DDBA 8307 Week 1 to Week 8 Assignment & Discussion Questions

DDBA 8307 Quantitative Business Data Analysis Using SPSS

DDBA 8307 Week 1 Assignment Create a “Mock” SPSS Data File

DDBA/8307 Week 1 Discussion Applying the Integrative Model

DDBA 8307 Week 2 Assignment SPSS – Descriptive Statistics

DDBA8307 Week 2 Discussion Unit of Analysis and Sample Size

DDBA/8307 Week 3 Assignment SPSS – Independent-Samples t-Test

DDBA - 8307 Week 3 Discussion Data Assumptions and Parametric Statistical Tests

DDBA 8307 Week 4 Assignment SPSS – Analysis of Variance (ANOVA)

DDBA-8307 Week 4 Discussion Role of Theory in Quantitative Research

DDBA 8307 Week 5 Assignment SPSS – Correlation

DDBA 8307 Week 5 Discussion Relationships and Causation

DDBA 8307 Week 6 Assignment SPSS – Multiple Regression

DDBA 8307 Week 6 Discussion Reliability and Validity in Measurement

DDBA 8307 Week 7 Assignment SPSS – Two-Way Contingency Table Analysis Using Crosstabs

DDBA 8307 Week 7 Discussion Data Assumptions and Nonparametric Analyses

DDBA 8307 Week 8 Discussion 1 Quantitative Research and Social Change
DDBA 8307 Week 8 Discussion 2 Ethics and Quantitative Business Research

DDBA 8307 Entire Course solution (A+ Solution)

DDBA8307 Week 1 Assignment: Create a “Mock” SPSS Data File

For this Assignment you will use SPSS (PASW) software and learn to properly manipulate data according to APA requirements. This is an important skill and will be a major factor in future Assignments in this course and in your Doctoral Study.

To prepare for this Assignment, revisit Lessons 1–11 in the Green and Salkind (2017) text, with emphasis on Lesson 5. Review the SPSS Code Book document provided in the week’s Resources.

By Day 7

Submit a mock SPSS data file (.sav) based on the guidelines in the SPSS Code Book document provided in this week’s Resources. Note: You will be creating a data file consisting of the nine variables identified in the SPSS Code Book document. You will not input any data into the file.

Note: Refer to the Week 1 Assignment Rubric for specific grading elements and criteria.

Week 1 Discussion: Applying the Integrative Model          

In order to put quantitative research into a larger context, it is helpful to think about how the application of quantitative analysis may inform and shape the path of one’s research. You may recall that research methodology does not begin—nor does it end—with a decision to apply a qualitative, quantitative, or mixed-methods design. In fact, that decision has a pivotal position in the process once predecessor factors, such as the research question and purpose statement, are in place.

To prepare for this Discussion, review the article “An Integrative Model for Teaching Quantitative Research Design,” by Corner (2002), and consider your own Doctoral Study topic: How would you align your thought process with this integrative model? In DDBA 8300: Applied Research Methods—Qualitative and Quantitative, you were asked to frame your research question, or a potential research question, in a way that would lend itself to quantitative analysis.

By Day 3

Post an explanation of how you would align your thought process for your specific research topic with Corner’s integrative model. In your explanation, be sure to do the following:

•           Describe how you would align your current thinking and potential quantitative research approach with the integrative model. Does this approach make sense for your Doctoral Study? Why or why not?
•           Explain the aspects of your interests that would benefit from an integrative approach, as well as any areas where your process might diverge. Devote special attention to the portions of the model that particularly apply to quantitative research.
•           Identify which two of the proposed exercises from Corner (2002) would be most beneficial to you and why. If you wish, you may choose to attempt the exercises and describe the results in your posting.

Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source.

 
Week 2 Assignment: SPSS – Descriptive Statistics

For this Assignment, you will use SPSS (PASW) software and learn to properly manipulate data according to APA requirements. This is an important skill and will be a major factor in future Assignments in this course, as well as in your Doctoral Study.

