Student Mobility Exploring the Impacts of Frequent Moves on Achievement: Summary of a Workshop (2010) / Chapter Skim
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2 Which Children Are Most Affected by Mobility?
2 Which Children Are Most Affected by Mobility?
Pages 9-32
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RESEARCH OVERVIEW Arthur Reynolds provided a synthesis of the research on the effects of mobility on educational progress. He began with data from the National Assessment of Educational Progress (NAEP)
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The 16 studies Reynolds identified measured nonstructural school moves across grades K-12. Each had measured premobility achieve ment levels and also included a full set of control variables, and each provided measures of reading and mathematics achievement as well as school dropout.
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Looking at just the impact on dropout rates, Reynolds found that the effects of mobility varied somewhat with its timing: both early mobility and mobility during high school had the greatest impact. The studies varied in the magnitude of the impact they found.
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One participant observed that it might be possible to use the literature on the predictors of dropping out -- such as disruptive or aggressive behavior or problems with self-regulation early in development -- as a starting point for researching the mechanisms that connect mobility to dropping out. Reynolds concurred, noting that other research suggests interactions among mobility, involvement with the juvenile justice system, and lower academic achievement, but these processes and relationships are not well understood.
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2. What is the broad nature of school moves (during the school year, between school years, structural reasons, family reasons)
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ended up behind their peers in literacy skills, even when their prior achievement levels are taken into account; this effect is strongest for lowSES children. While there is no overall negative impact for mathematics achievement, there is a negative effect for low-SES children that lingers TABLE 2-1 School Mobility at the National Level Frequency of School Change Percentage During kindergarten (n = 17,745)
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Looking at just the number of moves children made during this time period, Lee and Burkam found that while a single move had no impact, two or more moves were associated with somewhat lower achievement in third grade -- and again the effects were stronger for some children, such as those receiving special education services. Reflecting on what they had found, Lee observed that the effects of mobility seem to be small, but the available data do not yet provide a complete picture of school moves in the early primary grades.
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, Hannaway reported.3 She suggested two factors that offer at least a partial explanation: a dramatic increase in the immigrant population (274 percent between 1990 and 2000) and an increase in options for public schooling, such as charter schools and school choice programs.4 Using state administrative data that included information on free and reduced-price lunch status, ethnicity, sex, English language proficiency, special education status, and achieve ment, Hannaway developed a picture of mobility among elementary and middle school students in the state and its effects on their academic achievement.
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Charter schools saw an increase of more than 30 percent, and, in general, the schools with greater percentages of minority students and students eligible for free and reduced-price lunch saw higher turnover rates. Mobility rates varied significantly for North Carolina students in dif ferent subgroups, as shown in Table 2-2.
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, the population is 43.3 percent white, 23.3 percent black, 26.2 percent Hispanic, 2.4 percent Asian, 0.2 percent American Indian, and 4.6 percent multiracial. Using administrative data, Dukes was able to capture information about all the school moves children enrolled in public schools in the 2007-2008 year had made since the start of kindergarten, regardless of the time of year of the move (he excluded structural moves)
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Number of Moves 1 2 3 4 5 Cross 0.00 District Movers All Movers –0.05 –0.10 Within District Movers –0.15 Cumulative Effect FRPL Eligible –0.20 Black –0.25 Hispanic –0.30 FIGURE 2-3 The effect of multiple moves on mathematics achievement in North Carolina. NOTE: FRPL = free and reduced-price lunch.
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Than Once (%) White 48.47 37.65 32.66 Black 19.39 26.98 33.52 Hispanic 25.10 28.54 27.29 Asian 2.57 2.25 1.29 American Indian 0.29 0.29 0.26 Multiracial 4.19 4.29 4.97 Eligible for free and reduced- 48 33 20 price lunch Students with disabilities 47 32 20 English language learners 49 34 17 SOURCE: Dukes (2009)
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Dukes observed that, as with other data sets, the interesting patterns emerge in the analyses of specific groups of children. New York City Amy Ellen Schwartz presented similar results for students in New York City public schools.
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Another participant pointed out that high mobility rates within a school tend to foster more mobility, because "the higher the mobility in the school, the more likely it is that there will be a seat open at any given time for an incoming student." Thus a district may be more likely to place an incoming mobile student who arrives in the middle of the year in a school with more frequent openings than in a stable school. For Schwartz, these results highlight the importance of using district policy both to minimize the number of structural moves children make (by, for example, structuring schools to cover grades K through 8)
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. This system was designed to support research that identifies risk and protective factors among cohorts of children from birth to age 21 using administrative data from multiple public agencies, including the School District of Philadelphia, the Department of Child Health and Welfare, and homeless shelters.
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Fantuzzo and his colleagues used KIDS data, including administra tive records from multiple city agencies together with school outcome data, to study the prevalence and impact of publicly monitored risks. They found that the rate of homelessness among these children, at 9.2 percent, was three times higher than the national average for elementaryage students.
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The next step, Fantuzzo explained, was to look in greater detail at the experiences of the mobile and homeless children in the cohort, particularly the co-occurrence of other publicly monitored risk factors. Table 2-5 shows the levels of risk for four categories among the third grade cohort (school mobile only, homeless only, both, or neither)
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Yet, Masten pointed out, there is surprising variation even in these groups -- with some children with multiple risk factors faring very well, and some highly mobile children scoring very high on achievement tests. Rural Poverty Fantuzzo focused on homelessness in an urban setting, but disadvantaged children in rural or small-town settings may have somewhat different experiences.
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Looking at a particular district in which this cycle had developed, Schafft described a student population of which 46 percent were eligible for free and reduced-price lunch, and in which an average of 1.6 middle school students per day entered or left their schools, for a turnover rate of 29 percent. Approximately half the moves took place within the district, and the median distance between students' new and old schools was ↑Varying degree of out migration of residents, Long-term especially younger and economic ↓ Population more educated decline in rural community Devalued housing Low-income Economic and ↑ Conversion of families remain housing insecurity single-family under circumstances leads to frequent homes into of increased short-distance multiple rental economic insecurity residential moves units FIGURE 2-5 The cycle of poverty, residential mobility, and community disadvantage.
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Second, Schafft has found that transience is not simply an academic issue, but is closely linked to broader questions about family and community disadvantage. Thus he advocates multidisciplinary and multimethod research, applied in the pursuit of questions that look beyond the school, as the best analytic approach to the problems faced by highly mobile families.
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DISCUSSION Looking across the data about student mobility in different places and circumstances, participants had various detailed questions about the col 7 Details about the study can be found at http://www.icpsr.umich.edu/PHDCN/.
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0 75 White - stayers 70 White - moved w/in Chicago 65 White - moved outside Chicago 60 Black - stayers 55 Black - moved w/in Chicago 50 Black - moved outside Chicago Median Income 45 (in thousands of dollars) Latino - stayers 40 Latino - moved w/in Chicago 35 Latino - moved outside Chicago 30 1995 1997/1998 2002 Survey Wave FIGURE 2-6 Unadjusted trajectories of neighborhood median income among stayers, movers within the city, and movers out of Chicago, by race and ethnicity.
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• omeless children appear to be at particularly high risk, not only H because they are highly mobile but also because they have numerous other risk factors, such as family disruption, prolonged economic distress, and lack of social, community, and other resources. • ore negative effects may be associated with moves within a M school district than moves between districts, and with moves that take place during the school year, compared with those that occur between school years.
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