Assignment 1: Factorial Analysis of Variance in SPSSEarlier this week, you practiced using factorial ANOVA models with SPSS and, ideally, used the Collaboration Lab to ask, answer, and otherwise address any questions you had. In this Assignment, you apply what you learned to answer a social research question using factorial ANOVA.To prepareReview the datasets provided.Construct a research question with social change implications based on one of those datasets.Be sure to focus on the assumptions of this test and ask yourself, âDoes it make sense to interpret the mean of this dependent variable?âRemember that you will need categorical predictor variables.The AssignmentUse SPSS to answer the research question you constructed. Then, compose a 1- to 2-paragraph analysis in APA format in which you answer the following questions:What is the null hypothesis for your question?What research design(s) would align with this question?What dependent variable was used and how is it measured?What independent variable is used and how is it measured?If you found significance, what is the strength of the effect?What is the answer to your research question?What are the possible implications of social change?NOTE: Be sure to include ALL your datasav output with your analysis. I am uploading the data in excel form and SPSS please convert back to SPSS and provide all the output tables in your Word document and here is some additional datasav that you can possibly choose only 1 is required.Note the output is required from SPSS; however, this site you are not able to click and get it or can I upload the dataset so I converted them to Excel and you can convert them back to SPSS to get the output only the Afrobarometer you can get online I put the link and if you are using that. ALL AREAS MUST BE CITED AND REFERENCED IN APAhttp://afrobarometer.org/data/merged-round-6-data-36-countries-2016HS Long Study_[student] (4).savThe Media References is the attached PDFLaureate Education (Producer). (2017k). Introduction to Factorial analysis of variance [Video file]. Baltimore, MD: Author.Laureate Education (Producer). (2017f). Factorial analysis of variance [Video file]. Baltimore, MD: Author.Please use any other references if you have to
Problem Set Week TwoThe questions in the assignment follow the examples provided in the weekly guidance lectures.The first question this week focuses on the kind of data we have. Different levels of data allow us to do different kinds of analysis, so we need to understand what we have to work with. Question two involves developing the probability of randomly picking a student who has certain characteristics from the sample. Question three involves finding the probability of randomly picked employees falling within the top one-third of different groups using Excel functions. Question four and five involve using statistical tests to determine if the compa-ratio (an alternate measure of pay).The final question asks for an interpretation of your opinion on the question of equal pay for equal work based on the work done this week. Both the assignment file and the data file are located in the Course Materials section at the bottom in the Multi-Media section. The assignment file contains all of the weekly assignments (for Weeks 2, 3, and 4). See the labeled tabs at the bottom of the Excel assignment file. The data in the data file needs to be copied over into the assignment file, and you will be set for the entire class. *I downloaded all the info I think you are going to need, if you have any questions contact me.
NEWS understands the issues that they must overcome in terms of quality, speed, and controlling costs. NEWS believes that the analysis that your team has provided in the last three Excel modules has led to successful strategic plans. The NEWS BOD would like to understand the probability of this success before granting permission for the CEO to execute the plan. Data has been gathered on the last 50 process improvement program (PIP) projects that the NEWS BOD had approved. The BLUE columns describe whether the PIP was initially approved as a quality, speed, or cost control project, or combination. (1 = Yes) The GREEN columns describe the quality, speed, and $ results from each project. Regardless of how the PIP was initially chosen, the positive or negative results were gathered in terms of quality, speed, and $ at the end of the project. The RED column describes the BOD final determination of whether the PIP was successful or not; old BOD criteria was confidential. (1 = Yes) Question 1: Using the data given below, complete Task 1. The BOD would like to know the percentage of PIP projects completed per each category, since their short-term memory has hindered their ability to remember the percentage that began as an effort to overcome quality, speed, and cost issues. Also, briefly discuss the PIP success rate attributable to each type of PIP effort based on the BOD’s confidential