Tuesday, March 18, 2014

Face Validity and the New Ed School Rankings

For those of you outside academia, "face validity" is a fancy term academics use that simply means that something makes sense upon first glance (or "on face").  US News and World Report released their latest grad school rankings last week, and one thing I notice is the lack of face validity.

First, I should note that I'm a Vanderbilt alum and Vanderbilt dropped to #2 in the education school rankings after ranking #1 for five consecutive years.  I hesitated to write this post lest anybody think it's simply sour grapes.  Or maybe an attempt to draw attention to the fact that Vanderbilt dropped in the rankings as soon as I left . . .

In all seriousness, I really don't care all that much where Vanderbilt ranks.  I don't even know what it actually means to be the top-ranked school of education.  What matters most?  Outcomes of students?  Research of faculty?  Selectivity?  The current rankings measure the latter two but not the first (my biggest criticism of them would be that they make virtually no attempt to measure the education students actually receive).  We could construct the rankings 100 different ways that would all make some amount of sense, so it's a bit ridiculous that the USNWR rankings draw so much attention (and yet, here I am, helping them draw even more attention).

Despite the fact that I claim I have no idea what it means to be the top-ranked school of education, if pressed I'd have to posit that the top four, in some order, are Vanderbilt (Peabody), Columbia (Teacher's College), Stanford, and Harvard.  I think those four have the most history and prestige, but I could be wrong.  By one measure, they have the most recognizable scholars (Stanford has 21; Harvard 19; Columbia 12; and Vanderbilt 11 of the 200 scholars ranked) -- so I don't think I'm totally off-base here.  So it's interesting to me that those schools rank second, third, fourth, and eighth.

Another measure would be to look at which schools have the top-ranked programs.  USNWR ranks the top 20 or so programs in 10 different fields, though they do so solely based on nominations by Deans.  I'd consider these rankings "face validity" because they're simply asking knowledgeable people what looks like it would make sense to them rather than doing any sort of comprehensive multivariate analysis. By clicking on each school's profile, one can see how many programs that school has that made the cut. Below are the top 25 ranked schools of education and the number of fields in which they were ranked:

2.) Vanderbilt: 9
5.) Wisconsin: 9
8.) Columbia: 9
15.) Michigan St.: 8
16.) Ohio St.: 8
4.) Stanford: 7
8.) Columbia: 7
8.) Michigan: 7
11.) UCLA: 7
7.) Washington: 6
10.) Texas: 6
22.) Virginia: 6
25.) Indiana: 6
3.) Harvard: 5
14.) UC-Berkeley: 5
5.) Penn: 4
20.) NYU: 4
20.) Minnesota: 4
18.) USC: 3
13.) Oregon: 1
17.) Kansas: 1
22.) Pitt: 1
24.) BC: 1
1.) Johns Hopkins: 0
11.) Northwestern: 0
18.) Arizona St.: 0

A few things stick out here:

-There looks to be only a mild correlation between a school's overall rank and the number of top programs it has.

-A number of schools have quite a few top-ranked programs but are outside the top 10 -- Michigan State, Ohio State, Virginia, and Indiana are particularly notable.

-Meanwhile, Northwestern and Johns Hopkins have exactly zero top-ranked programs and yet rank above all of those programs.

-Yes, you read that right: Johns Hopkins -- the new #1 School of Education -- has exactly zero programs ranked among the top 20 or so in the country.  Now, I freely admit that I have absolutely no idea whether or not Johns Hopkins has the best faculty, students, research, or anything else we try to measure, but that's pretty striking.


So, something doesn't make sense here.  How could Deans perceive that schools have a slew or dearth of top programs while the overall rankings indicate that the school as a whole is actually merely really good or the cream of the crop?

One possibility is that the Deans' perceptions are wrong and that the data collected by USNWR are a better indicator of quality.  Another possibility is the opposite -- that the rankings are a sham and that we should listen to the Deans.  A third possibility is that the program rankings are misleading in some way (e.g. some fields are more important than others, important fields are missed, the narrow margins are insignificant, or that a few top-5 programs is better than a bunch of top-20 programs).

If it's number one or two then I'd argue that either the overall rankings or the program rankings are lacking in face validity.  In reality, it's probably some mixture of all of the above.  But if memory serves, I believe that Texas, Oregon, and Johns Hopkins have been second one year and 10th or lower another year just in the past five years.  It's possible that school quality changes that fast, but it seems rather unlikely.

