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Quants Term 1

Course Outline for DTC Introduction to Quantitative Methods Term 1 2011

All will take place in R0.41

1. Introduction to quantitative research design (Kevin Mole, week 2, Thursday 10-12)

The main forms of quantitative research design, correlational studies, longitudinal designs and experimental/quasi-experimental designs; populations, samples and sampling frames, non-response bias, and survey designs.

2. Descriptive statistics and exploratory data analysis (Kevin Mole week 2, Friday 10-12)

Orientation to SPSS Variables (independent, dependent); Levels of measurement (nominal, ordinal and continuous). Frequency tables, histograms, simple crosstabulations (including calculating percentages / proportions appropriately). Summary measures of central tendency and spread (mean, mode median; SD and inter-quartile range). Introducing students to data entry/dataset construction within SPSS.

3. Sampling and the basis of inferential statistics (Kevin Mole week 3,Thursday 10-12)

Key differences between descriptive and inferential statistics; concepts of sampling and the standard error, confidence intervals, hypothesis testing, probability, Type I and Type II error, statistical power and statistical assumptions of normality. homogeneity of variance).

4. Questionnaire construction and survey design (Kevin Mole week 3 Friday 10-12)

Do’s and don’ts in designing questionnaires. (Pointer to AT session on Online questionnaire design). Practical aspects of survey implementation and fieldwork.

5. Reliability and validity in measurement - theory (David Arnott week 4 Thursday 10-12)

Measurements as the key to quantitative research, operationalising concepts, reliability, validity and generalisability in quantitative terms.

6. Reliability and validity in practice - (David Arnott week 5 Thursday 10-12)

Constructing and evaluating scales The benefits of moving from items to scales, how attitude scales, tests, inventories etc. are constructed, assumptions of unidimensionality, assessing reliability, Cronbach’s alpha, inter-rater measures. Practical examples of data manipulation in SPSS.

7. Non-parametric tests (chi-square, etc.) (David Arnott week 6 Thursday 10-12)

The analysis of nominal/categorical data, two-way and three-way cross-tabulations. Practical application using SPSS. (Pointer to AT sessions in logistic regression and log-linear models)

8. Comparing means (Kevin Mole week 6, Friday 10-12)

Reprise around confidence intervals; single sample, independent and related t-tests, non-parametric equivalents. One-way ANOVA and multiple comparisons. The concept of effect size as distinct from statistical significance.

9. ANOVA and factorial designs (Kevin Mole week 7 Thursday 10-12)

Basic concepts such as the sums of squares, variation explained, homogeneity of variance. Models with two and three or more variables. Exploring Interactions as well as main effects when modelling data (SPSS practical).

10. Correlation and simple linear regression (Kevin Mole week 8 Thursday 10-12)

Start by describing simple associations between two measures in the forms of simple two-way tables for nominal/ordinal data and graphically as scatterplots for continuous variables. build from such simple presentation through to statistical concepts of correlation, partial correlation and regression. How these analyses can be completed with the SPSS software package.

11. Multiple linear regression (Michael Mol week 9 Thursday 10-12)

Extend the previous discussion of correlation and simple linear regression to more complex statistical modelling using multiple regression. The aims and purposes of multiple regression , the logic of statistical control, partial correlation, causal pathways, The development of statistical models, explanation and prediction. Practical examples (SPSS) of how the analyses described are applied to longitudinal research data.

12. Secondary data analysis and quality in research reporting (Michael Mol week 10 Thursday 10-12)

The benefits and limitations of secondary data analysis. How to access secondary data (ESDS and UK Data Archive).

Possibly critiquing published research papers that have used quantitative methods, considering dimensions such as whether the research question was appropriately conceived, whether the particular analytic techniques employed were fit for purpose, whether results are clearly and succinctly reported, whether the conclusions follow from the results.