criteria shown only as success or failure Results. ANSWER: Quality received a 58%, Speed-58%, Costs 52%, Q&S-24%, Q&C22%, S&C 32%, Q,S,C-10% The PIP success rate is dependent on whether the project was approved for each category. Question 2: Using the data given below, complete Task 2. NEWS is very proud of their PIP initiative and has briefed the press that their success rate is greater than 50%. Is this true? Explain. The BOD is very concerned about this next process improvement project decision. It is truly a make or break initiative for the company, and therefore a more conservative set of success criteria has been provided. Does the new criteria change the rate of success of past PIP initiatives? Given this probability of success, what recommendation would you make to the BOD? ANSWER: No, It is not true. The NEW PIP success rate is at 22%, which is less than the 300 Defective Free per 1000 25 1 – 1 5 809 14 $ 127,488.00 1 Speed > 15 Days Reduced 26 1 – – 1 395 18 $(452,635.00) 1 Costs
It is important to look at data in a graphical form. Patterns are the essence of data exploration, and the eyeâs ability to discern forms and patterns makes visual display integral to the process. The visual display of quantitative information can help us see connections and relationships in the data, which are oftentimes difficult to detect in tables of numbers. We should look at data in a graphical form, and not rely solely on computational or statistical metrics. In this discussion, we will explore graphs in linear regression. Our data are taken from an article by Frank Anscombe in a 1973 article in The American Statistician, which discusses scatterplots in relation to regression analyses.First, download the dataset MHA610_Week 5_Discussion_Regression_Data.xls. This is a simple Excel workbook, with data on one sheet. There are eight columns of data, with headings X1, Y1, X2, Y2, X3, Y3, X4, Y4. Import the data into Statdisk using the MHA610_Week 5_Discussion_Regression_Data.CSV file, and perform the following analyses.Calculate the regressions of Y1 on X1, Y2 on X2, Y3 on X3, and Y4 on X4, and compare the results (summary statistics). Explain what, if anything, you find unusual about these results.Plot each set of data, along with the fitted regression line. Describe what the graphs tell you about the relationships between the Xâs and the Yâs.Explain what lessons you draw from this exercise.Place the summary statistics and the plots in a separate Word document and attach that document to your initial post. Address the questions in the body of your initial discussion post.
The Act Tet had a mean composite score of 18 and a standard deviation of 6. Assume the scores are normally distributed.What percent of the students had a score that is less than 14?SHOW WORK PLEASE!
Describe a study of interest in which you could use some form of Chi Square test.
Choose any published database from the internet (such as those from the Census Bureau or any financial or sports sites) or from your workplace. You may opt to use one of the data files provided by the instructor if applicable. Your chosen database must be pre-approved by the instructor.If the file is large than 200 observations, randomly choose 200 observations from the data.Explain each variable in the file that you are analyzing. Be sure your file includes at least 3 scale variables and at least 2 nominal variables.Conduct a descriptive analysis on any 2 interval / ratio variables you wish using Descriptive_Statistics.xls and Frequency_Distribution.xls. Explain the output.Conduct 3 different hypothesis tests of your choice using appropriate variables from the file (note: you must use 3 different tests and not run one test on 3 different variables). In each case, state the variables being tested as well as the hypothesis, decision and conclusion. Use 3 of the following (1-Sample Test for Means, 1-Sample Test for Proportions, 2-Sample Test for Means – Independent Samples, 2-Sample Test for Means – Paired Samples, 2-Sample Test for Proportions, Analysis of Variance, Chi Square Goodness of Fit Test, Chi Square Test of Independence, Correlation Test).Develop a model to predict an interval / ratio variable using at least 2 other variables. Use Multiple_Regression.xls and state the regression model and which variables are or are not significant. Also, use the model to make a prediction by making up values for each of the independent variables.Write a one to two page summary of your findings. Include the data file in the appendix.Expectations: Copy & paste the Excel displays into a Word file if you can. Upload the Word file (or the Excel files) for credit. Comments in which you describe your findings should be included with each display. APA formatting is not required but the assignment should be formatted professionally so that it flows well for weekly assignments and you should adhere to the rules of written English in your punctuation, grammar and spelling.
