# rsch6210 all weeks discussions latest 2017

Question

Week 1 discussion

The Logic of Inference: The Science of Uncertainty

Describing and explaining social phenomena is a complex task. Box’s quote speaks to the point that it is a near impossible undertaking to fully explain such systems—physical or social—using a set of models. Yet even though these models contain some error, the models nevertheless assist with illuminating how the world works and advancing social change.

The competent quantitative researcher understands the balance between making statements related to theoretical understanding of relationships and recognizing that our social systems are of such complexity that we will always have some error. The key, for the rigorous researcher, is recognizing and mitigating the error as much as possible.

As a graduate student and consumer of research, you must recognize the error that might be present within your research and the research of others.

To prepare for this Discussion:

Use the Walden Library Course Guide and Assignment Help found in this week’s Learning Resources to search for and select a quantitative article that interests you and that has social change implications.

As you read the article, reflect on George Box’s quote in the introduction for this Discussion.

For additional support, review the Skill Builder: Independent and Dependent Variables, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.

By Day 3

Post a very brief description (1–3 sentences) of the article you found and address the following:

Describe how you think the research in the article is useful (e.g., what population is it helping? What problem is it solving?).

Using Y=f(X) +E notation, identify the independent and dependent variables.

How might the research models presented be wrong? What types of error might be present in the reported research?

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

By Day 5

Remembering that all research has some error, respond to at least one colleague’s post and comment on how we as social change agents and critical consumers of research can balance the usefulness with the error in the research. Do we throw the research out because of too much error, or is there something useful that it can tell us?

Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.

Week 2 discussion

Displaying Data

Visual displays of data provide you and anyone else with a graphical display of what is often a complex array of quantitative data. A key strength of visualization is the ability to quickly enlighten you with key data. Rather than solely relying on your audience to interpret numerical values and statistics explained in a narrative, a visual display can easily illustrate descriptions, relationships, and trends. Although the focus is on simplicity, the researcher has an obligation to present these graphical displays in a clear and meaningful way.

For this Discussion, you will explore ways to appropriately display data.

To prepare for this Discussion:

Review the Learning Resources for this week related to frequency distributions and graphic displays of data.

Using the SPSS software, open the General Social Survey dataset found in this week’s Learning Resources.

Next, create a figure or table from a few selected variables within the dataset.

Finally, think about what is good about how the data are displayed in the figure or table you created and what is not so good.

By Day 3

Post your display of the table or figure you created and provide an explanation of why this would be the best way to display the data provided.

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

By Day 5

Respond to at least one of your colleagues’ post and determine whether you are able to understand the “whole picture” of the data or understand the data in its entirety. What might you add to their display and why? What might you change to their display and why?

Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.

Week 3 discussion

Central Tendency and Variability

Understanding descriptive statistics and their variability is a fundamental aspect of statistical analysis. On their own, descriptive statistics tell us how frequently an observation occurs, what is considered “average”, and how far data in our sample deviate from being “average.” With descriptive statistics, we are able to provide a summary of characteristics from both large and small datasets. In addition to the valuable information they provide on their own, measures of central tendency and variability become important components in many of the statistical tests that we will cover. Therefore, we can think about central tendency and variability as the cornerstone to the quantitative structure we are building.

For this Discussion, you will examine central tendency and variability based on two separate variables. You will also explore the implications for positive social change based on the results of the data.

To prepare for this Discussion:

Review this week’s Learning Resources and the Descriptive Statistics media program.

For additional support, review the Skill Builder: Visual Displays for Categorical Variables and the Skill Builder: Visual Displays for Continuous Variables, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.

Review the Chapter 4 of the Wagner text and the examples in the SPSS software related to central tendency and variability.

From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one continuous and one categorical variable Note: this dataset will be different from your Assignment dataset).

As you review, consider the implications for positive social change based on the results of your data.

By Day 3

Post, present, and report a descriptive analysis for your variables, specifically noting the following:

For your continuous variable:

Report the mean, median, and mode.

What might be the better measure for central tendency? (i.e., mean, median, or mode) and why?

Report the standard deviation.

How variable are the data?

How would you describe this data?

What sort of research question would this variable help answer that might inform social change?

Post the following information for your categorical variable:

A frequency distribution.

An appropriate measure of variation.

