M. Ed. in Educational Psychology
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M. Ed. in Educational Psychology
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quantitative method and statistics psychology of child development psychology of learning and classroom processes alternative perspectives on special education |
Quantitative Methods and Statistics
Aims: This module has been designed as a core module for the M Ed
in Educational Psychology it aims to: · To familiarise students with quantitative research methods commonly used in education and psychology · To introduce students to quantitative methods of data collection · To introduce students to quantitative methods of data analysis ·
To introduce students to the SPSS computer package Learning
Outcomes: On
completion of this unit successful students will be able to: · Plan, execute and evaluate a small-scale project using quantitative methodology ·
Demonstrate a comprehensive understanding of techniques applicable
to their own and other’s research and advanced scholarship Transferable
skills The following transferable skills will be addressed:
Content: Independent and
dependent variables: their identification and selection Experimental
manipulation, control and internal validity: the roles of random allocation,
matching, and counterbalancing in independent groups, related samples and
repeated measures designs. The experimental
manipulation of more than one independent variable in factorial designs: the
contribution of interaction effects. Descriptive and
summary statistics: measures of central tendency and dispersion; skew and
kurtosis; frequency distributions; graphical methods including frequency
histograms and cumulative frequency plots; exploratory data analysis including
stem and leaf and box and whisker displays. Probability theory:
rules for assigning and combining probabilities; the OR rule with mutually
exclusive and non-mutually exclusive events; the AND rule with independent and
non-independent events; the binomial distribution (and its normal approximation) The normal
distribution: z scores and areas under the curve; the sampling distribution of
the sample mean. Statistical
inference: significance testing (including the null and alternative hypothesis,
type 1 and type 2 errors, significance level, power and sample size; effect size
and confidence intervals. Z tests and t tests
of means for single sample, independent samples and related sample designs. Confidence
intervals: for population means; for the difference between two population
means. Mean and error bar
graphs Non parametric
alternatives to t tests: the sign test; Wilcoxon matched pairs signed ranks
test; Mann Whitney test. Tests of
proportions: chi square tests for goodness of fit and for contingency tables. Cramers phi as a
measure as a measure of association in contingency tables. McNemar’s test of
change. Bivariate
correlation and linear regression: scatter plots; Pearson’s correlation
coefficient; partial correlation; the significance of a correlation coefficient;
the linear regression equation and its use in prediction; the accuracy of
prediction; Spearman’s and Kendall’s rank order correlation coefficient. The analysis of
variance: one factor independent and repeated measures designs; two factor
independent, repeated measures and mixed designs; main effects and interaction
effects (including graphical presentation); planned (including trend)
comparisons; the Bonferroni correction: post hoc comparisons (including the
choice between method); the analysis of simple effects. Non parametric
alternatives to one factor analysis of variance; Kruksall-Wallis, Friedman and
Cochroan’s Q tests The choice of an
appropriate statistical analysis: the issue of level of measurement (nominal,
ordinal, interval and ratio scales); test assumptions (e.g. normality,
homogeneity of variance, linearity); transformations of the dependent variable
in an attempt to meet assumptions; robustness; power efficiency. Teaching
and Learning This
class will be taught in the computer laboratory. It is a mixture of practical
experiments, computer simulations, and experiential learning Learning
hours:
Assessment
Recommended
Reading:
Dancey, C. P. & Reidy, J. (2002) Statistics Without Maths for Psychology, Harlow, Pearson, 0 13 033633 5. Field,
A (2000) Discovering Statistics: Using SPSS for Windows London: Sage Publications
ISBN 0 7619 5755 3 Kinnear,
PR & Gray, CD (2001) SPSS version 10 for Windows Made Simple. London:
Lawrence Erlbaum Associates ISBN 1 84169 118 6 One
of these statistics books must be brought to each class. You are advised to
familiarise yourselves with at least the first three chapters in the book and to
try out the exercises on the PCs before the beginning of the first lesson. If
you are not up to speed with a key board, it will be in your best interest to
put in some practice before the start of the module. SPSS version 10 is available in both computer labs |
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Send mail to neil.humphrey@man.ac.uk
with questions or comments about this programme.
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