 1, pp. 189–205). Oxford University Press.
48.
KochA.AlvesH.KrugerT.UnkelbachC. (2016). A general valence asymmetry in similarity: Good is more alike than bad. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(8), 1171–1192.
LanceC. E.ButtsM. M.MichelsL. C. (2006). The sources of four commonly reported cutoff criteria what did they really say?Organizational Research Methods, 9(2), 202–220.
51.
LeeJ.PaekI. (2014). In search of the optimal number of response categories in a rating scale. Journal of Psychoeducational Assessment, 32(7), 663–673.
52.
LiuY.GruskyD. B. (2013). The payoff to skill in the third industrial revolution. American Journal of Sociology, 118(5), 1330–1374.
53.
MalteseA. V.TaiR. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907.
54.
MargolisJ.EstrellaR.GoodeJ.Jellison HolmeJ.NaoK. (2008). Stuck in the shallow end. MIT Press.
55.
MargolisJ.FisherA. (2002). Unlocking the clubhouse: Women in computing. MIT Press.
56.
MauW.-C. (2003). Factors that influence persistence in science and engineering career aspirations. Career Development Quarterly, 51(3), 234–243.
57.
MichaelsG.NatrajA.Van ReenenJ. (2014). Has ICT polarized skill demand? Evidence from eleven countries over 25 years. Review of Economics and Statistics, 96(1), 60–77.
58.
MillerD. I.EaglyA. H.LinnM. C. (2015). Women’s representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology, 107(3), 631–644.
59.
MisaT. J. (Ed.) (2010). Gender codes: Why women are leaving computing. John Wiley & Sons.
60.
MorganS. L.GelbgiserD.WeedenK. A. (2013). Feeding the pipeline: Gender, occupational plans, and college major selection. Social Science Research, 42(4), 989–1005.
National Center for Education Statistics. (2019). Program for the International Assessment of Adult Competencies (PIAAC)—Sample problem-solving in technology-rich environments items. https://nces.ed.gov/surveys/piaac/sample_pstre.asp
63.
National Research Council. (2007). Rising above the gathering storm: Energizing and employing America for a brighter economic future. The National Academies Press.
64.
New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66, 60–92.
65.
NunnallyJ. C.BernsteinI. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
66.
PainoM.RenzulliL. A. (2013). Digital dimension of cultural capital: The (in)visible advantages for students who exhibit computer skills. Sociology of Education, 86(2), 124–138.
PintrichP. R.De GrootE. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40.
70.
PintrichP. R.SmithD. A. F.GarciaT.MckeachieW. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.
71.
PodsakoffP. M.MacKenzieS. B.PodsakoffN. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
72.
PuckettC. (2015). Technological change, digital adaptability, and social inequality. Northwestern University.
PuckettC.GravelB. (2020). Institutional ambiguity and de facto tracking in STEM. Teachers College Record, 122(11).
75.
RafalowM. H. (2018). Disciplining play: Digital youth culture as capital at school. American Journal of Sociology, 123(5), 1416–1452.
76.
ReadyD. C.WrightD. L. (2011). Accuracy and inaccuracy in teachers’ perceptions of young children’s cognitive abilities: The role of child background and classroom context. American Educational Research Journal, 48(2), 335–360.
77.
RidgewayC. L. (2011). Framed by gender: How gender inequality persists in the modern world. Oxford University Press.
78.
RogoffB. (2003). The cultural nature of human development. Oxford University Press.
79.
RosenbaumJ. (2001). Beyond college for all. Russell Sage Foundation.
80.
SatorraA.BentlerP. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248.
81.
SchoenbachR.BraungerJ.GreenleafC.LitmanC. (2003). Apprenticing adolescents to reading in subject-area classrooms. Phi Delta Kappan, 85, 133–138.
82.
SimsC. (2014). From differentiated use to differentiating practices: Negotiating legitimate participation and the production of privileged identities. Information, Communication & Society, 17(6), 670–682.
83.
Solís SalazarM. (2015). The dilemma of combining positive and negative items in scales. Psicothema, 27(2), 192–199.
84.
StraussM. E.SmithG. T. (2009). Construct validity: Advances in theory and methodology. Annual Review of Clinical Psychology, 5, 1–25.
85.
StreinerD. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99–103.
86.
TaiR. H.LiuC. Q.MalteseA. V.FanX. (2006). Planning early for careers in science. Science, 312(5777), 1143–1144.
van DeursenA. J. A. M.HelsperE. J. (2015). The third-level digital divide: Who benefits most from being online? In RobinsonL.CottenS. R.SchulzJ.HaleT. M.WilliamsA. (Eds.), Communication and information technologies annual studies in media and communications (Vol. 10, pp. 29–52). Emerald Group Publishing.
90.
van DeursenA. J. A. M.HelsperE. J.EynonR. (2016). Development and validation of the internet skills scale (ISS). Information, Communication and Society, 19(6), 804–823.
91.
van DeursenA. J. A. M.van DijkJ. A. G. M. (2011). Internet skills and the digital divide. New Media & Society, 13(6), 893–911.
92.
VogtD. S.KingD. W.KingL. A. (2004). Focus groups in psychological assessment: Enhancing content validity by consulting members of the target population. Psychological Assessment, 16(3), 231–243.
93.
VygotskyL. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
94.
WangX. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121.
95.
WarschauerM. (2003). Technology and social inclusion: Rethinking the digital divide. MIT Press.
96.
WestonR.GoreP. A.Jr (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751.
97.
WoodA. M.TaylorP. J.JosephS. (2010). Does the CES-D measure a continuum from depression to happiness? Comparing substantive and artifactual models. Psychiatry Research, 177(1–2), 120–123.
98.
XieY.FangM.ShaumanK. (2015). STEM education. Annual Review of Sociology, 41, 331–357.
99.
XieY.ShaumanK. (2003). Women in science: Career processes and outcomes. Harvard University Press.
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