Another way of looking at the rankings would be to look at programs that rank at the very top of their fields.  If we look at the number of programs that rank in the top 10/top 5/#1 in their field, we get a different picture for each one and from above:

Wisconsin: 8/7/1
Vanderbilt: 8/5/2
Michigan State: 7/4/2
Columbia: 6/5/0
Michigan: 6/5/0
Ohio State: 6/1/0
Stanford: 5/5/2
Virginia: 4/1/0
Harvard: 3/2/0
UCLA: 3/1/1
Texas: 3/0/0
Indiana: 3/0/0
UC-Berkeley: 2/0/0
Washington: 2/0/0
Penn: 2/0/0
Minnesota: 2/0/0
Oregon: 1/1/0
Kansas: 1/1/0
USC: 1/1/0

When we look at this way, we notice that four schools ranked in the top 10 didn't have a single program ranked in the top five in its field (#1 Johns Hopkins, #5 Penn, #7 Washington, and #10 Texas).

Another thing you may notice is that there are only eight #1 rankings in 10 fields.  That's because the schools ranked first in student counseling and personnel services (Maryland) and technical/vocation education (Penn State) didn't make the top 25.  Which may confirm my earlier hypothesis that some fields are viewed as more important than others.  Or not.  If we look at the number of programs ranked among the best in their field or the top 10/5/#1 in their field, we find a few schools outside the top top 25 that dwarf most of the top 25 schools:

33.) Penn State: 9/4/2/1
33.) Georgia: 9/6/3/0
26.) Maryland: 7/2/1/1
26.) Illinois: 6/2/1/0

All of which leads to a whole lot of confusion.  I'm not really sure what the rankings are measuring to begin with, but it sure seems odd that their specialty rankings would be so misaligned with their comprehensive rankings.  I'd bet that a lot of the Deans polled for the specialty rankings (and academics who think like they do) probably think the overall rankings are lacking in face validity.

Thursday, March 13, 2014

How Does Poverty Affect Academic Performance? Part 2: Theory

Today I continue the series examining the ways in which poverty influences academic performance.  Part 1 explored the achievement gap and some trends and causes and future parts will discuss social factors and environmental conditions experienced by families living in poverty that may also impact academic performance.  In other words, what, exactly, is it about living in poverty that results in dramatically lower achievement and attainment?

Before we can answer that, we need to first understand why poverty would matter.  Below, I briefly discuss some theory and literature that points us toward some possibilities.

Neighborhood Effects
Though a wide array of social conditions influence children’s academic performance, researchers and policymakers have focused more on the links between housing and neighborhoods and educational outcomes; from the Gautreaux decision to the MTO experiment and beyond. The results of this strand of policy and research have run a wide gamut. A recent review of the literature [1] concludes that:

Housing programs have successfully helped poor parents move to safer and less disadvantaged communities and, in some cases, less segregated neighborhoods . . . Despite the ability for some of these programs to bring about context changes, it appears much more difficult to improve the educational outcomes of children. Early Gautreaux results suggested large benefits for children moving to the suburbs, but . . . more recent MTO research concludes that neighborhood change is not enough to substantially improve schooling quality or educational outcomes (p. 478).

In short, while there may be sufficient reason to believe that housing policy can positively and significantly impact the academic performance of some of the poorest Americans, there is as of yet no conclusive evidence that we know how to do this on a consistent basis.

One reason behind the contradicting findings may be the lack of a clear consensus on a theoretical framework outlining the relationships between potential levers of housing policy and academic performance. In their introduction to the Neighborhood Poverty series, Gephart and Brooks-Gunn [2] write that

Multiple theoretical perspectives, fragmented by discipline and often by method, provide partial, potentially complementary (but sometimes conflicting) guidance about the characteristics of neighborhoods that may affect the development of children, youth, and families, and about the mechanisms through which such characteristics affect families and individuals. (p. xvii)

Although the field has come a long way in the 17 years since, the problem they identify has never been fully resolved. Why would these policies have led to changes in children’s educational performance? While the theory supporting such a relationship has been well-developed in some areas, it remains highly fragmented – particularly across different disciplines. In other words, while theoretical models regarding parts of the story abound, we do not yet have an all-encompassing theoretical framework. Jencks and Mayer [3] divide theories relating neighborhoods to child development into three groups: epidemic models, collective socialization models, and institutional models.