To prepare for this Assignment, review the Week 2 Assignment Exemplar document provided in the week’s Resources, and download the Week 2 Assignment Template. Be sure to review the footnotes, as they provide helpful information. Also, review Lessons 20 and 21 in the Green and Salkind (2017) text, as well as the Copying and Pasting SPSS Output Into Word document, located in this week’s Resources. Download and use the Week 2 Assignment Dataset file for this Assignment. Keep in mind that you cannot draw conclusions using descriptive statistics without further testing (inferential statistics). Inferential statistics (e.g., t-tests, ANOVA, correlation, regression, etc.) will be covered in later weeks.

By Day 7

Submit an application of descriptive statistics within a quantitative business research context that follows the Week 2 Assignment Template. Your application must include the following:

·         An explanation of the implications of “Scales of Measurement” in quantitative research
·         A properly stated research question
·         A “Presentation of Findings” section, to include appropriate descriptive statistics for nominal (categorical/qualitative) and scale (ordinal, interval, and ratio) data using appropriately formatted APA table(s)
·         One appropriate graph for a nominal variable (e.g., pie chart) and one appropriate graph for scale (quantitative) variable (e.g., histogram)
·         An Appendix containing the SPSS output (see the Week 2 Assignment Exemplar)
·         Correct APA formatting, including in-text citations and a separate References page where appropriate

Please Note: You will cut and paste the appropriate SPSS output into the Appendix. The SPSS output is not in APA format, so you will need to type the information from the SPSS output to the appropriate sections of the APA table. You must use the Week 2 Assignment Template to complete this Assignment.

Week 2 Discussion: Unit of Analysis and Sample Size

Quantitative researchers must give serious consideration to the unit of analysis, the most elementary part of what is to be studied. Units of analysis can vary in scope, ranging from individuals to groups to institutions and beyond. Once the appropriate unit of analysis has been identified, the researcher can then start to address initial concerns such as the required sample size. Determination of the unit of analysis also impacts, but is not limited to, decisions regarding research design, data collection methods, and data analysis decisions, just to name a few.

To prepare for this Discussion, review the seminal article by Francis et al. (1999). Then review Rubric item 2.6b in the DBA Doctoral Rubric and Research Handbook. Think about the “who” or “what” you will need to use as your unit of analysis.

By Day 3

Post an assessment of the impact of the unit-of-analysis selection in quantitative doctoral business research. In your assessment, do the following:

•  Describe the importance of ensuring the unit of analysis aligns with the doctoral research purpose.
•  Explain the broader implications of selecting the incorrect unit of analysis on the practice to business.
•  Analyze the relationship between sample size for the chosen unit of analysis and statistical power.
•  Justify how and why the unit of analysis for you proposed quantitative study is appropriate for your research question.

Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source.

 
Week 3 Assignment: SPSS – Independent-Samples t-Test

The independent-samples t-test is one of several inferential statistical tests in the t-test family. The purpose of the independent-samples t-test is to examine whether there is a statistically significant group mean (average) difference on a scale (numerical) business outcome variable. For example, a researcher might be interested in knowing whether night shift workers produce more widgets, on average, than day shift workers. The independent-samples t-test is used when the researcher has one nominal (categorical) independent variable, with two levels, day shift and night shift, and one scale (numerical) dependent variable (e.g., monthly number of widgets produced).

To prepare for this Assignment, review the Paul and Garg (2014) article, focusing on the independent-samples t-test. Also, review the Week 3 Assignment Exemplar and Week 3 Assignment Template documents, as well as the tutorial videos, provided in this week’s Resources. Be sure to review the footnotes in the Week 3 Assignment Exemplar, as the footnotes provide additional explanatory information. Consider how independent-samples t-tests can impact the progress or results of a doctoral research study.

