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Give a real-world situation or problem; clearly identify the independent and dependent variables; give an explanation of whether an independent-samples or related-samples t test is most appropriate; the statistical null and alternative hypotheses; and describe what information the effect size would tell you that the probability value would not tell you, and your hypothetical results using correct APA format. Solution T-Test Example The two sample t-test simply tests whether or not two independent populations have different mean values on some measure. For example, we might have a research hypothesis that rich people have a different quality of life than poor people. We give a questionnaire that measures quality of life to a random sample of rich people and a random sample of poor people. The null hypothesis, which is assumed to be true until proven wrong, is that there is really no difference between these two populations. We gather some sample data and observe that the two groups have different average scores. But does this represent a real difference between the two populations, or just a chance difference in our samples? T-Test Statistic The statistics t-test allows us to answer this question by using the t-test statistic to determine a p-value that indicates how likely we could have gotten these results by chance. By convention, if there is less than 5% chance of getting the observed differences by chance, we reject the null hypothesis and say we found a statistically significant difference between the two groups. See Statistical Data Analysis for more information about hypothesis testing. http://faculty.vassar.edu/lowry/ch12pt1.html.

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Topic: Learning Team Number of Pages: 2 (Double Spaced) Number of sources: 1 Writing Style: APA Type of document: Essay Academic Level:Master Category: Psychology VIP Support: N/A Language Style: English (U.S.) Order Instructions: I will attach the instruction. On this paper please follow the instructions carefully. Thank you Correlation PSYCH/610 Version 2 1 University of Phoenix Material Correlation A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data: Viewing Time in Seconds Preference Rating 10 3 12 4 24 7 5 3 16 6 3 4 11 4 5 2 21 8 23 9 9 5 3 3 17 5 14 6 What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor. LEARNING OBJECTIVES · Explain how researchers use inferential statistics to evaluate sample data. · Distinguish between the null hypothesis and the research hypothesis. · Discuss probability in statistical inference, including the meaning of statistical significance. · Describe the t test and explain the difference between one-tailed and two-tailed tests. · Describe the F test, including systematic variance and error variance. · Describe what a confidence interval tells you about your data. · Distinguish between Type I and Type II errors. · Discuss the factors that influence the probability of a Type II error. · Discuss the reasons a researcher may obtain nonsignificant results. · Define power of a statistical test. · Describe the criteria for selecting an appropriate statistical test. Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so. SAMPLES AND POPULATIONS Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.

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BUS 308 Week 3 Lecture 1 Examining Differences - Continued Expected Outcomes After reading this lecture, the student should be familiar with: 1. Issues around multiple testing 2. The basics of the Analysis of Variance test 3. Determining significant differences between group means 4. The basics of the Chi Square Distribution. Overview Last week, we found out ways to examine differences between a measure taken on two groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample test situation). We looked at the F test which let us test for variance equality. We also looked at the t-test which focused on testing for mean equality. We noted that the t-test had three distinct versions, one for groups that had equal variances, one for groups that had unequal variances, and one for data that was paired (two measures on the same subject, such as salary and midpoint for each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel to perform a one-sample mean test against a standard or constant value. This week we expand our tool kit to let us compare multiple groups for similar mean values. A second tool will let us look at how data values are distributed – if graphed, would they look the same? Different shapes or patterns often means the data sets differ in significant ways that can help explain results. Multiple Groups As interesting as comparing two groups is, often it is a bit limiting as to what it tells us. One obvious issue that we are missing in the comparisons made last week was equal work. This idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions arise about things such as performance appraisal ratings, education distribution, seniority impact, etc. Some of these can be tested with the tools introduced last week. We can see, for example, if the performance rating average is the same for each gender. What we couldn’t do, at this point however, is see if performance ratings differ by grade, do the more senior workers perform relatively better? Is there a difference between ratings for each gender by grade level? The same questions can be asked about seniority impact. This week will give us tools to expand how we look at the clues hidden within the data set about equal pay for equal work. ANOVA So, let’s start taking a look at these questions. The first tool for this week is the Analysis of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes variance (which it does) but the purpose of an ANOVA test is to determine if the means of different groups are the same! Now, so far, we have considered means and variance to be two distinct characteristics of data sets; characteristics that are not related, yet here we are saying that looking at one will give us insight into the other. The reason is due to the way the variance is an.

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Section 1: Data File Description The fictional data represents a teacher's recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of 35 students which totals to 105 students (sample size, N = 105). The dataset has 21 variables but in this case only two variables will be analyzed. These are: gender and gpa variables. The gender variable is categorized as nominal since the numbers are arbitrarily assigned to represent group membership. The ‘gpa’ variable belongs to the interval data group since it has a true zero which is meaningful. Alternatively, gender could be categorized as a categorical variable while ‘gpa’ ‘as a continuous variable. Section 2: Testing Assumptions 1. Articulate the assumptions of the statistical test. Paste SPSS output that tests those assumptions and interpret them. Properly integrate SPSS output wher1e appropriate. Do not string all output together at the beginning of the section. All statistical tests operate under a set of assumptions. For the t test, there are three assumptions: · The first assumption is independence of observations. · The outcome variable Y is normally distributed. · The variance of Y scores is approximately equal across groups (homogeneity of variance assumption) Figure 1: histogram of GPA The histogram above shows that the variable is probably not normally distributed. The bell shape is absent and two peaks are evident. Table 1: descriptives Descriptives Statistic Std. Error GPA Mean 2,78 ,075 95% Confidence Interval for Mean Lower Bound 2,63 Upper Bound 2,93 5% Trimmed Mean 2,80 Median 2,72 Variance ,583 Std. Deviation ,764 Minimum 1 Maximum 4 Range 3 Interquartile Range 1 Skewness -,052 ,236 Kurtosis -,811 ,467 With reference to the table 1 above, the ‘GPA’ variable is in the ideal range for skewness due to the fact that its absolute value for skewness are is less than .50 (approximately symmetric). The GPA variable is not ideal but acceptable since its kurtosis value is greater than .50 but less than 1. This new information gives mixed signals about the data being normal and only a normality test could iron out the differences. Table 2: Normality test Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. GPA ,091 105 ,033 ,956 105 ,001 a. Lilliefors Significance Correction Looking at the table above, the p-value is less than 0.05. Therefore, the null hypothesis is rejected and thus it can be concluded that the variable is not normally distributed. However, since the sample size is sufficiently large, one does not need to worry about this violation. On the other hand, Levene’s test provides a p =0.566 (table 3) meaning that the null hypothesis should not be rejected. Thus the homogeneity of variances assumption is not violated. It’s also assumed that proper research procedures that maintain independence of observations were followed. Two of the th.

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