Epidemic models
Epidemic models theorize that neighborhood characteristics spread much like disease spreads – from person to person. For example, one person decides to use drugs, then another, then another, and so forth (or, perhaps, read Shakespeare). In this way, peer norms are the main driver of individual behavior; those raised in neighborhoods where going to college is the norm are more likely to attend college, and those raised in neighborhoods where dropping out of high school is the norm are more likely to drop out.

Collective socialization models
Collective socialization models hold that values are derived from adults who live in the neighborhood. Adults both serve as examples to which children should aspire and enforce rules within the neighborhood. These models would theorize that people who grow up in neighborhoods where drug dealers are idolized would be more likely to deal drugs when they come of age while those who grow up in neighborhoods full of shopkeepers would be more likely to open their own store and people who grow up around college graduates would be more likely to attend college themselves.

Institutional Models
Institutional models underline the importance of adults from outside of the neighborhood; particularly those in positions of authority (teachers, police, etc.). Theories under this umbrella posit that children from poorer neighborhoods interact with different outside authority figures and/or are treated differently by outside authority figures. Children treated with more respect and concern by these authority figures would then stand a better chance of graduating from high school or avoiding jail.

Discussion
Theories under all these umbrellas overlap with one another and often predict similar outcomes (for example, that students in poorer neighborhoods will be less likely to graduate). Both because of that fact and because they all have empirical backing, we should consider all three when predicting and studying how social policy might impact academic performance. That those in lower classes live in worse housing is not seriously questioned. Indeed, the local home values seem to explain differences in school-wide achievement that other background variables do not [4]. This may be due in part to those with means opting to move into neighborhoods zoned for better schools, but is also likely the result of a more complicated relationship between homes and neighborhoods and various behaviors and actions. For example, it has been theorized that perception of disorder in one’s surroundings leads to other negative behaviors [5, 6]. Hastings [7] posits that neighborhood effects are compounded by a vicious cycle wherein poorer neighborhoods need more services and the situation is exacerbated when government officials fail to recognize, and subsequently act on, this condition.

Stress Theory
Based on developmental research, Shonkoff and Phillips [8] add stress theory as a fourth group of neighborhood effects theories, though it is more often cited by health researchers. Stress Theory posits that stressors more common in poorer neighborhoods (which might range from crime to lead paint) have deleterious effects on children. These negative effects add up to create stress and inhibit development. A recent advance in the study of stress was the creation of the Adverse Childhood Experiences (ACE) survey [9], which measures accumulated stress through exposure to various stressors in childhood and strongly predicts later health and academic outcomes. Stress theory would predict that children exposed to more negative experiences would be more distracted, less focused, more stressed, and lower achieving in school.

Ecological Systems Theory
Widely used by those who research both neighborhoods and family/home conditions and their effect on child development, ecological systems theory [10] and the bioecological model [11] theorize that children are affected by people and institutions in five different nested levels: immediate friends, family and surroundings (the microsystem); the relationships between these immediate surroundings (the mesosystem); the outside experiences of immediate friends and family (the exosystem); the cultural context in which one lives (the macrosystem); and the historical context in which one lives (the chronosystem). Each system influences each child differently and to different extents depending on both the degree of exposure to, and context of, each. Students who experience problems in their immediate surroundings (e.g. family conflict), relationships between these different groups (e.g. a poor relationship between their church and parents), extended social systems (e.g. a parent working in a stressful job), cultural context (e.g. high rates of poverty and unemployment), and/or historical context (e.g. racial discrimination) would be expected to perform worse in school.

Resources
Resources likely matter both directly and indirectly. In the most direct sense, more money enables families to purchase more goods to aid their children’s learning. For example, a recent study using two national databases found that families who earn more money or begin earning more money spend more on physical items like books and toys in addition to enrichment activities like sports and art classes [12]. More indirectly, economists and psychologists argue that a lack of resources diverts attention away from other tasks. For example, focusing attention on finding adequate food or water decreases the amount of attention a parent can focus on their child’s physical health or the homework due the next day [13]. The former predicts that a child with more stimulation at home and more activities outside the home will perform better in school because he/she had more learning experiences; the latter predicts that a child whose parents have to spend less time and energy ensuring basic needs are met will perform better in school because he/she received more attention and care.

