**Using Statistics in the Social and Health Sciences with SPSS and Excel**Abbott, M. (2017).*Using statistics in the social health sciences with SPSS and Excel.*Wiley & Sons.

**This is the first main text reading for this week. These chapters (Ch. 7,8,11,12) discuss the basic correlational analysis, linear regression, and single sample t-tests. **

**IBM SPSS Essentials: Managing and Analyzing Social Sciences Data**Kulas, J., Roji, R., & Smith, A. (2021).*IBM SPSS essentials: Managing and analyzing Social Sciences data.*John Wiley & Sons Inc.

**This is the second main text reading for this week. These chapters (Ch. 9 and Ch. 11 only pp. 117-124) discuss the basic t-tests and simple correlations and regressions. **

**Laerd Statistics**Laerd Statistics. (2020).*Test that your data meets important assumptions.*Lund Research Ltd.

**Laerd is a great statistics site and this page discusses the basic inferential test assumptions.****Laerd Statistics**Laerd. (2018).*One-way ANOVA in SPSS Statistics.*Lund Research Ltd.

**Laerd is a great statistics site and this page discusses ANOVA.****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 resource 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.****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.

**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.****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.

**This source covers the Pearson correlation test.****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**Sims, J. (2018). Simple linear regression. In B. B. Frey (Ed.),*The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 1517-1519). SAGE.

**Simple linear regression is the simplest form of prediction. In this week, you will learn that the correlation analysis is related to regression analysis. However, correlations are used to examine relationships, while regression analyses are used for prediction.***t*-testsKorosteleva, 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 resource describes the three different*t*- tests and when each of them is used.**Levene's Homogeneity of Variance Test**Chen, Y. H., Wang, Y., & Kromrey, J. (2018). Levene’s homogeneity of variance test. In B. B. Frey (Ed.),*The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 970-972). SAGE.

**Please recall that 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 ANOVA.**

**ASC Statistics Resources**Academic Success Center (ASC). (2024).*Statistics resources.*Northcentral University.

**This page in the ASC 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.****APA Formatting Guide for Statistics**American Psychological Association. (2022).*American Psychological Association Style (7th ed.) numbers and statistics guide.*American Psychological Association. https://doi.org/10.1037/0000165-000

**This resource gives a summary of how to format statistics in APA 7th edition formatting.****Presenting Statistics in Text**Academic Success Center (ASC). (2024).*Presenting statistics in text.*Northcentral University.

**This page from the ASC offers support on how to use appropriately the different elements of statistics within text for reports, papers, assignments, etc.****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.

**Hypothesis testing is the main method used in statistics to examine statistical inference. The researcher sets a hypothesis (supposition) about a population parameter. In statistics, the hypothesis that is always tested is the null hypothesis.****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 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.**ANOVA**Boone, E. (2018).*Analysis of variance.*In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 87 - 89). SAGE.

**When comparing differences between three groups or more, one of the most common analyses is the analysis of variance (ANOVA). This resource provides a thorough explanation of this parametric technique.****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.

**As you learned last week, 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.****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 last 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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