Week 1Answer the following questions:1. Jackson (2012) even-numbered Chapter Exercises (p. 244).2. What is the purpose of conducting an experiment? How does an experimental design accomplish its purpose?3. What are the advantages and disadvantages of an experimental design in an educational study?4. What is more important in an experimental study, designing the study in order to make strong internal validity claims or strong external validity claims? Why?5. In an experiment, what is a control? What is the purpose of a control group? Of single or multiple comparison groups?6. What are confounds? Give an example of a design that has three confounds. Describe three ways to alter the design to address these confounds and explain the advantages and disadvantages of each.7. What does “cause” mean and why is it an important concept in research? How are correlation and causation related?8. You are a researcher interested in addressing the question: does smiling cause mood to rise (i.e., become more positive)? Sketch between-participants, within-participants, and matched-participants designs that address this question and discuss the advantages and disadvantages of each to yielding data that help you answer the question. Describe and discuss each design in 4-5 sentences.Week 2This is a two part assignment that will be submitted within one document.Part IPart I checks your understanding of key concepts from Jackson and Trochim & Donnelly.Answer the following questions:1. Jackson even-numbered Chapter exercises (pp. 220-221; 273-275)2. What are degrees of freedom? How are the calculated?3. What do inferential statistics allow you to infer?4. What is the General Linear Model (GLM)? Why does it matter?5. Compare and contrast parametric and nonparametric statistics. Why and in what types of cases would you use one over the other?6. Why is it important to pay attention to the assumptions of the statistical test? What are your options if your dependent variable scores are not normally distributed?Part IIPart II introduces you to a debate in the field of education between those who support Null Hypothesis Significance Testing (NHST) and those who argue that NHST is poorly suited to most of the questions educators are interested in. Jackson (2012) and Trochim and Donnelly (2006) pretty much follow this model. Northcentral follows it. But, as the authors of the readings for Part II argue, using statistical analyses based on this model may yield very misleading results. You may or may not propose a study that uses alternative models of data analysis and presentation of findings (e.g., confidence intervals and effect sizes) or supplements NHST with another model. In any case, by learning about alternatives to NHST, you will better understand it and the culture of the field of education.Answer the following questions:1. What does p = .05 mean? What are some misconceptions about the meaning of p =.05? Why are they wrong? Should all research adhere to the p = .05 standard for significance? Why or why not?2. Compare and contrast the concepts of effect size and statistical significance.3. What is the difference between a statistically significant result and a clinically or “real world” significant result? Give examples of both.4. What is NHST? Describe the assumptions of the model.5. Describe and explain three criticisms of NHST.6. Describe and explain two alternatives to NHST. What do their proponents consider to be their advantages?7. Which type of analysis would best answer the research question you stated in Activity 1? Justify your answer.Week 3Answer the Following Questions1. Jackson, even-numbered Chapter Exercises, pp. 308-310.2. What is an F-ratio? Define all the technical terms in your answer.3. What is error variance and how is it calculated?4. Why would anyone ever want more than two (2) levels of an independent variable?5. If you were doing a study to see if a treatment causes a significant effect, what would it mean if within groups, variance was higher than between groups variance? If between groups variance was higher than within groups variance? Explain your answer6. What is the purpose of a post-hoc test with analysis of variance?7. What is probabilistic equivalence? Why is it important?Week 4Answer the Following Questions:1. Jackson, even-numbered Chapter Exercises, pp. 335-337.2. Explain the difference between multiple independent variables and multiple levels of independent variables. Which is better?3. What is blocking and how does it reduce “noise”? What is a disadvantage of blocking?4. What is a factor? How can the use of factors benefit a design?5. Explain main effects and interaction effects.6. How does a covariate reduce noise?7. Describe and explain three trade-offs present in experiments.Week 5Quasi-Experimental DesignsPart I – Answer the following questions:1. Jackson (2012), even-numbered chapter exercises, p 360.2. Describe the advantages and disadvantages of quasi-experiments? What is the fundamental weakness of a quasi-experimental design? Why is it a weakness? Does its weakness always matter?3. If you randomly assign participants to groups, can you assume the groups are equivalent at the beginning of the study? At the end? Why or why not? If you cannot assume