Non-Cognitive Factors
Recent writings have focused the attention of researchers [14] and the public [15] on the non-cognitive skills of students, with some evidence that they may be stronger predictors of school success than cognitive skills [see, for example: 16]. Some researchers group self-control together with attention as psychological effects of poverty [17] since the stresses encountered by those living in poverty can deplete both over time [18], but I instead include self-control with non-cognitive factors. Tough lists grit, self-control, zest, social intelligence, gratitude, optimism, and curiosity as the seven factors “especially likely to predict life satisfaction and high achievement” (p. 76). Students whose environments foster development of these skills and traits would be more likely to earn higher grades, score higher on tests, and graduate from high school and college.

Culture of Poverty
Popularized by Oscar Lewis [19] and “The Moynihan Report” [20], the “culture of poverty” theory essentially argued that people living in poverty had developed a destructive culture that perpetuated the cycle of poverty. Lewis later clarified [21] that he believed that:

The people in the culture of poverty have a strong feeling of marginality, of helplessness, of dependency, of not belonging. They are like aliens in their own country, convinced that the existing institutions do not serve their interests and needs. Along with this feeling of powerlessness is a widespread feeling of inferiority, of personal unworthiness . . . People with a culture of poverty have very little sense of history. They are a marginal people who know only their own troubles, their own local conditions, their own neighborhood, their own way of life. Usually, they have neither the knowledge, the vision nor the ideology to see the similarities between their problems and those of others like themselves else in the world (p. 21).

Lewis continues on to argue that although he believes those living in poverty had changed their culture, that these changes were not all negative. He argues, for example, that a focus on the more immediate present rather than long-term planning could lead to a more joyful and carefree life.

Though largely discredited and ignored in recent decades [22], the “culture of poverty” hypothesis has made a recent comeback among scholars [23] – but this time with a different meaning. Rather than focusing on the shortcomings of those living in poverty, the focus has shifted to examining how living in poverty affects the culture of families and neighborhoods. In this sense, Lewis may have been right that those living in poverty often feel outcast, isolated, and hopeless – but scholars now see these as an outcome rather than cause of poverty. Scholars investigating the relationship between culture and poverty would expect students who are more isolated, feel less hope for the future, and engage in less long-run planning to perform worse in school.

Conclusion
The theories discussed above all influence the research that I'll discuss in future posts and make appearances in a wide range of articles and topics. Indeed, researchers from different fields and disciplines often cite different theories in order to support similar arguments. Collectively, they predict that children with more stress, fewer resources, strained relationships, more chaotic surroundings, and worse role models will earn lower grades, perform worse on tests, drop out more frequently, and earn fewer degrees.  The next posts will explore some more specific and tangible ways in which students living in poverty experience these types of factors and conditions and how those experiences subsequently affect academic performance.



