RCH 8303, Quantitative Data Analysis 1
\nCourse Learning Outcomes for Unit IV
\nUpon completion of this unit, students should be able to:
\n1. Perform statistical tests using software tools.
\n1.1 Upload an appropriate data file.
\n1.2 Perform correlation using appropriate menu options.
\n1.3 Explain the purpose of the Spearman rank-order correlation.
\n2. Explain results of statistical tests.
\n2.1 Describe the differences between unstandardized and standardized data.
\n2.2 Discuss the differences between alternative hypotheses.
\n2.3 Elaborate on the effects of outliers on the correlation coefficient.
\n2.4 Describe the assumptions for correlation.
\n2.5 Contrast the differences between a paired sample t-test and an independent sample t-test.
\n3. Judge whether null hypotheses should be rejected or maintained.
\n3.1 Explain the differences between the null and alternative hypotheses, and perform option
\nselection.
\n3.2 Discuss what effect size is and how it is calculated.
\nCourse\/Unit
\nLearning Outcomes
\nLearning Activity
\n1.1, 1.2, 1.3, 3.1
\nUnit Lesson
\nChapter 6, pp. 127\u2013129
\nUnit IV Assignment 2
\n2.1, 2.2, 2.3, 2.4, 2.5,
\n3.2
\nUnit Lesson
\nUnit IV Assignment 1
\nRequired Unit Resources
\nChapter 6: Simple Statistical Tests, pp. 127\u2013129
\nUnit Lesson
\nUnit IV Plan
\nThe Unit IV assignment will be in two parts. Part 1 of your assignment requires you to complete modules of
\nthe CITI Program EOSA that relate directly to the reading in this unit. Each of the modules has a final quiz
\nthat must be completed and successfully passed, demonstrating your knowledge of basic statistics and the
\nresearch process.
\nFor Part 2, you will review how to conduct a correlation and determine whether the test is statistically
\nsignificant or not.
\nThere are two topic (sample nursing essay examples by the best nursing assignment writing service)s in the Unit IV CITI EOSA course.
\nCorrelations (ID 17632): This module describes and explains the extent of relationships between two
\nvariables. It explains in detail the two different types of tests: Pearson product-moment correlation for
\nparametric data and Spearman rank-order correlation for nonparametric data.
\nUNIT IV STUDY GUIDE
\nCorrelation
\nRCH 8303, Quantitative Data Analysis 2
\nUNIT x STUDY GUIDE
\nTitle
\nComparing Correlation Coefficients (ID 17633): This module describes how to conduct the appropriate
\nanalysis to compare two correlation coefficients. This module is important to understanding the different types
\nof comparisons.
\nWhat is Correlation?
\nUnit IV starts with a different type of outcome form of testing. The focus of Unit IV is correlation, which is a
\nmethodology that tests to determine whether there is a relationship between variables. An example of this
\ncould be whether there is a relationship between the two variables\u2014cigarette smoking and the rate of throat
\ncancer incidence. A research question could be framed; Is cigarette smoking related to likelihood of throat
\ncancer?
\nWith correlation, you are trying to find out if there is any relationship between your variables. Typically, you
\nwill have two variables, and you will want to see if there is any relationship between the two. This can be a
\npositive or even a negative relationship. Important to note, though, that this is not a causal relationship. For
\nthis test, our research question and hypotheses could be written as:
\nRQ: What is the relationship between a person\u2019s weight and a person\u2019s runtimes?
\nH0: There is no relationship between a person\u2019s weight and a person\u2019s runtimes.
\nHA: There is a relationship between a person\u2019s weight and a person\u2019s runtimes.
\nR and R Commander make it very easy to conduct simple statistical tests. If you are not comfortable utilizing
\nR and R commander you may use whatever statistical software program you choose. The answers you
\nsubmit for your assignment must be correct regardless of the software you choose.
