Title: Music Participation and Socioeconomic Status as Correlates of Change:
1Music Participation and Socioeconomic Status as
Correlates of Change
- A Longitudinal Analysis of Academic Achievement
Peter Miksza Indiana University
2Acknowledgments
- Dr. Ken Kelley
- Inquiry Methodology Program
- Indiana University
- Dr. Charles P. Schmidt
- Music Education
- Indiana University
3Relating 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
4Correlational 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
5National 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)
6NELS88 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
7Purpose 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?
8Sample 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
9Results
10Results
11Multilevel 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)
12Multilevel 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
13Multilevel Model Analyses
14Multilevel Model Results
15Multilevel Model Results
16Multilevel 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
17Plots of predicted academic achievement by music
and SES
18Plots of predicted academic achievement by music
and SES
19Plots of predicted academic achievement by music
and SES
20Plots of predicted academic achievement by music
and SES
21Summary 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
22Limitations
- Causal vs. Correlational
- Age of data
23Conclusion
- 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
24Questions?
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.