References
  1. DeLuca, S. and E. Dayton, Switching Social Contexts: The Effects of Housing Mobility and School Choice Programs on Youth Outcomes. Annual Review of Sociology, 2009. 35(1): p. 457-491.
  2. Gephart, M.A. and J. Brooks-Gunn, Introduction, in Neighborhood Poverty: Context and Consequences for Children, J. Brooks-Gunn, G.J. Duncan, and J.L. Aber, Editors. 1997, Russell Sage: New York. p. xiii-xxii.
  3. Jencks, C. and S.E. Mayer, The Social Consequences of Growing Up in a Poor Neighborhood, in Inner-City Poverty in the United States, Committee on National Urban Policy and National Research Council, Editors. 1990, National Academies Press: Washington, DC.
  4. Kane, T.J., D.O. Staiger, and G. Samms, School Accountability Ratings and Housing Values. Brookings-Wharton Papers on Urban Affairs, 2003(4): p. 83-137.
  5. Franzini, L., et al., Perceptions of disorder: Contributions of neighborhood characteristics to subjective perceptions of disorder. Journal of Environmental Psychology, 2008. 28(1): p. 83-93
  6. Sampson, R.J. and S.W. Raudenbush, Seeing Disorder: Neighborhood Stigma and the Social Construction of "Broken Windows". Social Psychology Quarterly, 2004. 67(4): p. 319-342.
  7. Hastings, A., Neighbourhood Environmental Services and Neighbourhood 'Effects': Exploring the Role of Urban Services in Intensifying Neighbourhood Problems. Housing Studies, 2009. 24(4): p. 503-524.
  8. Shonkoff, J.P. and D.A. Phillips, From Neurons to Neighborhoods: The Science of Early Childhood Development. 2000, Washington, DC: National Academy Press.
  9. Felitti, V.J., The relationship of adverse childhood experiences to adult health: Turning gold into lead. Zeitschrift fur Psychosomatische Medizin und Psychotherapie, 2002. 48(4): p. 359-369.
  10. Bronfenbrenner, U., The ecology of human development: Experiments by nature and design. 1979: Harvard Univ Press.
  11. Bronfenbrenner, U. and P.A. Morris, The ecology of developmental processes, in Handbook of Child Psychology: Volume 1: Theoretical Models of Human Development, R.M. Lerner, Editor. 1998, John Wiley & Sons Inc: Hoboken, NJ. p. 993-1028.
  12. Kaushal, N., K. Magnuson, and J. Waldfogel, How Is Family Income Related to Investments in Children's Learning?, in Whither Opportunity? Rising Inequality, Schools, and Children's Life Chances, G.J. Duncan and R.J. Murnane, Editors. 2011, Russell Sage Foundation: New York. p. 187-205.
  13. Banerjee, A.V. and S. Mullainathan, Limited Attention and Income Distribution. The American Economic Review, 2008. 98(2): p. 489-493.
  14. Heckman, J.J., Policies to foster human capital. Research in Economics, 2000. 54(1): p. 3-56.
  15. Tough, P., How children succeed: Grit, curiosity, and the hidden power of character. 2012, Boston: Houghton Mifflin Harcourt.
  16. Duckworth, A.L. and M.E. Seligman, Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 2005. 16(12): p. 939-944.
  17. Mullainathan, S., The Psychology of Poverty. Focus, 2011. 28(1): p. 19-22.
  18. Spears, D., Economic Decision-Making in Poverty Depletes Behavioral Control. The BE Journal of Economic Analysis & Policy, 2011. 11(1).
  19. Lewis, O., The culture of poverty. Scientific American, 1966. 215(4): p. 19 - 25.
  20. Office of Policy Planning and Research, The Negro family: The case for national action. 1965, United States Department of Labor: Washington, DC.
  21. Lewis, O., The culture of poverty, in Poor Americans: How The White Poor Live, M. Pilisuk and P. Pilisuk, Editors. 1971, Transaction, Inc.: New York. p. 20-26.
  22. Small, M.L., D.J. Harding, and M. Lamont, Reconsidering Culture and Poverty. The ANNALS of the American Academy of Political and Social Science, 2010. 629(1): p. 6-27.
  23. Cohen, P., ‘Culture of Poverty’ Makes a Comeback, in New York Times. 2010.

Monday, March 10, 2014

How Does Poverty Influence Academic Performance? Part 1 in a Series

Today marks the start of a new multi-part series on poverty and academic performance.  I expect to post two or three new parts most weeks in the coming months (there will be at least 20 parts).  As I explained last week, the goal of the series is to start to figure out exactly what it is about living in poverty that so dramatically affects students' achievement and attainment and to explore possible policy interventions that could mitigate poverty's impacts.


Background

Almost twenty years ago, researchers first focused on a large (as much as one standard deviation) gap between Black and White students, which they termed the "Black-White Test Score Gap" (Jencks and Phillips, 1998).  The consensus term soon became “achievement gap” (Meyers, 2012), which has been used more widely to refer to both the gaps in achievement and attainment between different races and ethnicities and between those of different classes or socioeconomic backgrounds (Murphy, 2009). This series will focus on the gaps between different classes.  I'll also explore "academic performance" broadly defined rather than just test scores.

Academic performance differs widely between students of different social classes across a long list of indicators, including (but not limited to): standardized test scores, grades, graduation rates, college entrance exams, college matriculation, college graduation, and completion of a graduate degree (Meyers, 2009).

Though I'll be focusing on the gaps between classes, it's worth nothing that while the Black-White test-score gap narrowed steadily during the 1970s and 1980s . . . but that progress has essentially stalled during the past quarter-century. Why?  Potential explanations include changes in families (Berends, Lucas, and Pe├▒aloza, 2008), the re-segregation of schools (Condron, 2009) and/or a myriad of other factors (Covay, 2010). Neal (2005) argues that we do not really know why progress stalled, but notes that most of the narrowing during the previous two decades occurred because of gains by Blacks in the middle and at the top of the distribution while those at the bottom largely remained at the same level. The timing of the halt in progress corresponds roughly with both the reversal of the desegregation movement over the past 40 years (Berends and Penaloza, 2010; Vigdor and Ludwig, 2008) and with widening economic inequality between both Blacks and Whites and rich and poor (Magnuson and Waldfogel, 2008; Mayer, 2001).

