Understanding quantitative data in educational research / Nicoleta Gaciu.

Format:

Book

Author/Creator:

Gaciu, Nicoleta, author.

Contributor:

Kathryn Faul and Joseph A. Wallace Fund.

Language:

English

Subjects (All):

Education--Research--Data processing.

Education.

Quantitative research.

R (Computer program language).

Education--Research.

Physical Description:

xx, 356 pages : illustrations ; 24 cm

Place of Publication:

London ; Thousand Oaks, California : SAGE Publications, 2021.

Contents:

Machine generated contents note: pt. One Understanding quantitative data and R

1. Introduction to information, knowledge and quantitative data

1.1. Quantitative data, information and knowledge in education

1.1.1. What is quantitative data?

1.2. Measurement and scales of measurement

1.2.1. What is a measurement? What is a scale?

1.3. From concepts to constructs and variables

1.4. Types of variables

1.5. Quantitative data and R

1.5.1. Why use R?

Further reading

2. An introduction to R and RStudio

2.1. Installing R

2.2. Upgrading R

2.3. Installing and using RStudio

2.4. Functions, packages and libraries

2.5. Working with data in R

2.5.1. Data structures and value types in R

2.6. Importing and exporting data sets

2.6.1. Importing data sets in R

2.6.2. Exporting data sets from R

2.7. Getting help on R and RStudio

pt. Two Data visualisation

3. Graphical representation of data

3.1. Using tables

3.2. Using graphs

3.2.1. Bar graph

3.2.2. Pareto graph

3.2.3. Pie graph

3.2.4. Histogram

3.2.5. Box and whisker graph

3.2.6. Line graph

3.2.7. Scatter graph

pt. Three Providing information about data

4. Descriptive statistics

4.1. From raw data to frequency distributions

4.2. Measures of location or central tendency

4.2.1. Mode

4.2.2. Median

4.2.3. Mean

4.3. Advantages and disadvantages of using the mode, median and mean

4.4. Measures of location and graphical display

5. Measures of dispersion and distributions

5.1. Range

5.2. Percentiles, deciles and quartiles

5.3. Interquartile range

5.4. Mean deviation

5.5. Standard deviation

5.6. Coefficient of variation

5.7. Shape of distributions and skewness

5.8. Advantages and disadvantages of using the range, mean deviation and standard deviation

5.9. Measures of dispersion and graphical display

6. Normal distribution and standardised scores

6.1. From histogram to normal distribution curve

6.2. Other visual methods for assessing normality of data

6.2.1. The boxplot and the normal distribution

6.2.2. The QQ plot and the normal distribution

6.3. Normal distribution and standard deviation

6.4. Statistical tests for normality

6.5. Standard normal distribution and z-scores

6.6. Transforming data values into z-scores

pt. Four Making estimations and predictions from data

7. Fundamentals of inferential statistics

7.1. What is inferential statistics and how does it work?

7.2. From sample to population

7.3. Sampling strategies

7.3.1. Random sampling methods

7.3.2. Non-random sampling methods

7.4. Making decisions about the population based on the information about the sample

7.5. Standard distributions

7.5.1. Standard normal distribution

7.5.2. R-distributions

7.5.3. Chi-squared distributions

8. Estimation and hypothesis testing

8.1. Making estimations

8.1.1. Standard error

8.1.2. Sample size

8.1.3. Confidence interval and confidence level

8.2. Statistical hypothesis testing process

8.2.1. Null and alternative hypotheses

8.2.2. Directional and non-directional hypotheses

8.2.3. Decisions about the null hypothesis: statistical levels, types of error and power

8.2.4. Regions of rejection

8.3. Selection of statistical tests

pt. Five From sample to population

9. One-sample tests

9.1. Parameter hypothesis testing using sample statistics

9.2. One-sample statistical tests for interval/ratio data

9.2.1. Z-test

9.2.2. F-test

9.2.3. Sign test

9.3. One-sample statistical tests for ordinal data

9.3.1. Wilcoxon signed-rank test

9.4. One-sample statistical tests for nominal data

9.4.1. Binomial test

9.4.2. Pearson chi-squared goodness-of-fit test

10. Differences between two independent or dependent samples

10.1. Differences between two independent samples

10.1.1. Mann-Whitney test (or Wilcoxon rank-sum test)

10.1.2. Independent samples t-test

10.1.3. Chi-squared test

10.2. Differences between two dependent samples

10.2.1. Wilcoxon signed-rank test

10.2.2. Paired samples t-test

10.2.3. McNemar's test

11. Differences between more than two independent samples

11.1. The analysis of variance (ANOVA)

11.2. One-way ANOVA

11.2.1. Calculating one-way ANOVA by hand

11.2.2. Computing one-way ANOVA in R

11.2.3. Post-hoc tests for one-way ANOVA

11.2.4. Measuring the effect size in a one-way ANOVA

11.3. Two-way ANOVA

11.3.1. Computing a two-way ANOVA in R

11.3.2. Interaction plot for two-way ANOVA test results

11.3.3. Post-hoc analysis in two-way ANOVA

11.3.4. Measuring the effect size in two-way ANOVA

11.4. Kruskal-Wallis ANOVA test

11.4.1. Post-hoc analysis for the Kruskal-Wallis test

12. Differences between more than two dependent samples

12.1. Repeated measures ANOVA

12.2. One-way repeated measures ANOVA

12.2.1. Checking assumptions for one-way repeated measures ANOVA

12.2.2. Computing one-way repeated measures ANOVA and Mauchly's test of sphericity

12.2.3. Post-hoc analysis for one-way repeated measures ANOVA

12.2.4. Measuring the effect size in a one-way repeated measures ANOVA

12.3. Two-way repeated measures ANOVA

12.3.1. Checking assumptions for two-way repeated measures ANOVA

12.3.2. Computing two-way repeated measures ANOVA in R

12.4. Friedman's test

12.4.1. Computing Friedman's test in R

12.4.2. Post-hoc analysis for Friedman's test

12.4.3. Measuring the effect size in Friedman's test

12.5. Cochran's Q-test

12.5.1. Manual calculation of Q-statistic

12.5.2. Computing Cochran's Q-test in R

12.5.3. Post-hoc analysis for Cochran's Q-test

12.5.4. Effect size for Cochran's Q-test

pt. Six Relationships and predictions

13. Relationships between variables

13.1. Covariance and correlation between two variables

13.1.1. Visual representation of the correlation

13.1.2. Coefficient of determination

13.1.3. Errors of the correlation coefficient

13.2. Correlations for more than two variables

13.2.1. Visual representation of the correlation matrix

13.3. Correlations and scales of measurement

13.3.1. Pearson's correlation coefficient

13.3.2. Spearman's correlation coefficient

13.3.3. Lambda, phi and Cramer's V correlation coefficients

14. Predictions for independent and dependent variables

14.1. Linear regression models

14.2. Ordinary least squares regression

14.2.1. Creating the OLS regression model

14.2.2. Checking for statistical significance

14.2.3. Assessing the assumptions of the linear regression model

14.3. Multiple linear regression

14.3.1. Creating the multiple linear regression model

14.3.2. Checking statistical significance

14.3.3. Assessing the assumptions of the multiple linear regression model

Further reading.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Kathryn Faul and Joseph A. Wallace Fund.

Other Format:

ebook version :

ISBN:

9781473982147

1473982146

9781473982154

1473982154

OCLC:

1203024774

Publisher Number:

99987412646