\nAs noted in Unit III, once data are collected, a researcher needs to be able to describe, summarize, and,
\npotentially, detect patterns in the data they have recorded with meaningful numerical scales such as
\nhistograms. After reviewing the data, decisions must be made regarding whether the assumptions of the
\nparticular test have been met. If they have, then conducting of the test can proceed. Reviewing these two
\ntutorials, Homogeneity of Variances and Testing for Normality, will be very helpful to you.
\nPrior to conducting any statistical test though, the researcher must first meet the assumptions of the particular
\ntest.
\nFor an example of a correlation test, make sure when you access R that you also load R Commander. Type
\nin library(Rcmdr) or see Unit I for a refresher on how to gain access to R Commander. Once R and R
\nCommander have been loaded, the next step is to load the data set wtruntime that will be used (Figure 1).
\nRCH 8303, Quantitative Data Analysis 3
\nUNIT x STUDY GUIDE
\nTitle
\nFigure 1
\nData Set Wtruntime Successfully Uploaded
\nRCH 8303, Quantitative Data Analysis 4
\nUNIT x STUDY GUIDE
\nTitle
\nViewing the data set allows a user to examine the type of category information and numeric values (Figure 2).
\nFigure 2
\nVisual Representation of Wtruntime Data Set
\nRCH 8303, Quantitative Data Analysis 5
\nUNIT x STUDY GUIDE
\nTitle
\nThe next step is to view the data set in a graphical form. This will allow us the opportunity to view the data set
\nin graphical display. Select Graphs and then Histogram (Figure 3).
\nFigure 3
\nHistogram Selection Menu
\nRCH 8303, Quantitative Data Analysis 6
\nUNIT x STUDY GUIDE
\nTitle
\nNext step is to select which variable (Figure 4) to view in a histogram. Since we have two in this data set, we
\nwill do this for each one.
\nFigure 4
\nVariable Selection Menu: Weight Selection
\nRCH 8303, Quantitative Data Analysis 7
\nUNIT x STUDY GUIDE
\nTitle
\nVariable Weight Histogram (Figure 5) is shown below.
\nFigure 5
\nVariable Weight Histogram
\nRCH 8303, Quantitative Data Analysis 8
\nUNIT x STUDY GUIDE
\nTitle
\nVariable Runtimes Histogram (Figure 6) is shown below.
\nFigure 6
\nVariable Runtimes Histogram
\nRCH 8303, Quantitative Data Analysis 9
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\nOur next step is to view the numerical summaries of our data (Figure 7).
\nFigure 7
\nNumerical Summaries of Data Selection Menu
\nRCH 8303, Quantitative Data Analysis 10
\nUNIT x STUDY GUIDE
\nTitle
\nNext, since we have two variables, selecting both of them will summarize both data sets (Figure 8).
\nFigure 8
\nVariable Selection Menu
\nRCH 8303, Quantitative Data Analysis 11
\nUNIT x STUDY GUIDE
\nTitle
\nSelecting the Statistics tab displays options available to user. Selecting Skewness and Kurtosis will display
\nthese numbers as well (Figure 9).
\nFigure 9
\nNumerical Display Options Menu
\nFigure 1
\nRCH 8303, Quantitative Data Analysis 12
\nUNIT x STUDY GUIDE
\nTitle
\nNumerical display output (Figure 10) is shown below.
\nFigure 10
\nNumerical Display Output
\nRCH 8303, Quantitative Data Analysis 13
\nUNIT x STUDY GUIDE
\nTitle
\nThe next step is to determine whether the data are normally distributed. Assessing a variable\u2019s distribution
\ncan help determine which of the correlation tests can be used (Figure 11).
\nFigure 11
\nTesting for Normality Selection
\nRCH 8303, Quantitative Data Analysis 14
\nUNIT x STUDY GUIDE
\nTitle
\nThis displays a menu (Figure 12) of which variables to use. Since we will check each variable, we will start
\nwith the Weight variable first and use the Anderson-Darling Test of Normality.