How variable are the data?

How would you describe this data?

What sort of research question would this variable help answer that might inform social change?

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

By Day 5

Respond to at least one colleagues’ post with a comment on the presentation and interpretation of their analysis. In your response, address the following questions:

Was the presentation of results clear? If so, provide some specific comments on why. If not, provide constructive suggestions.

Are you able to understand how the results might relate back to positive social change? Do you think there are other aspects of positive social change related to the results?

Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.

Week 4 discussion

The Importance of Relationships

As its name implies, confidence intervals provide a range of values, along with a level of confidence, to serve as an estimate of some unknown population value. Since it is rare to have access to the entire population, you must frequently rely on the confidence interval of the sample to make some inference about the population of interest. Before making accurate inferences to the population, we need to fully understand how the three key components of the interval—variability in the data, sample size, and confidence level—impact the width of the interval.

For this Discussion, you will explore the relationship between these components and understand the trade-off between reducing risk in our confidence of estimates and increasing precision.

To prepare for this Discussion:

Review Chapters 7 and 8 of the Frankfort-Nachmias & Leon-Guerrero text and in Chapter 8, p. 256, consider the 2012 Benghazi Terrorist Attack Investigation and focus on how different levels of confidence and sample size work together.

Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of confidence intervals.

Use the Course Guide and Assignment Help found in this week’s Learning Resources to search for a quantitative article related to confidence intervals.

Using the SPSS software, General Social Survey dataset and choose a quantitative variable that interests you.

By Day 3

Using SPSS:

Take a random sample of 100.

Calculate the 95% confidence interval for the variable.

Calculate a 90% confidence interval.

Take another random sample of 400.

Calculate the 95% confidence interval for the variable.

Calculate a 90% confidence interval.

Post your results and an explanation of how different levels of confidence and sample size affect the width of the confidence interval. Next, consider the statement, “Confidence intervals are underutilized” and explain what the implications might be of using or not using confidence intervals. Provide examples based on the results of your data. Also, use your research to support your findings.

By Day 5

Respond to one of your colleague’s posts and explain how you might see the implications differently.

Week 5 discussion

Statistical Significance and Meaningfulness

Once you start to understand how exciting the world of statistics can be, it is tempting to fall into the trap of chasing statistical significance. That is, you may be tempted always to look for relationships that are statistically significant and believe they are valuable solely because of their significance. Although statistical hypothesis testing does help you evaluate claims, it is important to understand the limitations of statistical significance and to interpret the results within the context of the research and its pragmatic, “real world” application.

As a scholar-practitioner, it is important for you to understand that just because a hypothesis test indicates a relationship exists between an intervention and an outcome, there is a difference between groups, or there is a correlation between two constructs, it does not always provide a default measure for its importance. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice.

For this Discussion, you will explore statistical significance and meaningfulness.

To prepare for this Discussion:

Review the Learning Resources related to hypothesis testing, meaningfulness, and statistical significance.

Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of statistical power and significance testing.

Review the American Statistical Association’s press release and consider the misconceptions and misuse of p-values.

Consider the scenario:

A research paper claims a meaningful contribution to the literature based on finding statistically significant relationships between predictor and response variables. In the footnotes, you see the following statement, “given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level.”

By Day 3

Post your response to the scenario in which you critically evaluate this footnote. As a reader/reviewer, what response would you provide to the authors about this footnote?

By Day 5

Respond to at least one of your colleagues’ posts and explain the benefits and consequences of the “relaxed” level of significance.

Week 6 discussion

Research Design and t Tests: How Are They Connected?

The best way to solidify your understanding of statistical testing is to actually engage in performing some data analysis. This week you will work with a real, secondary dataset to construct a research question, perform a t test, and interpret the results.

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

Review the Learning Resources and the media programs related to t tests.

Using the SPSS software, open the High School Longitudinal Study dataset found in this week’s Learning Resources and construct a research question that involves a comparison of a means test.

By Day 3

Use SPSS to answer the research question you constructed and post your response to the following:

What is your research question?

What is the null hypothesis for your question?

What research design would align with this question?

What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)?

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?

Identify your research question and explain your results for a lay audience, what is the answer to your research question?

By Day 5

Respond to one of your colleagues’ posts and:

Make recommendations for the design choice.