equivalence at either end, what can you do? Please explain.4. Explain and give examples of how the particular outcomes of a study can suggest if a particular threat is likely to have been present.5. Describe each of the following types of designs, explain its logic, and why the design does or does not address the selection threats discussed in Chapter 7 of Trochim and Donnelly (2006):a. Non-equivalent control group pretest onlyb. Non-equivalent control group pretest/posttestc. Cross-sectionald. Regression-Discontinuity6. Why are quasi-experimental designs used more often than experimental designs?7. One conclusion you might reach (hint) after completing the readings for this assignment is that there are no bad designs, only bad design choices (and implementations). State a research question for which a single-group post-test only design can yield relatively unambiguous findings.Part II – Answer the following questions:1. What research question(s) does the study address?2. What is Goldberg’s rationale for the study? Was the study designed to contribute to theory? Do the results of the study contribute to theory? For both questions: If so, how? If not, why not?3. What constructs does the study address? How are they operationalized?4. What are the independent and dependent variables in the study?5. Name the type of design the researchers used.6. What internal and external validity threats did the researchers address in their design? How did they address them? Are there threats they did not address? If so how does the failure to address the threats affect the researchers’ interpretations of their findings? Are Goldberg’s conclusions convincing? Why or why not?Week 6Your study could:Examine the literature in your topic area and identify five articles published within the past five years that investigate mediating, moderating, or independent variables in an attempt to contribute to theory in the topic area. Write a paper in which for each article, you:1. Describes the theory the researchers explore. What are the key constructs in the theory? How are they related? Identify which ones are cause, effect, mediating, or moderating constructs. How are the constructs operationalized?2. Briefly describe the study, including the number of participants and research methods.3. Briefly describe the statistical analyses used4. Briefly described the findings and how the researchers interpreted them and their contribution to theory.Using some or all of the five articles, argue for a gap in the knowledge in the topic area and briefly describe a study involving mediator and or moderator variables that can contribute to theory. Week 7Samples, Power Analysis, and Design Sensitivity Warm-up ActivityDownload G*Power and play around with it. See how changes in assumptions and parameters affect sample size estimates.Part 11. Compare and contrast internal and external validity. Describe and give examples of research questions for which external validity is a primary concern. Describe and give examples of research questions in which internal validity is a primary concern. Discuss strategies researchers use in order to make strong claims about the applicability of their findings to a target population.2. Compare and contrast random selection and random assignment. Be sure to include a discussion of when you would want to do one or the other and the possible consequences of failing to do random selection or random assignment in particular situations.3. Explain the relationship between sample size and the likelihood of a statistically significant difference between measured values of two groups. In other words, explain why, all else being equal, as sample size increases the likelihood of finding a statistically significant relationship increases.4. Compare and contrast probability and non-probability sampling. What are the advantages and disadvantages of each?Part 2If you do a quantitative study for your dissertation, you must estimate the sample size you will need in order to have a reasonable chance of finding a relationship among the variables stated in your research hypotheses (should one exist), given your statistical analysis(es) and assumptions/calculations of factors 2-4 above. You must do this, even if you plan to use a convenience sample (see below). There are a number of sample size calculators available. Northcentral uses G*Power, which is required in this Activity. You will use G*Power’s “a priori power analysis” function to calculate a sample size. If it yields an unrealistically large size sample, you will rethink your design and assumptions and, perhaps, use G*Power’s “compromise power analysis” to estimate a workable sample size that makes sense. If you plan on using a convenience sample, you would use both analyses as part of your argument that your convenience sample is large enough.Submit the Following1. Calculate the sample size needed given these factors:· one-tailed t-test with two independent groups of equal size· small effect size (see Piasta, S.B., & Justice, L.M., 2010)· alpha =.05· beta = .2· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample half the size. Indicate the resulting alpha and beta. Present an argument that your study is worth doing with the smaller sample.2. Calculate