\nFigure 12
\nWeight Variable Test of Normality Options Menu
\nRCH 8303, Quantitative Data Analysis 15
\nUNIT x STUDY GUIDE
\nTitle
\nWeight Variable Test of Normality output display (Figure 13) is shown below.
\nFigure 13
\nWeight Variable Test of Normality Output
\nSince we have failed to reject the null hypothesis, this indicates there is no difference between the data\u2019s
\ndistribution and a theoretical normal distribution. For more information, please review the tutorials located in
\nthe dissertation center about this topic (sample nursing essay examples by the best nursing assignment writing service).
\nRCH 8303, Quantitative Data Analysis 16
\nUNIT x STUDY GUIDE
\nTitle
\nRuntimes Variable Test of Normality selection menu (Figure 14) is shown below.
\nFigure 14
\nRuntimes Variable Test of Normality Selection Menu
\nRCH 8303, Quantitative Data Analysis 17
\nUNIT x STUDY GUIDE
\nTitle
\nRuntimes Variable Test of Normality output display (Figure 15) is shown below.
\nFigure 15
\nRuntimes Variable Test of Normality Output Display
\nSince we have failed to reject the null hypothesis indicating there is no difference, the data are normally
\ndistributed. For more information, please review the tutorials Homogeneity of Variances and Testing for
\nNormality.
\nRCH 8303, Quantitative Data Analysis 18
\nUNIT x STUDY GUIDE
\nTitle
\nThe next graph to view is a scatterplot of both variables to see how well they fit the line (Figure 16).
\nFigure 16
\nScatterplot Selection Menu
\nRCH 8303, Quantitative Data Analysis 19
\nUNIT x STUDY GUIDE
\nTitle
\nNext, select which will be the x and y variables (Figure 17).
\nFigure 17
\nScatterplot Variable Selection Menu
\nRCH 8303, Quantitative Data Analysis 20
\nUNIT x STUDY GUIDE
\nTitle
\nScatterplot results (Figure 18) are shown below.
\nFigure 18
\nScatterplot Results
\nThe scatterplot results in a good display of the data points. There are six that are clearly not on the line.
\nRCH 8303, Quantitative Data Analysis 21
\nUNIT x STUDY GUIDE
\nTitle
\nOur next step is to run the correlation to determine the strength of the relationship between the weight and
\nruntimes variables (Figure 19).
\nFigure 19
\nCorrelation Selection Menu
\nRCH 8303, Quantitative Data Analysis 22
\nUNIT x STUDY GUIDE
\nTitle
\nSince our data are normally distributed and are continuous variables, we can use the Pearson productmoment test. Selecting both variables and checking to make sure the Two-sided test is selected will allow us
\nto run the test (Figure 20).
\nFigure 20
\nCorrelation Test Options menu
\nRCH 8303, Quantitative Data Analysis 23
\nUNIT x STUDY GUIDE
\nTitle
\nFigure 21
\nCorrelation Test Output Display
\nThe results of the test are statistically significant (p-value = 0.000001806). The test has a Pearson r of
\n.8525839 indicating a positive relationship. One could write these results as:
\nA test of the relationship between a person\u2019s weight and their runtimes was performed. Both variables
\nwere considered normally distributed. The Pearson Product-Moment Correlation Coefficient (r) was
\nselected as the test statistic. The result of the test was significant, r(18) = .853, 95% CI (.659, .940), p
\n< .001.
\nIn conclusion, the correlation test discussed in this unit is used to determine whether there is a relationship
\nbetween variables. Unit V will focus on t-tests and the different types of t-tests and the type of data needed to
\nrun the analyses.
\nRCH 8303, Quantitative Data Analysis 24
\nUNIT x STUDY GUIDE
\nTitle
\nLearning Activities (Nongraded)
\nNongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
\nthem. If you have questions, contact your instructor for further guidance and information.
\nWhen studying APA formatting, pay particular attention to the sections that pertain to formatting for research
\nand statistics.<\/p>\n","protected":false},"excerpt":{"rendered":"
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