Given the recent growth in wealth and income inequality, perhaps it should not surprise us that recent evidence indicates that socioeconomic gaps have actually widened during that time.  Reardon (2011) finds that the test-score gap between students from high- and low-income families (90th vs. 10th percentile) has grown by 30-40% over the past 25 years and is now approximately twice as large as the black-white achievement gap, the opposite of 50 years ago (see first image below).  In a working paper, Bailey and Dynarski (2011) find that the gap in college completion between students from families in the top and bottom income quartile grew by 14 percentage points between children born in the early 1960s and around 1980 (see second image below). And another current working paper finds that the gap in educational attainment between children from high- and low-income households grew by a full half-year between the 1954 and 1987 birth cohorts (Duncan, Kalil, and Ziol-Guest, 2013 -- see third image below).  The stalling out of progress on the Black-White achievement gap and growth in gaps between classes signals massive problems with our current efforts.  But what else could we do?


Source: Reardon (2011)

Source: Bailey & Dynarski (2011)

Source: Duncan, Kalil, & Ziol-Guest (2013)



Causes of the Achievement Gap

Before we can decide what to change, we need to understand why these gaps exist in the first place.  While study after study finds that family income (see, for example: Blau, 1999; Duncan, Brooks-Gunn, and Klebanov, 1994; Sirin, 2005) and wealth are significant predictors of academic achievement (see, for example: Orr, 2003; Shanks, 2007; Yeung and Conley, 2008), the causes of these differences are less clear.

What is clear, however, is that these differences in achievement are driven largely by differences outside of school. Consensus on this point has grown since the “Coleman Report” (Coleman et al., 1966) found that non-school factors are stronger predictors of the achievement of a given student than in-school factors, a finding that has been replicated countless times over the past forty plus years (see, for example: Alexander, Riordan, Fennessey, and Pallas, 1982; Hauser, 1972; Sirin, 2005). The current consensus is that home background factors predict about two-thirds of achievement and school factors predict about one-third (Rothstein, 2004). Indeed, if there is anything upon which education researchers agree it is that student achievement is influenced more by non-school factors than in-school factors – and the evidence is overwhelming.

The relative importance of non-school factors can be seen early on; when students begin school, a large gap in achievement already exists (Lee and Burkam, 2002). Racial gaps are non-existent in infants, but observable in toddlers (Fryer and Levitt, 2013), so the causes are almost certainly environmental rather than genetic.

Not only is the achievement gap present when students begin school, it grows during summer breaks (Borman and Benson, 2010; Downey, von Hippel, and Broh, 2004; Entwisle and Alexander, 1992; Heyns, 1978). The growing gap between high- and low-SES kids during summer months eventually results in high schoolers who are more likely to be assigned to different tracks despite similar ability earlier in life and decreases the odds of low-SES students both graduating from high school and enrolling in four-year colleges (Alexander, Entwisle, and Olson, 2007).

Given that the gap forms before school and widens during breaks from school, our best estimate is that about three-quarters of the gap is formed outside of school and about one-quarter is formed while students are in school (Murphy, 2009). This makes sense when we consider that kids spend only about 14-15% of their waking hours actually inside of schools from birth through high school*.


Next Steps

If we know that achievement gaps form before school and widen during summers, the next thing we need to know is how students' lives differ during these times.  And which of these differences affect academic performance?

In the next parts, I'll explore why differences in housing and neighborhoods, health and health care, and family and home environment exist, assess the evidence that they affect academic performance, review the theory as to why they would affect academic performance, and discuss the potential of policy to address the problem.




*If a student spends 7 hours per day in school and attends 180 days of school for 13 years, they spend 16,380 total hours in school. Assuming 16 waking hours per day for 18.5 years, the average child would spend 108,114 hours awake from birth through high school. 16,380/108,114 = 15.15%. A more realistic estimate is probably to assume that students attend 12.5 years of school on average for 6.5 hours/day 170 days per year, but sleep 9 hours per day, which would yield an estimate of 13.97%. Some students would spend far more time in school if they sleep longer hours and/or attend schools with longer days/years, while others would spend far fewer hours if they sleep less, attend school less regularly, and/or drop out of school before graduating.


References:

Alexander, K. L., Entwisle, D. R., and Olson, L. S. (2007). Lasting Consequences of the Summer Learning Gap. American Sociological Review, 72, 167-180.

Alexander, K. L., Riordan, C., Fennessey, J., and Pallas, A. M. (1982). Social Background, Academic Resources, and College Graduation: Recent Evidence from the National Longitudinal Survey. American Journal of Education, 90(4), 315-333.