By Day 7

Submit a synthesis of statistical findings derived from independent-samples t-tests that follows the Week 3 Assignment Template. Your synthesis must include the following:

·         A description and justification for using the independent-samples t-test
·         A properly formatted research question
·         A properly formatted H0(null) and H1 (alternate) hypothesis
·         An APA-formatted Results section for the independent-samples t-test, to include the following:
o    Identification of the statistical test
o    Identification of independent and dependent variables, including identification of the number of levels for the independent variable
o    Identification of data assumptions and assessment outcome
o    Inferential results in correct APA statistical notation format
o    A properly formatted box plot
o    APA-formatted references
·         An Appendix including the SPSS output (Group Statistics and Independent Samples Test parameters; see Week 3 Assignment Exemplar)

 
Week 3 Discussion: Data Assumptions and Parametric Statistical Tests

The accuracy of parametric statistical tests is largely based on the data distribution of the collected data. Parametric tests are based on distribution assumptions, such as normality, linearity, equality of variances, etc. These assumptions and others vary based on the statistical test; therefore, it is critical for quantitative researchers to evaluate the assumptions pertaining to their statistical analyses and identify actions taken if assumptions are grossly violated.

To prepare for this Discussion, review the Lumley et al. (2002) article, as well as Lessons 19–21 and 24 in the Green and Salkind (2017) text. Use the Walden Library databases to identify a research example using your doctoral research proposal and consider the role and importance of the assumptions underlying each parametric test.

By Day 3

Post a comparison of one-sample, paired-samples, and independent-samples t-tests within the context of quantitative doctoral business research. In your comparison, do the following:

·         Describe the research example related to your doctoral research proposal.
·         Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.
·         Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.
·         Explain options researchers have when assumptions are violated.


Week 4 Assignment: SPSS – Analysis of Variance (ANOVA)

In Week 3, you ran the independent-samples t-test to compare the mean of two groups. However, there may be circumstances where you need to compare more than two group means. An ANOVA is a statistical method used to compare the means of two or more groups. For example, an IT manager wants to determine whether the mean (average) times required to complete a certain IT task differ based on the three types of employee training. The IT manager randomly selects 10 employees who have undergone the three types of training (three groups). Using an ANOVA, the IT manager could analyze data to verify whether the mean times required to complete the same IT task varies based on the three types of training.

For this Assignment, you will run a one-way ANOVA using the Week 4 Data File for One-way ANOVA.sav data file.

To prepare for this Assignment, review Lesson 25 from the Green and Salkind (2017) text, the Week 4 Assignment Exemplar and Week 4 Assignment Template documents, and the tutorial videos provided in this week’s Resources. Review the Roy and Saha (2016) article. Be sure to review the footnotes in the Week 4 Assignment Exemplar, as they provide additional explanatory information. Consider how you would extend your quantitative research to be appropriate for a one-way ANOVA. Download Week 4 Data File for One-way ANOVA.sav from the weekly resources.

By Day 7

Submit a synthesis of statistical findings derived from ANOVA that follows the Week 4 Assignment Template. Your paper must include the following:

·         A description and justification for using the one-way ANOVA
·         A properly formatted research question
·         A properly formatted H0(null) and H1 (alternate) hypothesis
·         An APA-formatted “Results” section for the one-way ANOVA
o    Identification of the statistical test
o    Identification of independent and dependent variables, including the identification of the number of levels for the independent variable
o    Identification of data assumptions and assessment outcome
o    Inferential results in correct APA statistical notation format
o    A properly formatted box plot
·         A discussion on how you would extend the one-way ANOVA to a two-way ANOVA using the variables in the Week 4 Data File for One-way ANOVA.sav dataset.
·         Properly APA-formatted references
·         Appendix containing SPSS output (see Week 4 Assignment Exemplar)

Note: You will cut and paste the appropriate SPSS output into the Appendix.

Week 4 Discussion: Role of Theory in Quantitative Research

Quantitative researchers use theory to explain and predict relationships.

