**Center for Teaching and Learning. (2019, August).***Downloading IBM SPSS Statistics*. Northcentral University.**As part of your registration for this course, NU provides access to the Statistical Package for the Social Sciences (SPSS). Take time to ensure your access to SPSS so that you can troubleshoot problems now. If you have difficulty accessing SPSS, contact the NU IT support desk (servicedesk@ncu.edu). Once you have access to SPSS, any questions should be addressed to your instructor.****Stats II Week 1 Examine the data for errors****Booker-Zorigan, B. (2021, August 13).***Stats II Week 1 Examine the data for errors*. [Video] Kaltura.

This video presents a walkthrough of the Week 1 assignment, including an overview of how to examine the data for errors.**Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral 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.****APA Formatting Guide for Statistics****American Psychological Association. (2020).***Publication manual of the American Psychological Association (7th ed.).*

This resource gives a summary of how to format statistics in APA 7th edition formatting.**Fraudulent and misleading data.****Bradford, L. (2018). Fraudulent and misleading data. In B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (Vol. 1, pp. 707-708). SAGE Publications, Inc., https://www-doi-org.proxy1.ncu.edu/10.4135/9781506326139.n276**

This reading discusses the concepts of reporting fraudulent and misleading statistics with the intention of misguiding the reader. Unfortunately, when this behavior happens, the researcher will face serious professional consequences.**Descriptive statistics****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**

This reading discusses the concepts of reporting fraudulent and misleading statistics with the intention of misguiding the reader. Unfortunately, when this behavior happens, the researcher will face serious professional consequences.

by Andy FieldISBN: 9781526419521Publication Date: 2018-01-08Chapter 1 (pp. 1-36) discusses the basic measurement terminology and concepts needed for statistical analyses. Moreover, it discusses the different types of characteristics of the variables, and it provides the basic information related to the dissemination of the results.*Discovering Statistics Using IBM SPSS Statistics***This is a Redshelf book which you will access through the Bookshelf on the top navigation bar in the course.**

**Entering data in IBM SPSS****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Entering data in IBM SPSS. In Performing data analysis using IBM SPSS (pp. 5-13). Wiley & Sons.**

In these pages, you will find the instructions to enter the data in SPSS. The resource also presents with the two types of displays and their respective functions.**Importing data from Excel to IBM SPSS****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Entering data in IBM SPSS. In Performing data analysis using IBM SPSS (pp. 5-13). Hoboken APA Citation**

In these pages, you will find the instructions to enter the data in SPSS. The resource also presents with the two types of displays and their respective functions.**Parameters.****Mitra. A. (2012). Parameters. In N. J. Salkind (Ed.),***Encyclopedia of research design*(pp. 998-999). SAGE.

In these pages, you will find the definition of a parameter. It is important for you to understand the difference between a parameter and a statistic. The parameter is a characteristic of the population, while a statistic is a characteristic of a sample.**Levels of measurement****Coleman, J. S. (2018). Levels of measurement. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 969). SAGE.**more...**Sample****Huck, S. W., Beavers, A. S., & Esquivel, S. (2012). Sample. In N. J. Salkind (Ed.),***Encyclopedia of research design*(pp. 1295-1299). SAGE.

This chapter discusses the characteristics of the samples. It defines what a sample is and the use of samples in research to make inferences about a population.**SPSS.****Gordon, M., & Courtney, R. (2018). SPSS. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1577-1583). SAGE.**

This resource presents an introduction to the basic commands available in SPSS. It presents the description of the different windows found in the software, as well as instructions to conduct basic analyses.