Bailey, M. J., and Dynarski, S. M. (2011). Gains and gaps: Changing inequality in US college entry and completion (Vol. No. 17633): National Bureau of Economic Research. http://www.nber.org/papers/w17633

Berends, M., Lucas, S. R., and Pe├▒aloza, R. V. (2008). How Changes in Families and Schools Are Related to Trends in Black-White Test Scores. Sociology of Education, 81(4), 313-344. doi: 10.1177/003804070808100401

Berends, M., and Penaloza, R. V. (2010). Increasing Racial Isolation and Test Score Gaps in Mathematics: A 30-Year Perspective. Teachers College Record, 112(4), 978-1007.

Blau, D. M. (1999). The Effect of Income on Child Development. Review of Economics and Statistics, 81(2), 261-276. doi: doi:10.1162/003465399558067

Borman, G., and Benson, J. (2010). Family, Neighborhood, and School Settings Across Seasons: When Do Socioeconomic Context and Racial Composition Matter for the Reading Achievement Growth of Young Children? The Teachers College Record, 112(5), 5-6.

Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, F., Mood, A. M., Weinfeld, F. D., and York, R. L. (1966). The Equality of Educational Opportunity Report. Washington, D.C.: U.S. Government Printing Office.

Condron, D. J. (2009). Social Class, School and Non-School Environments, and Black/White Inequalities in Children's Learning. American Sociological Review, 74(5), 685-708. doi: 10.1177/000312240907400501

Covay, E. A. (2010). The Emergence and Persistence of the Black-White Achievement Gap. (Doctoral Thesis), University of Notre Dame.

Downey, D. B., von Hippel, P. T., and Broh, B. A. (2004). Are schools the great equalizer? Cognitive inequality during the summer months and the school year. American Sociological Review, 69, 613 - 635.

Duncan, G. J., Brooks-Gunn, J., and Klebanov, P. K. (1994). Economic Deprivation and Early Childhood Development. Child Development, 65(2), 296-318.

Duncan, G. J., Kalil, A., and Ziol-Guest, K. M. (2013). Increasing Inequality in Parent Incomes and Children’s Completed Schooling. http://www.hks.harvard.edu/inequality/Seminar/Papers/Duncan13.pdf

Entwisle, D. R., and Alexander, K. L. (1992). Summer Setback: Race, Poverty, School Composition, and Mathematics Achievement in the First Two Years of School. American Sociological Review, 57(1), 72-84.

Fryer, R. G., and Levitt, S. D. (2013). Testing for Racial Differences in the Mental Ability of Young Children. The American Economic Review, 103(2), 981-1005. doi: 10.1257/aer.103.2.981

Hauser, R. M. (1972). Socioeconomic Background and Educational Performance. Washington, D.C.: American Sociological Association.

Heyns, B. (1978). Summer learning and the effects of schooling. New York: Academic Press.

Jencks, C., and Phillips, M. (1998). The Black-White Test Score Gap: An Introduction. In C. Jencks and M. Phillips (Eds.), The Black-White Test Score Gap (pp. 1-51). Washington, DC: The Brookings Institute Press.

Lee, V. E., and Burkam, D. T. (2002). Inequality at the starting gate. Washington, DC: Economic Policy Institute. Magnuson, K., and Waldfogel, J. (2008). Steady Gains and Stalled Progress: Inequality and the Black-White Test Score Gap: Russell Sage Foundation.

Mayer, S. E. (2001). How Did the Increase in Economic Inequality between 1970 and 1990 Affect Children's Educational Attainment? American Journal of Sociology, 107(1), 1-32.

Meyers, C. V. (2009). Tracking the Gaps. In J. Murphy (Ed.), The Educator's Handbook for Understanding and Closing Achievement Gaps. Thousand Oaks, CA: Corwin Press.

Meyers, C. V. (2012). The Centralizing Role of Terminology: A Consideration of Achievement Gap, NCLB, and School Turnaround. Peabody Journal of Education, 87(4), 468-484. doi: 10.1080/0161956x.2012.705149

Murphy, J. (2009). The Educator's Handbook for Understanding and Closing Achievement Gaps. Thousand Oaks, CA: Corwin Press.

Neal, D. (2005). Why Has Black-White Skill Convergence Stopped? National Bureau of Economic Research Working Paper Series, No. 11090.