Explain whether you think that this is the appropriate t test to use for the question. Why or why not?

As a lay reader, were you able to understand the results and their implications? Why or why not?

Week 7 discussion

Research Design for One-Way ANOVA

Similar to the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week, you will once again work with a real, secondary dataset to construct a research question, perform a one-way ANOVA, and interpret the results.

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

Review this week’s Learning Resources and media program related to one-way ANOVA testing.

Using the SPSS software, open the General Social Survey dataset found in this week’s Learning Resources.

Using the General Social Survey dataset, construct a research question that can be answered by a one-way ANOVA.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

What is your research question?

What is the null hypothesis for your question?

What research design 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?

Explain your results for a lay audience and further explain what the answer is to your research question.

By Day 5

Respond to at least one of your colleagues’ posts and respond based the following:

Do you think the variables are appropriately used? Why or why not?

Does the analysis answer the research question? Be sure to provide constructive and helpful comments for possible improvement.

If there was a significant effect, comment on the strength and its meaningfulness.

As a lay reader, were you able to understand the results and their implications? Why or why not?

Week 8 discussion

Correlation and Bivariate Regression

Similar to the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will once again work with a real, secondary dataset to construct a research question, perform a correlation and bivariate regression model, and interpret the results.

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

Review this week’s Learning Resources and media program related to regression and correlation.

Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of correlations between two variables.

Construct a research question using the General Social Survey dataset, which can be answered by a Pearson correlation and bivariate regression.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

What is your research question?

What is the null hypothesis for your question?

What research design 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?

Explain your results for a lay audience; explain the answer to your research question.

By Day 5

Respond to at least one of your colleagues’ posts and comment on the following:

Do you think the variables are appropriately used? Why or why not?

Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.

If there was a significant effect, comment on the strength and its meaningfulness.

As a lay reader, were you able to understand the results and their implications? Why or why not?

Week 9 discussion

Multiple Regression

As with the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will once again work with a real, secondary dataset to construct a research question, estimate a multiple regression model, and interpret the results.

To prepare for this Discussion:

Review this week’s Learning Resources and media program related to multiple regression.

Create a research question using the General Social Survey that can be answered by multiple regression.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

What is your research question?

What is the null hypothesis for your question?

What research design 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?

What other variables were added to the multiple regression models as controls?

What is the justification for adding the variables?

If you found significance, what is the strength of the effect?

Explain your results for a lay audience, explain what the answer to your research question.

By Day 5

Respond to at least one of your colleagues’ posts and comment on the following:

Do you think the variables are appropriately used? Why or why not?

Does the addition of the control variables make sense to you? Why or why not?

Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.

If there was a significant effect, comments on the strength and its meaningfulness.

As a lay reader, were you able to understand the results and their implications? Why or why not?

Week 10 discussion

Estimating Models Using Dummy Variables

You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.

To prepare for this Discussion:

Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.

Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.

By Day 3

Estimate a multiple regression model that answers your research question. Post your response to the following:

What is your research question?

Interpret the coefficients for the model, specifically commenting on the dummy variable.

Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?

By Day 5

Respond to at least one of your colleagues’ posts and provide a constructive comment on their assessment of diagnostics.

Were all assumptions tested for?

Are there some violations that the model might be robust against? Why or why not?

Explain and provide any additional resources (i.e., web links, articles, etc.) to provide your colleague with addressing diagnostic issues.

Week 11 discussion

Categorical Data Analysis

As with the previous week’s Discussion, this Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will once again work with a real, secondary dataset to construct a research question, perform categorical data analysis that answers the question, and interpret the results.

To prepare for this Discussion:

Review Chapters 10 and 11 of the Frankfort-Nachmias & Leon-Guerrero course text and the media program found in this week’s Learning Resources related to bivariate categorical tests.

Create a research question using the General Social Survey dataset that can be answered using categorical analysis.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

What is your research question?

What is the null hypothesis for your question?

What research design 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?

Explain your results for a lay audience and further explain what the answer is to your research question.

By Day 5

Respond to at least one of your colleagues’ posts and comment on the following:

Do you think the variables are appropriately used? Why or why not?

Does the analysis answer the research question? Be sure and provide constructive and helpful comments for possible improvement.

As a lay reader, were you able to understand the results and their implications? Why or why not?

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