the sample size needed given these factors:· ANOVA (fixed effects, omnibus, one-way)· small effect size· alpha =.05· beta = .2· 3 groups· Assume that the result is a sample size beyond what you can obtain. Use the compromise function to compute alpha and beta for a sample approximately half the size. Give your rationale for your selected beta/alpha ratio. Indicate the resulting alpha and beta. Give an argument that your study is worth doing with the smaller sample.3. In a few sentences, describe two designs that can address your research question. The designs must involve two different statistical analyses. For each design, specify and justify each of the four factors and calculate the estimated sample size you’ll need. Give reasons for any parameters you need to specify for G*Power. Week 8Write your mock Concept Paper using the Concept Paper template found in the Dissertation Center. Follow the template guidelines for each section.1. Write an Introduction describing your topic.2. Write the Statement of the Problem section.3. Describe the Purpose of the Study. Include the results of your power analysis.4. State your Research Question and your null and alternative hypotheses. Be sure that your question aligns with your purpose.5. Write a Brief Review of the Literature.6. Complete the Research Methods section (including the Operational Definition of Variables, Constructs, and Measurement sub sections). Follow the instructions in the CP template. Be sure to:a. Identify the strengths and weaknesses of your envisioned design and methods.b. Identify threats to validity and how your design will address them.c. Justify why your chosen design and methods are more appropriate for your research question than alternatives you have considered.d. Define the constructs you will measure and what you will do in order to determine how to operationalize them.e. Describe the sample you propose to study and its characteristics; this should include, but is not limited, to: 1) age; 2) gender; 3) ethnicity; 4) additional cultural factors; and 5) education level. Justify your choice of sample.f. Describe your method of sampling.g. Describe the type of data you need to collect and how you will collect it.h. Briefly describe any ethical issues you foresee with your study. Make a preliminary assessment of the level of risk associated with participation in your study that might need to be raised with the Institutional Review Board.i. Describe and justify how you will analyze your data and the descriptive statistics will you present.j. Explain how you conducted your power analysis.k. Describe how you will handle your data, check for accuracy etc.l. What problems do you foresee in implementing the design? How might you prevent them?
Finish the self check quizzes and the quizzes for the statistic online course. Approximately total 100 multiple choice question. I will give you the web address. login account and pass word later. Ty.
Choose any published database from the internet (such as those from the Census Bureau or any financial or sports sites) or from your workplace. You may opt to use one of the data files provided by the instructor if applicable. Your chosen database must be pre-approved by the instructor.If the file is large than 200 observations, randomly choose 200 observations from the data.Explain each variable in the file that you are analyzing. Be sure your file includes at least 3 scale variables and at least 2 nominal variables.Conduct a descriptive analysis on any 2 interval / ratio variables you wish using Descriptive_Statistics.xls and Frequency_Distribution.xls. Explain the output.Conduct 3 different hypothesis tests of your choice using appropriate variables from the file (note: you must use 3 different tests and not run one test on 3 different variables). In each case, state the variables being tested as well as the hypothesis, decision and conclusion. Use 3 of the following (1-Sample Test for Means, 1-Sample Test for Proportions, 2-Sample Test for Means – Independent Samples, 2-Sample Test for Means – Paired Samples, 2-Sample Test for Proportions, Analysis of Variance, Chi Square Goodness of Fit Test, Chi Square Test of Independence, Correlation Test).Develop a model to predict an interval / ratio variable using at least 2 other variables. Use Multiple_Regression.xls and state the regression model and which variables are or are not significant. Also, use the model to make a prediction by making up values for each of the independent variables.Write a one to two page summary of your findings. Include the data file in the appendix.Expectations: Copy & paste the Excel displays into a Word file if you can. Upload the Word file (or the Excel files) for credit. Comments in which you describe your findings should be included with each display. APA formatting is not required but the assignment should be formatted professionally so that it flows well for weekly assignments and you should adhere to the rules of written English in your punctuation, grammar and spelling. NOTE: Data set contains 2 nominal variable( size of the venue, team owned by Bollywood star or not), 3 scale variables (win by runs,win by wickets and season week). Amendments can be done according to question if needed.