Orr, A. J. (2003). Black-White Differences in Achievement: The Importance of Wealth. Sociology of Education, 76(4), 281-304.

Reardon, S. F. (2011). The Widening Academic Achievement Gap between the Rich and the Poor: New Evidence and Possible Explanations. In R. Murnane and G. Duncan (Eds.), Whither Opportunity? Rising Inequality and the Uncertain Life Chances of Low-Income Children (pp. 91-115). New York: Russell Sage Foundation.

Rothstein, R. (2004). Class and schools: Economic Policy Institute Washington, DC.

Shanks, T. R. W. (2007). The impacts of household wealth on child development. Journal of Poverty, 11(2), 93-116.

Sirin, S. R. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research, 75(3), 417-453.

Vigdor, J. L., and Ludwig, J. (2008). Segregation and the Test Score Gap. In K. Magnuson and J. Waldfogel (Eds.), Steady Gains and Stalled Progress: Inequality and the Black-White Test Score Gap (pp. 181-211). New York: Russell Sage Foundation.

Yeung, W. J., and Conley, D. (2008). Black-White Achievement Gap and Family Wealth. Child Development, 79(2), 303-324. doi: doi:10.1111/j.1467-8624.2007.01127.x

Friday, March 7, 2014

Friday Notes

A few thoughts that occurred to me this week:

-Here's an interesting piece on Teaching students how to combat traumas of poverty on the yoga mat (h/t: Alexander Russo) by PBS earlier this week that relates to my research on stress, poverty, and academics.  I'm certainly not going to stand here and insist that every student learn yoga, but the piece raises a whole lot of interesting questions and important issues.

-Really interesting move by TFA to pilot two programs in which corps members are trained for a year prior to graduation and, separately, supported during years 3-5 of teaching.  I'm not surprised by the move to support current corps members for longer, since they've always been touchy about the attrition rate, but I'm very surprised by the move to train future corps members for longer.  It will be interesting to see whether the additional training improves performance, but perhaps more interesting to see if it improves retention.  I could see it going either way -- teachers feeling like they need to serve longer because they put forth more effort up front to gain the position, or teachers feeling more burnt out after two years (which would now be three) because they've put in more time and effort at that point.

-One misconception I've seen in a few posts lately is that if we start focusing on non-cognitive skills it will mean we can teach fewer cognitive skills and, therefore, math and reading achievement (etc.) will suffer.  This seems shortsighted to me since a large part of the reason non-cognitive skills are so compelling is that they lead directly to better academic performance.   One of the first studies to draw attention to this notion, for example, found that "grit" had a stronger effect on GPA than did IQ (more on "grit" here).  Now, a rigorous new 3-year randomized controlled trial finds that teaching social and emotional skills resulted in students posting larger gains in reading and math achievement than those in the control group.  So, I think that's a pretty clear "no" in response to the theory that teaching more non-cognitive skills will harm achievement.

-I doubt we'll ever stop debating the merits of pre-school, and here's some pushback against Russ Whitehurst's recent skeptical review of the evidence.  I don't think there's any question that the evidence here is mixed, but what I find compelling is that more than a couple studies have found large effects decades past the intervention.  The vast majority of interventions in education yield small effects that fade out quickly, so even if it's only a few of the very best pre-school programs that are having these effects it seems worth trying again.

-Starting Monday, I'll be running a multi-part series on how poverty impacts academic performance.  I'm looking forward to some great dialogue around the series . . .

Thursday, March 6, 2014

How Does Poverty Influence Academic Performance? Find out starting next week . . .

I read, seemingly everywhere about how poverty does or doesn't influence students' performance in schools (including  NY Times on poverty, USA Today on assessments  Sociological Images on SAT scoresEd Week on grit, and Brookings on college enrollment/graduation).

But I notice one thing in common among these pieces -- nobody actually seems to know exactly why living in poverty would or wouldn't lead to a change in achievement or attainment.  In other words, what is it about living in poverty that drives students' poor performance in schools?

We know students from wealthier families far outperform students from lower-income families in schools, but there's no consensus among researchers and very little knowledge among the general public as to why that's the case.  Heck, significant numbers of people still seem to think the relationship isn't even causal.

So, next Monday I'm going to begin a series that draws on my dissertation research to start to answer that question.  Expect 2-3 posts per week over the course of the spring as I explore 19 different ways in which living in poverty negatively impacts students' performance in school and what we can do about this.

I look forward to what should be a vigorous discussion . . .