Advertisement

Results

  • Craig Lambert
Chapter

Abstract

This Chapter begins with a summary of the tests used to screen the data on each of the dependent variables to determine which statistical analyses were appropriate. The screening procedures consisted of confirming the normality of score distributions, testing for outliers, and confirming the homogeneity of the variance on each of the variables. It was found that the five variables connected with the model of NP complexity were suitable for a full factorial analysis using a MANOVA model. Comparative structures had to be collapsed across one factor to normalize the distribution and variance and then analyzed using a two-way factorial ANOVA model. Relative clause use was analyzed using non-parametric tests of the effects of each factor separately and subsequently corrected for the number of comparisons. In other words, the research hypotheses (Sect.  7.1) had to be tested in three parts as they related to each set of dependent measures.

References

  1. Cramer, D. (1998). Fundamental statistics for social research. London: Routledge.Google Scholar
  2. Cramer, D., & Howitt, D. (2004). The Sage dictionary of statistics: A practical resource for students of the social sciences. Thousand Oaks, CA: Routledge.CrossRefGoogle Scholar
  3. Cramer, E., & Bock, R. (1966). Multivariate analysis. Review of Educational Research, 36, 604–617.Google Scholar
  4. Diessel, H., & Tomasello, M. (2005). A new look at the acquisition of relative clauses. Language, 81, 882–906.CrossRefGoogle Scholar
  5. Duane, D., & Seward, L. (2011). Measuring skewness. Journal of Statistics Education, 19, 1–18.Google Scholar
  6. Fields, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage.Google Scholar
  7. Hoaglin, D., & Iglewicz, B. (1987). Fine-tuning some resistant rules for outlier labeling. Journal of American Statistical Association, 82, 1147–1149.CrossRefGoogle Scholar
  8. Hoaglin, D., Iglewicz, B., & Tukey, J. (1986). Performance of some resistant rules for outlier labeling. Journal of American Statistical Association, 81, 991–999.CrossRefGoogle Scholar
  9. Howell, D. (2009). Statistical methods for psychology (6th ed.). New York: Wadsworth.Google Scholar
  10. Huberty, C., & Petoskey, M. (2000). Multivariate analysis of variance and covariance. In H. Tinsley & S. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling. New York: Academic.Google Scholar
  11. Martin, W., & Bridgman, K. (2012). Quantitative and statistical research methods: From hypothesis to results. Somerset, NJ: Wiley.Google Scholar
  12. Ravid, D., & Berman, R. (2010). Developing noun phrase complexity at school age: A text-embedded cross-linguistic analysis. First Language, 30, 3–26.CrossRefGoogle Scholar
  13. Razali, N., & Wah, Y. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lillefous and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2, 21–33.Google Scholar
  14. Shapiro, S., & Wilk, M. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611.CrossRefGoogle Scholar
  15. Tukey, J. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Craig Lambert
    • 1
  1. 1.Curtin UniversityPerthAustralia

Personalised recommendations