The theoretical framework (Rubric item 1.10, Theoretical/Conceptual Framework) is the structure that explains the theory of a research study. The theoretical framework introduces and describes the theory that explains why the research problem under study exists. All quantitative DBA doctoral research studies must identify an appropriate theory in which to ground their research. The identified theory will provide the impetus for the selection of the independent/predictor variables.

To prepare for this Discussion, review the video tutorial titled “Theoretical/Conceptual Framework” located in this week’s Resources, as well as Rubric items 1.3, 1.4, 1.6, and 1.10 and pp. 42–44 in the DBA Doctoral Rubric and Research Handbook. Consider a possible theory for your proposed research problem and think about how the key constructs, propositions, tenets, etc., of the theory may help you better understand your business problem.
 
By Day 3

Post an analysis of the role of theory within the context of your quantitative doctoral business research. In your analysis, do the following:

·         Describe the central role theory plays in deductive reasoning when conducting quantitative business research.
·         Explain the critical relationship between the theory, specific business problem, purpose statement, and research question for a DBA applied Doctoral Study.
·         Provide at least one example from your own DBA doctoral research that illustrates the impact of theory on the development of an applied Doctoral Study.

 
Week 5 Assignment: SPSS – Correlation

Correlation analyses are a set of statistical tests used to determine whether there are relationships between two or more variables. At their most basic level, simple bivariate correlations, using Pearson’s Product-Moment Correlation test, are used to assess the initial relationship between two scale (numerical) variables. For example, a researcher might be interested in understanding the bivariate relationship between several business variables; however, it is important to note that correlation does not imply causality.

For this Assignment, you will run a Pearson Product-Moment Correlation Coefficient analysis using the Week 5 Data File for the Pearson Product-Moment Correlation Coefficient.sav data file.

To prepare for this Assignment, review Lesson 16A and Lesson 31 in your Green and Salkind (2017) text, as well as the Week 5 Assignment Exemplar and Week 5 Assignment Template documents, provided in this week’s Resources. Consider how a correlation analysis would effectively allow you to answer your research questions.

By Day 7

Submit a synthesis of statistical findings derived from correlational analysis that follows the Week 5 Assignment Template. Your synthesis must include the following:

·         A description and justification for using the Pearson Product-Moment correlation test
·         A properly formatted research question
·         A properly formatted H0(null) and H1 (alternate) hypothesis
·         An APA-formatted “Results” section for the Pearson Product-Moment test
o    Identification of the statistical test
o    Identification of variables (there is no distinction between independent and dependent variables for the Pearson Product-Moment correlation test)
o    Identification of data assumptions and assessment outcom
o    Inferential results in correct APA statistical notation format
o    A properly formatted scatter plot
·         An explanation of the differences and similarities of correlation analysis and bivariate regression analysis
·         Properly APA-formatted references
·         An Appendix containing SPSS output (see Week 5 Assignment Exemplar)


Week 5 Discussion: Relationships and Causation

When considering the world of relationships between variables, it is a common mistake to assume causation when a correlation is present. A high correlation between variables does not necessarily indicate causation. A study may show that there is a positive correlation between salary and the quality of work of individual employees in that the more employees are paid, the better their performance. However, there may be no causation because other factors may impact the quality of an individual’s work, such as training and experience. Also, consider if there is a positive correlation between employee training and quality of work of individual employees. Should a researcher safely assume that a causal relationship exists here in that the better training that employees receive, the better their performance? While strong correlations prompt researchers to take notice of possible causality, researchers must also be aware of attentional bias and prior beliefs when interpreting correlations. It is, therefore, important to examine how causation is established. In this Discussion, you will distinguish between the two concepts of causation and correlation and apply them to your potential Doctoral Study.

To prepare for this Discussion, review Lesson 31 in the Green and Salkind (2017) text and consider the correlation to your potential Doctoral Study topic. Your potential topic may or may not be appropriate for correlational methods, but for the purpose of this Discussion, assume it is.

