Music Participation and Socioeconomic Status as Correlates of Change:

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Music Participation and Socioeconomic Status as
Correlates of Change

• A Longitudinal Analysis of Academic Achievement

Peter Miksza Indiana University
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Acknowledgments

• Dr. Ken Kelley
• Inquiry Methodology Program
• Indiana University
• Dr. Charles P. Schmidt
• Music Education
• Indiana University

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Relating Music Education and Academic Achievement

• A significant topic among scholars and advocates
• (Bresler, 2002 Mark, 2002 Morrison, 2000)
• Arguments cautioning an emphasis of the
  extra-musical benefits of music education
• (Gardner, 1999 Reimer, 1999)
• Reality of educational climate in the United
  States

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Correlational InvestigationsInstrumental music
education, academic achievement, SES

• Klinedinst, 1991, 1992
• Reading and math achievement positively related
  to performance achievement
• Academic achievement, self-concept, SES
  associated with retention

• Fitzpatrick, 2006
• Academic achievement of instrumental students
  greater when compared to non-instrumental
  students regardless of SES
• In some cases low SES instrumental students
  outperformed high SES non-instrumental students
  in academic achievement

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National Education Longitudinal Survey of 1988
(NELS88)

• Catterall, Chapleau, Iwanaga, 1999
• Overall, high SES instrumental music students 46
  more likely to achieve high math proficiency
  scores
• At the first wave of the study (8th grade), 21.1
  of low SES instrumental music students scored
  high math proficiency scores whereas only 10.7
  of low SES non-music participants had high math
  proficiency scores
• This discrepancy was found to be even larger when
  examining the third wave of the study (12th
  grade) (33 music to 15.5 non-music)

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NELS88 in the Current Study

• Re-analysis of music participation, academic
  achievement, and SES data
• Music participation
• Band, chorus, and/or orchestra (several variables
  recoded and combined)
• Status remains constant across all three data
  collection waves (8th, 10th, and 12th grade)
• Tracking individuals over time rather than
  aggregate analyses
• SES at first data collection wave
• Composite variable created by the National Center
  for Educational Statistics
• Parents education level, parents occupation,
  family income
• Academic achievement
• Math, Reading, Science, and Social Studies

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Purpose and Research Questions
To examine whether change in academic achievement
from 8th to 12th grade varied as a function of
participation in school music, SES, or the
interaction of music participation and SES

• Did academic achievement scores change
  significantly from 8th to 12th grade?
• Did individual students vary in the amount they
  changed in academic achievement from 8th to 12th
  grade?
• Was participation in music a significant
  predictor of initial status or rate of change in
  academic achievement?
• Was student SES a significant predictor of
  initial status or rate of change in academic
  achievement?
• Did initial status or rate of change in academic
  achievement vary as a function of the interaction
  of music participation and SES?

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Sample Characteristics

• Original NELS88 sample
• 1,734 schools randomly selected from 39,000
• 1,057 actually participated
• 80 public, 20 private
• 24,599 subjects participated in the 1st wave
• 10,000 subjects participated in the 1st, 2nd, and
  3rd waves

• Sample for current study
• 5,335 subjects
• Consistent status of participation in band, choir
  and/or orchestra for all three waves
• Regional distribution 20.2 Northeast, 27.6
  North Central, 32.8 South, 19.3 West
• Gender distribution 50.9 male, 49.1 female
• Participation distribution 21.9 music
  participants, 78.1 non-participants

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Results
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Results
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Multilevel Model
A regression technique used to test prediction
equations applied to nested data sets

• Level-1
• Examining intra-individual change over time by
  treating scores at each time point as nested
  within individuals

• Level-2
• Inter-individual differences are examined across
  time as well

Simultaneous analyses are carried out at level-1
(within-person, individual) and level-2
(between-person, group)
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Multilevel Model Components
Multilevel model analyses result in the
estimation of parameters which can be used to
describe the intra- and inter-individual change
that has taken place

• Fixed Effects (?)
• Mean growth parameters
• Predicted initial status (intercept)
• Predicted rate of change (slope)

• Variance Components (?)
• Total within-person residual variance across all
  occasions
• Individual deviation from each fixed
  effect/growth parameter

Maximum likelihood estimation methods are those
most commonly used to derive the parameters The
parameters can then be tested for statistical
significance using z-tests
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Multilevel Model Analyses
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Multilevel Model Results

• NOTE plt.01, plt.001

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Multilevel Model Results
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Multilevel Model Results

• Model Comparisons
• Conditions
• Models analyzed in this study were
• Estimated with full-maximum likelihood procedures
• Derived from identical data sets
• Nested in design (A within B within C within D
  within E)
• Test statistic
• Change in deviance statistic between nested
  models
• ?2 distribution with df equal to difference in
  number of parameters estimated between models
• Significant changes in deviance between all
  models - except D and E
• Results suggest that overall, model D is best fit

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Plots of predicted academic achievement by music
and SES
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Plots of predicted academic achievement by music
and SES
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Plots of predicted academic achievement by music
and SES
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Plots of predicted academic achievement by music
and SES
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Summary of findings

• Academic achievement increased significantly from
  8th to 12th grade

• Significant differences in initial status were
  found between music participants and
  non-participants

• With the exception of reading, music
  participation was not significantly related to
  the rate of change in academic achievement

• SES was a significant predictor of initial status
  and rate of change for all academic achievement
  variables

• Model comparisons indicated that the interaction
  between music participation and SES did not
  significantly improve the overall fit

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Limitations

• Causal vs. Correlational
• Age of data

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Conclusion

• Achievement gap between music participants and
  non-participants

• Trends consistent across all academic achievement
  variables

• Findings support those of previous researchers
• (e.g., Catterall et al, 1999 Fitzpatrick, 2006
  Klinedinst, 1991, 1992)

• Consequences of policies that sacrifice monetary
  or time-related resources for music programs

• Potential effects of time-consuming music
  programs on student academic progress

• Achievement gap between low and high SES subjects
  that grows more disparate over time

• Music participants maintained higher levels of
  academic achievement regardless of SES

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Questions?
Thank You!
The data used in this study were drawn from
public use computer files provided by U.S. Dept.
of Education, National Center for Education
Statistics. NATIONAL EDUCATION LONGITUDINAL
STUDY BASE YEAR THROUGH FOURTH FOLLOW-UP,
1988-2000 Computer file, www.icpsr.umich.edu.
ICPSR version. Washington, DC U.S. Dept. of
Education, National Center for Education
Statistics producer, 2002. Ann Arbor, MI
Inter-university Consortium for Political and
Social Research distributor, 2004.