**SPSS Confidence Scores.sav****You will use this data file as part of this week’s assignment. The data in this file are the same data presented in Table 1 within the assignment instructions.**

**SPSS Confidence Scores.sav****You will use this data file as part of this week’s assignment. The data in this file are the same data presented in Table 1 within the assignment instructions.**

**Stats II Week 2****Lloyd, C. (2021, August 6).***Stats II Week 2*. [Video] Kaltura.

This video presents a walkthrough of the Week 2 assignment, 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.**Assessing distribution shape: Normality, skewness, and kurtosis****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Assessing distribution shape: Normality, skewness, and kurtosis (pp. 139 - 145). In Performing data analysis using IBM SPSS. Wiley & Sons.**

This source describes the procedures to examine for normality using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.**Explore****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Explore (pp. 71-76). In Performing data analysis using IBM SPSS. Wiley & Sons.**

This source describes the procedures to examine for normality using the explore tab in SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.**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.**Robust statistics****Blaine, B. E. (2018). Robust statistics. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1435-1436). SAGE.**

This source defines and discusses the concept of robust statistics. These are procedures for which, in spite of a violation to the assumptions in statistics, the results can still be accepted as accurate results.**Kolmogorov-Smirnov test****Thombs, L. (2018). Kolmogorov-Smirnov test. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 931-932). SAGE.**

This source provides a description of the Kolmogorov-Smirnov test, a test that is used to examine the assumption of normality.**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.

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.**Meeting the homogeneity of variance****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Meeting the homogeneity of variance (pp. 464 - 469). In Performing data analysis using IBM SPSS. Wiley & Sons.**

This source describes the procedures to examine for the assumption of homogeneity of variance using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.**Normal distribution.****Nicol, A. A. M. (2022). Normal distribution. In B. B. Frey (Ed.),***The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 1088-1091). SAGE..*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.**Scientific method****Staddon, J. E. (2018). Scientific method. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1473-1477). SAGE.**

This chapter presents the relationship between the scientific method and how statistics are used to examine and analyze data to answer research questions. Moreover, these research questions are stated as hypotheses. These hypotheses include the null and the alternative hypothesis. Statistics test for the accuracy of the null hypothesis, which is the one that states that no differences exist between the groups or no relationship between the variables.**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.

**Supplemental Resources**

**ASC Statistics Resources****Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral 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.**

**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.*R*2.**Taraday, M., & Wieczorek-Taraday, A. (2018).***R*2. 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*R*2 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*. Northcentral 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.

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

**Stats II Week 4 Multiple Regression****Booker-Zorigan, B. (2021, August 7).***Stats II Week 4 Multiple Regression*. [Video] Kaltura.

This video presents a walkthrough of the Week 4 assignment, including an overview of how to complete a multiple regression.**Analysis of variance.****Boone, E. L. (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.**Correlation****Jung, H. J., & Randall, J. (2018). Correlation. In B. B. Frey (Ed.),***The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 413). SAGE.

Correlation is used to examine the relationship between variables. This relationship can be quantified by calculating the correlation coefficient.**Multicollinearity****Daoud, J. I. (2017). Multicollinearity and regression analysis.***J. Phys.: Conf. Ser. 949*, 012009. 10.1088/1742-6596/949/1/012009

Multicollinearity refers to the relationship between predictors in a multiple regression analysis. If two or more predictors are highly correlated to each other, the researcher must determine which one of them is going to be included in the mode.**Multiple linear regression****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**

Multiple linear regression is an extension of the simple linear regression. In this case, the researcher is building a prediction model with more than one predictor. The model determines the amount of contribution that a predictor has on the predicted variable.**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.**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.

**Supplemental Resources**

**ASC Statistics Resources****Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral 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.**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.*R*2.**Taraday, M., & Wieczorek-Taraday, A. (2018).***R*2. 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*R*2 is used to represent that amount of variance in the dependent variable, which is predicted by the independent variable.**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.**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.**

**divorce-studentversion.sav****You will use this data file to complete this week’s assignment.**

**Stats II Week 5 ANCOVA****Booker-Zorigan, B. (2021, August 7).***Stats II Week 5 ANCOVA*[Video]. Kaltura.

This video presents an overview of the Week 5 assignment, including a walkthrough of ANCOVA.**Stats II Week 5 ANOVA****Booker-Zorigan, B. (2021, August 7).***Stats II Week 5 ANOVA*[Video]. Kaltura.