By Day 3

Post an analysis of the difference between causation and correlation within the context of your DBA doctoral research study. In your analysis, do the following:

·         Assess the implications for professional practice when a researcher implies causation after using correlation (e.g., bivariate correlation) analyses.
·         Explain why the results of bivariate correlation analyses are considered weak in terms of internal validity.
·         Explain how would you extend or modify a research design to examine a true cause-and-effect relationship.
 

Week 6 Assignment: SPSS – Multiple Regression

Multiple linear regression is a logical extension to the Pearson Product-Moment Correlation test. Researchers use multiple linear regression to examine the relationship between at least two predictor variables and a scale (numerical) dependent variable. Multiple linear regression is the most commonly used statistical test for quantitative DBA studies.

For this Assignment, you will run a multiple linear regression using the Week 6 Data File for Multiple Linear Regression. You will use “job satisfaction” as the dependent variable.

To prepare for this Assignment, review Lesson 16A and Lessons 31–35 in your Green and Salkind (2017) text, the Week 6 Assignment Exemplar and Week 6 Assignment Template documents, as well as the tutorial videos provided in this week’s Resources. Consider how a multiple regression analysis will allow you to answer your research questions effectively.

By Day 7

Submit a synthesis of statistical findings derived from multiple regression analysis that follows the Week 6 Assignment Template. Your synthesis must include the following:

·         An APA Results section for the multiple regression test [see an example in Lesson 34 of the Green and Salkind (2017) text].
·         Only the critical elements of your SPSS output:
o    A properly formatted research question
o    A properly formatted H10 (null) and H1a (alternate) hypothesis
o    A descriptive statistics narrative and properly formatted descriptive statistics table
o    A properly formatted scatterplot graph
o    A properly formatted inferential APA Results Section to include a properly formatted Normal Probability Plot (P-P) of the Regression Standardized Residual and the scatterplot of the standardized residuals
o    An Appendix including the SPSS output generated for descriptive and inferential statistics
·         An explanation of the differences and similarities of bivariate regression analysis and multiple regression analyses

 
Week 6 Discussion: Reliability and Validity in Measurement

So far in this course, you have used existing data sets for weekly analyses; however, when writing your quantitative proposal, you will need to collect data using a preestablished instrument, one with sound psychometric (reliability and validity) properties, and an instrument that measures the variables/constructs you propose to measure. For this Discussion, you will explore the concepts of reliability and validity within a peer-reviewed journal article.

To prepare for this Discussion, select one of the following two required articles from this week’s Resources: Chaudhary et al. (2013) or Tang (2013). Assume you are contemplating using the instrument identified in your chosen article to measure the purported construct(s) in your study.

By Day 3

Post an explanation of the importance of psychometrically (reliability and validity) sound instruments to measure constructs within quantitative business research. In your explanation, do the following:

·         Describe the similarities and differences of reliability and validity within the context of quantitative business research.
·         Identify the instrument utilized in your chosen article, including relevant details such as requirements or tools.
·         Discuss the concepts of the instrument’s measurements and their psychometric properties.
·         Explain why or why not you would use this instrument in your own DBA Doctoral Study, providing support based upon your assessment of its psychometric properties.

Week 7 Assignment: SPSS – Two-Way Contingency Table Analysis Using Crosstabs

The Two-Way Contingency Table Analysis Using Crosstabs test is perhaps the most used nonparametric test. According to the Green and Salkind (2017) text, this type of test is used to evaluate whether a statistical relationship exists between two nominal (categorical) variables.

This week, you will use the Week 7 Data File for Two-way Contingency Table Analysis.sav data file.

To prepare for this Assignment, review Lesson 41 in your Green and Salkind (2017) text, as well as the Week 7 Assignment Template document provided in the week’s Resources. There is no Assignment Exemplar this week, as you should now be familiar with the format for presenting statistical analyses. Consider research scenarios where nonparametric analyses would be more appropriate than parametric analyses.

