LibGuides: EDR-8202 v5: Week 3 Resources

Week 3 Resources

personality.sav
You will use this data file to complete this week’s assignment.

Stats II Week 3 Part I Correlations
Lloyd, C. (2021, August 6). Stats II Week 3 Part I Correlations. [Video] Kaltura.
This video presents a walkthrough of the Week 3 Part I assignment, Correlations.
Stats II Week 3 Part II regression
Lloyd, C. (2021, August 6). Stats II Week 3 Part II regression. [Video] Kaltura.
This video presents a walkthrough of the Week 3 Part II assignment, regression.
Stats II Week 3 Part III t - test
Lloyd, C. (2021, August 6). Stats II Week 3 Part III t - test. [Video] Kaltura.
This video presents a walkthrough of the Week 3 Part III assignment, t-test.
Effect size.
Fritz, C. O., & Morris, P. E. (2018). Effect size. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 577-578). SAGE.
This section describes the different measures of effect size used in statistics. These effects are important in terms of the size and the possibility of being observed in further studies where the sample is obtained from the same population.
Independent samples t - test
Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Independent samples t - test (pp. 463 - 470). In Performing data analysis using IBM SPSS. Wiley & Sons.
This source describes the procedures to conduct an independent samples t - test analysis using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.
Inferential statistics.
Seaman, M. (2018). Inferential statistics. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 819-820). SAGE. <.br> This source discusses the importance of inferential statistics and how they are applied to the analysis of observed data from a sample. Furthermore, how the results of the observed data can be applied to make inferences to the general population.
Paired samples t - test
Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Paired samples t - test (pp. 471 - 474). In Performing data analysis using IBM SPSS. Wiley & Sons.
This source describes the procedures to conduct a paired samples t - test analysis using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.
Pearson correlation
Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Pearson correlation (pp. 159 - 164). In Performing data analysis using IBM SPSS. Wiley & Sons.
This source describes the procedures to conduct the Pearson correlation using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.
Pearson correlation coefficient
Gordon, M., & Courtney, R. (2018). Pearson correlation coefficient. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1299-1233). SAGE.
The Pearson correlation is used to examine the linear relationship between two variables. It is a measure that is used when both variables are continuous.
R².
Taraday, M., & Wieczorek-Taraday, A. (2018). R². In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1362-1363). SAGE.
This is the coefficient of determination used in a regression analysis. The R² is used to represent that amount of variance in the dependent variable, which is predicted by the independent variable.
Results section
Zheng, C. (2018). Results section. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1432). SAGE.
This resource describes the research paper section that includes the results of the statistical analysis. The results section is very important in the research paper because provides the answer to the research questions.
Scatterplots.
LeBeau, B. (2018). Scatterplots. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1456-1460). SAGE.
This source presents scatterplots. These are graphical representations that are used in statistics to examine the relationships between variables.
Simple linear regression
Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Simple linear regression (pp. 173 - 180). In Performing data analysis using IBM SPSS. Wiley & Sons.
This source describes the procedures to conduct a simple linear regression using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.
Simple linear regression.
Lawrence S. Meyers, , Glenn C. Gamst, , and A. J. Guarino. (2013). Simple linear regression. In B. B. Frey (Ed.), Performing Data Analysis Using IBM SPSS (pp. 1517-1519). Wiley.
Simple linear regression is the simplest form of prediction. In this section, you will learn that the correlation analysis is related to a regression analysis. However, correlations are used to examine relationships, while regression analyses are used to for prediction.
t - tests.
Korosteleva, O., & Song, B. (2018). t - tests. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1652-1654). SAGE.
The t - test is used with the t distribution to examine differences between two groups when the variances are not known. This section describes the three different t - tests and when each of them are used.
Levene’s homogeneity of variance test
Frey, B. (2018). The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1-4). Thousand Oaks,, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139
The Levene’s test is a test to examine the assumption of homogeneity of variance. This assumption is used in the independent t - test analysis and the analysis of variance.

Supplemental Resources

Academic Success Center (ASC). (2020). Statistics resources. National University.
This LibGuide includes all of the statistics resources curated by NU's Academic Success Center. Use the headings on the left side of this ASC page to navigate to your particular area of need.
Presenting Statistics in Text
This page from the ASC offers support on how to use appropriately the different elements of statistics within text that you write.
Hypothesis testing.
Coleman, J. S. (2018). Hypothesis testing. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 803-804). SAGE.
This resource was introduced in a previous week. Hypotheses are formed in inferential statistics and used to make decisions about the population using a sample. This source provides discussion regarding the decisions that are made based on hypothesis testing.
Alpha level.
Kim, H. W. (2018). Alpha level. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 65-66). SAGE.
The alpha level is chosen a priori as a level (typically .05) used to reject or not reject the null hypothesis, and this value is compared to p - value obtained from the statistical analysis. This chapter defines and discusses the concepts related to the alpha level.
p Value
Kim, H. W. (2018). p Value. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1195-1198). SAGE.
The p - value is the probability value that is used in conjunction with the concept of significance. This chapter discusses the different uses of the p - value.
Significance
Harlow, L. (2018). Significance. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1514-1516). SAGE.
The author of these pages presents the concept of significance in statistics. Significance indicates the probability of an event (e.g., the difference between two groups) happening by chance—or that true differences exist.
Type I error
Hannon, B. (2018). Type I error. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1741-1743). SAGE.
Type I error in hypothesis testing occurs when the null hypothesis is equivocally rejected. In other words, assuming that significant differences exist, when in fact they don’t exist.
Type II error.
Liu, X.S. (2018). Type II error. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1743-1745). SAGE.
As you learned from this resource in a previous week, Type II error in hypothesis testing occurs when the null hypothesis is equivocally not rejected—in other words, assuming that significant differences do not exist, when they do in fact exist.

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