This video presents an overview of the Week 5 assignment, including a walkthrough of ANOVA.**Stats II Week 5 MANOVA****Booker-Zorigan, B. (2021, August 7).***Stats II Week 5 MANOVA*[Video]. Kaltura.

This video presents an overview of the Week 5 assignment, including a walkthrough of MANOVA.

by Andy FieldISBN: 9781526419521Publication Date: 2018-01-08Chapter 12 (pp. 385-422) will discuss issues specific for comparing several means, or ANOVA. Among the topics included are, assumptions of the test, post hoc procedures, effect size, etc.*Discovering Statistics Using IBM SPSS Statistics***This is a Redshelf book which you will access through the Bookshelf on the top navigation bar in the course.**

**Eta squared****Fritz, C. O., & Morris, P. E. (2018). Eta squared. In B. B. Frey (Ed.),***The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 607). SAGE.

Eta squared is a measure of effect size estimate. It is very often used in the analysis of variance (ANOVA).*F*distribution.**Raunig, D. (2018).***F*distribution. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 659-662). SAGE. 10.4135/9781506326139

The*F*distribution is very important because it is related to the ANOVA. The*F*distribution is the most powerful analysis to compare two variances.**Analysis of variance.****Boone, E. L. (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.**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.**Interaction****Fritz, C. O., & Morris, P. E. (2018). Interaction. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 849-853). SAGE.**

This section explains how to interpret the effect of two or more independent variables in a dependent variable. Interaction is occurring when the effect of one variable changes depending on the value of another variable.**Multivariate analysis of variance. Methods of Multivariate Analysis****Rencher, A., & Christensen, W. (2012). Multivariate analysis of variance. Methods of Multivariate Analysis (pp. 169-244). Hoboken, NJ: Wiley & Sons.**

This is an extension of a univariate analysis of variance. In this case, you measure more than one dependent variable with each independent variable..**Multivariate analysis of variance****Stockburger, D. (2018). Multivariate analysis of variance. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1120 - 1126). SAGE.**

This is another reference that discusses MANOVA, which is an extension of a univariate analysis of variance. In this case, you measure more than one dependent variable with each independent variable.**Post hoc analysis.****Tipton, R., & Morgan, G. (2018). Post hoc analysis. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1271-1273). SAGE.**

These analyses are conducted after the rejection of the null hypothesis. They are used to examine mean differences. This section provides an overview of these tests used in the ANOVA.**One way between subject ANOVA****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). One way between subject ANOVA (pp. 477 - 484). In Performing data analysis using IBM SPSS. Wiley & Sons. <.br> This source describes the procedures to conduct a one-way ANOVA using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.****Two-Way Analysis of Variance****Harring, J., & Johnson, T. (2018). Two-way analysis of variance. In B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (Vol. 1, pp. 1734-1737). SAGE Publications, Inc.**

This test is an extension of ANOVA. In this case, you are testing the effect of two independent variables on a dependent variable. In addition, the interaction between both variables will be examined.**Two way between subject ANOVA****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Two way between subject ANOVA (pp. 507 - 520). In Performing data analysis using IBM SPSS. Wiley & Sons.**

This source describes the procedures to conduct a two-way ANOVA using SPSS. Furthermore, it provides explanations of the different outputs obtained in the analysis.

**Supplemental Resources**

**Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral 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****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.

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.**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.**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.**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.**Analysis of variance.****Boone, E. L. (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.

**Data mining****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**

This source discusses the process of applying algorithms to various data. These techniques can be used for prediction. The exploratory factor analysis is one of those techniques.