By Day 7

Submit a synthesis of statistical findings derived from Two-Way Contingency Table Analysis Using Crosstabs test that follows the Week 7 Assignment Template. Your synthesis must include the following:

·         An APA Results section for the “Two-Way Contingency Table Analysis Using Crosstabs”
·         Only the critical elements of your SPSS output:
o    A properly formatted research question
o    A properly formatted H10(null) and H1a (alternate) hypothesis
o    A descriptive statistics narrative and properly formatted descriptive statistics table
o    A properly formatted cluster bar
o    A properly formatted inferential APA Results section
o    An Appendix that includes the SPSS output generated for descriptive and inferential statistics [Examples of SPSS output are found on pp. 268–269 of Green and Salkind (2017).]

 
Week 7 Discussion: Data Assumptions and Nonparametric Analyses

Nonparametric tests, unlike parametric tests, do not imply any data assumptions. Nonparametric tests are commonly used under conditions where assumptions are violated or the required minimum sample size is not attained. The scale of measurement also can determine whether a parametric or nonparametric test is appropriate. The most common nonparametric tests are the chi-square test, two-way contingency analysis (a form of chi-square), and Mann-Whitney U-test, just to name a few.

To prepare for this Discussion, review Lesson 41 in the Green and Salkind (2017) text. Consider the impact of data analysis when the data distribution does not meet expected assumptions. Also consider conditions in which a nonparametric test is the most appropriate test.

By Day 3

Post an analysis of the relationship between data assumption violations and nonparametric analyses. In your analysis, do the following:

·         Compare the similarities and differences of parametric and nonparametric analyses in the context of data assumptions.
·         Provide at least one example of a parametric statistical test and its nonparametric equivalent, and explain how these examples illustrate the comparison of the two types of analysis.
·         Explain conditions under which you would use a nonparametric test (e.g., Mann-Whitney U-test over the independent samples t-test), including supportive examples from the course Resources for your explanation.

DDBA 8307 Week 8 Discussion 1: Quantitative Research and Social Change

It is crucial for you, as an independent scholar, to be able to apply knowledge and skills in these courses—not just to your DBA Doctoral Study, but to the wider world of business research. Promoting positive social change includes seeking opportunities for improvement of human or social conditions by promoting the worth, dignity, and development of individuals, communities, organizations, institutions, cultures, or societies.

To prepare for this Discussion, review the articles from Katzenstein and Chrispin (2011) and Santhosh and Baral (2015). Consider how the process and results of quantitative business research can shed insights into other areas related to positive social change such as corporate social responsibility.

By Day 3

Post an explanation of the relationship between quantitative business research results and positive social change. In your explanation, do the following:

·         Describe ways business leaders can benefit both financially and socially from quantitative data analyses.
·         Explain how you can directly apply perspectives on promoting positive social change to professional practice pertaining to your DBA Doctoral Study topic, providing examples from your DBA Doctoral Study prospectus.

 Week 8 Discussion 2: Ethics and Quantitative Business Research

When conducting a DBA doctoral research study, independent scholars are required to be as clear as possible in reporting the procedures used to obtain results. Being honest in accounting for their work is essential for researchers. The question arises as to what constitutes unethical behavior when conducting quantitative research. A major area of concern is fabrication of data or results and can lead to danger for others if, for example, critical business decisions are made on the basis of false findings.

To prepare for this Discussion, review the articles from Frechtling and Boo (2012) and Greenwood (2016). Consider the many ethical decisions you must make as an independent scholar during your DBA doctoral research. Moreover, think about the implications on business practice and key stakeholders if you present incorrect findings to business leaders.

By Day 4

Post an analysis of the role of ethical decision making on the practice of quantitative business research. In your analysis, do the following:

·         Explain the impact of using reliable and valid measures on quantitative findings.
·         Describe the negative impact of using inappropriate measurements, including supportive examples.
·         Explain the importance of knowledge of quantitative techniques to the ethical outcomes of quantitative research.

Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source.

Course: DDBA8307 Quantitative Business Data Analysis Using SPSS