Spy 315 Week 4 Worksheet

Week 4

Chapter 7, 8, & 9 Instructions

Due Week 4 Day 6 (Sunday)

Follow the instructions below to submit your answers for Chapter 7, 8, & 9 Practice Problems.

1. Save these instructions to your computer.

1. Type your answers into the shaded boxes below. The boxes will expand as you type your answers.

1. Each shaded box is worth 1 point unless otherwise noted, thus, 16 points total for Chapter 7, 8, & 9.

1. Resave this form to your computer with your answers filled in.

1. Upload the saved form to the Assignment Files tab.

Read Chapter 7 Practice Problem 26 in your text book and then type your answers into the shaded boxes below. Explain the results of any three rows from Table 7-15 on page 270. In your answer, please indicate the row first and then provide the explanation. For example, if you are interested in discussing the first row, you would begin by typing “Important” into the shaded box and then begin your explanation of the results. Given that the person to whom you are explaining this problem is not aware of t tests, a brief discussion of the nature of t tests is given below.

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Practice problem 26 presents significant or non-significant results in terms of t tests, a new concept in the text that requires a brief explanation. A key to understanding the t test is to recall an essential component of a Z test; namely, that a Z test is used when the population variance is known. Also, the shape of the population distribution follows a normal, symetrical curve where the left and right side of the curve are the same. Likewise, the Z tables provide exact percentages of the curve for each Z score. Unlike a Z test that is used when the population variance is known, the t test is used when the population variance is unknown. In a t test, the population variance must be estimated from sample data. Sample data provide less information from which to estimate population parameters, thus, creating more room for error in the estimate. Further, samples usually have more extreme means than an exact normal curve; therefore, the comparison distribution for a t test will not be a normal curve, but a slightly different curve called a t distribution. T distributions are similar to a normal curve in that they are bell-shaped, symmetrical, and unimodal, but are slightly more spread out than the normal curve and have more scores in the extreme tails. Aron, Coups, & Aron tells us that, ". . . it takes a sightly more extreme sample mean to get a significant result when using a t distribution than when using a normal curve" (2013, p. 232). Signficance or non-significance in Table 7-15 can be found in the last column labeled "Work vs. Home" where all t scores for the study are reported.

Aron, A., Coups, E., & Aron, E. (2013). Statistics for Psychology (6th ed.). Upper Saddle River, NJ: Pearson.

26. Type your answers for any three rows in the shaded boxes below.

Please scroll down for Chapter 8.

Chapter 8 Instructions

Read Chapter 8 Practice Problem 12 in your text book and then type your answers into the shaded box below. A brief discussion of a t test for independent means is given below.

Chapter 7 introduced a 't' test for a single sample where the single sample is compared to a population with a known mean, but an unknown variance. In Chapter 8, a 't' test for independent means is introduced where two samples of different participants are measured on some variable generating two groups of scores, one score for each person in the first group and one score for each person in the second group. Each score in the first group is independent of all other scores in the first group, and each score in the second group is independent of all other scores in the second group. Additionally, the collection of scores in the first group are independent of the collection of scores in the second group. It is not practical to compare each score in the first group with each score in the second group, so a more efficient method has been devised where the mean in the first group is compared to the mean in the second group; hence, the name, 't' test for independent means. Since we have two independent groups for which a population variance is unknown, we can estimate the population variance from sample data using appropriate statistical procedures. Both estimates can then be pooled to make one popultion variance. The pooled variance is used to determine the variance of the distribution of differences between means, and then the standard deviation of the distribution of differences between means. Finally, the 't' score is figured via a formula and compared to the 't' cutoff score for significance or non-significance.

12. Practice Problem 12 is worth 5 points.

Read Chapter 8 Practice Problem 14 in your text book and then type your answers into the shaded boxes below.

14b.      

14c.      

Please scroll down for Chapter 9.

Chapter 9 Instructions

Read Chapter 9 Practice Problem 23 in your text book and then type your answer into the shaded box below. Problem 23 involves analyzing more than two groups of scores. The statistical procedure for doing so is called an analysis of variance or ANOVA. A brief introduction to the logic of analysis of variance is given below.

To determine the significance between means of two groups, a t test is carried out. If the variation among the means of more than two groups is needed, the statistical procedure is an analysis of variance. For the purpose of problem 23, each group has the same number of scores. The null hypothesis in an analysis of variance is that all populations being compared have the same mean. The alternative hypothesis is that the mean is different in all populations being compared. To determine significance or non-significance, it must be determined if the means of the samples differ more than expected if the null hypothesis is true.

23. Please briefly discuss each of the four scales in Table 9-17.

Read Chapter 9 Practice Problem 24 in your text book and then type your answer into the shaded box below.

24a.