**TOSSE-R_adapted.sav****You will use this data file to complete this week’s assignment.**

**Stats II Week 6 Factor Analysis****Lloyd, C. (2021, August 6).***Stats II Week 6 Factor Analysis*[Video]. Kaltura.This video is meant as a general walkthrough of the process. When you complete your assignment, you'll see that your file utilizes a 22-question TOSSE-R survey instead of the 28-question survey referenced in this video..

by Andy FieldISBN: 9781526419521Publication Date: 2018-01-08Chapter 18 (pp. 569-609) will guide you through conducting an exploratory factor analysis using SPSS. Moreover, it discusses the different results obtained and how to interpret them after conducting the analysis.*Discovering Statistics Using IBM SPSS Statistics***This is a Redshelf book which you will access through the Bookshelf on the top navigation bar in the course.**

**Exploratory factor analysis****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**

Exploratory factor analysis is used to determine the items in a survey that are related to each other, and it is used to develop constructs or factors within the survey.**Principal components and factor analysis****Lawrence S. Meyers, Glenn C. Gamst, & A. J. Guarino. (2013). Performing Data Analysis Using IBM SPSS. Wiley.This source will guide you through the process of conducting factor analysis in SPSS. Factor analysis is used to identify themes or factors in a large set of variables.****Reliability Analysis****Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Reliability Analysis: Internal Consistency In Performing data analysis using IBM SPSS. Wiley & Sons. (pp. 311 - 318)This source will guide you through the process of conducting reliability analysis in SPSS. Moreover, you will be introduced to several techniques for internal consistency like coefficient alpha.****Scree plot****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 scree plot is a graphical representation of the numbers that are relevant to the factors related to the principal components of factor analysis.

**Supplemental Resources**

**Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral University.**Presenting Statistics in Text**

**Stats II Week 7 Non-parametric testing****Booker-Zorigan, B. (2021, August 7).***Stats II Week 7 Non-parametric testing*[Video]. Kaltura.

This video presents information about non-parametric testing.**Friedman 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**

This source describes the non-parametric version of the repeated measures analysis of variance. The Friedman test is used when one or more of the repeated measures ANOVA assumptions are not met.**Kruskal Wallis****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 This source describes the non-parametric version of the one-way analysis of variance. The Kruskal-Wallis test is used when one or more of the ANOVA assumptions are not met.****Mann-Whitney 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**

This source describes the non-parametric version of the independent samples*t*- test. The Mann-Whitney test is used when one or more of the*t*- test assumptions are not met.**Spearman correlation coefficient.****McHugh, M. (2018). Spearman correlation coefficient. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1555-1558). SAGE.**

This correlation statistic is used to measure the strength of association between to variables measured in the ordinal level.**Wilcoxon signed ranks 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**

This source describes the non-parametric version of the dependent samples*t*- test, which is the Wilcoxon signed test. The major assumption of this test is that the data are at least at the ordinal level of measurement.

**Supplemental Resources**

**Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral University.**Presenting Statistics in Text**

**Stats II Week 8 Summary of Analyses/Signature Assignment****Lloyd, C. (2021, August 6).***Stats II Week 8 Summary of Analyses/Signature Assignment*[Video]. Kaltura.

This video presents a summary of analyses and your signature assignment.**Alpha level.***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.**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.**Significance**

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.

**Supplemental Resources**

**Academic Success Center (ASC). (2020).***Statistics resources*. Northcentral University.**Presenting Statistics in Text****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.**more...*p*Value*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.**Robust statistics****Blaine, B. E. (2018). Robust statistics. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (pp. 1435-1436). SAGE.**

This source defines and discusses the concept of robust statistics. These are procedures for which, in spite of a violation to the assumptions in statistics, the results can still be accepted as accurate results.**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.

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.**Normal distribution.****Nicol, A. A. M. (2022). Normal distribution. In B. B. Frey (Ed.),***The SAGE encyclopedia of educational research, measurement, and evaluation*(pp. 1088-1091). SAGE..*R*2.**Taraday, M., & Wieczorek-Taraday, A. (2018).***R*2. 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*R*2 is used to represent that amount of variance in the dependent variable, which is predicted by the independent variable.**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.****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.**Exploratory factor analysis****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**

Exploratory factor analysis is used to determine the items in a survey that are related to each other, and it is used to develop constructs or factors within the survey.

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