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# SPSS Experimental Design Analysis

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1. An experiment was conducted to study the effects of different types of background music on the productivity of bank tellers. Two factors were studied, tempo of the music (A; slow, medium, and fast) and the style of music (B; instrumental, or vocal). For each combination of the two factors 4 branches of the bank were randomly selected, and 5 tellers were randomly sampled within each branch. Then a productivity measure was calculated for each teller at the end of a one-week period. The data is in the file teller.csv

Analyze this data to determine if there is any difference in the mean productivity of tellers by music tempo and/or type. Compare each treatment combination (e.g., A_1 B_2) with every other treatment combination using an appropriate method to handle the family-wise error rate. Produce any relevant figures that you feel might help explain the relationships present in the data.

2. A test preparation company is interested in comparing two preparation program (on-line versus in-person) on how well they prepare students for an admissions test. Ten subjects were randomly assignet to one of the two preparation programs and measured at the end of each of 4 weeks. The data is presented in the table below: (see attached).

Use this data to determine if there is a significant difference in test scores by test preparation method. Describe the tren in test scores over the four weeks of the study (e.g., is it linear, quadratic, etc.). Is the trend the same for online and in-person test preparation programs? Use your proposed model to determine if there are significant differences in the mean scores of In-person and online participants at Week 4. Produce relevant plots to help describe your analysis and to evaluate the assumptions of the model.

3. The data set Reading.csv includes data from a study comparing three different reading curricula for third grade students: (1) comprehension-focused; (2) decoding-focused; and (3) a balanced curriculum. In particular the study was interested in how students' comprehension of reading passages differed across the three curricula. Five schools were randomly selected for each of the three curricula (15 schools in total). After administering the curricula over the course of a year the students' state reading test scores were collected. The data set Reading.csv contains the following information:

- Column 1: Treatment conditions: (1) comprehension-based; (2) decoding-focused; and (3) balanced.

- Column 2: School identifier - this is number between 1 & 5 indicating which school in the treatment condition the student belongs to.

- Column 3: Class identifier - a number between 1 & 3 indicating which classroom within the school a student belongs to.

- Column 4: Student identifier - a number between 1 & 25 indicating the students ID number within the classroom.

- Column 5: Parent's Education - a categorical variable indicating the highest level of education a students parents' achieved: (1) less than high school; (2) some high school; (3) graduated high school/GED; (4) some college; (5) graduated college; (6) post-graduate study.

- Column 6: State test score.

Use the data to determine if there are significant differences between the three reading curricula, and compare them with an appropriate post hoc procedure. Your analysis should attempt to use the student's classroom and their parent's education as a covariate to explain some variation in the outcome. As part fo the analysis you should construct a contrast to test whether the men test score for the balanced group is significantly different from the average of the comprehension - and decoding - focused curriculum means.

https://brainmass.com/statistics/designing-studies/spss-experimental-design-analysis-519066

#### Solution Summary

This solution provides the appropriate statistical analysis with calculations, tables, graphs and explanation for the reading, teller and repeated measure problems. This solution is provided in an attached .zip file, with the report formatted in an attached Word document and .sav and .spv output files for each of the three tests are included.

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## Color-Distance Illusion Analysis

Having trouble entering data it is ok to use excel.

SPSS t-Tests (Independent Samples and Paired Samples)

Scenario:
The color-distance illusion (Kantowitz, Roediger, & Elmes, 1997) suggests that in art, warm colors (yellow, orange, red, and blends of these colors) appear to move toward the viewer when viewed from a distance, while cool colors (blue, green) seem to recede or move further away from the viewer when viewed from a distance. Suppose a researcher wanted to determine if it is true. The researcher could set up the following experimental design to test this:
Suspend one painting completed with red, yellow and/or orange WARM colors from the ceiling near the far end of a long room. Have participants stand against the wall at the opposite end of the room and make judgments regarding the distance of the painting from them. Suspend a second painting completed with blue and/or green COOL colors from the same area of the ceiling near the far end of the same long room. Have participants stand against the wall at the opposite end of the room and make judgments regarding the distance of the painting from them.
Ways in which this experiment would be controlled: same long room would be used (30 feet in length) and the painting would hang from same location in room (24 feet from the opposite wall). Also, participants would stand in same spot in room from which to make distance judgments (24 feet from the painting).

The data for this experiment can be carried out and analyzed with either a Between-Subjects Design or a Within-Subjects Design.

I. Use the following measures to set up both data sets - one data set using a Between-Subjects set-up (hint - will have 20 participants), and one data set using a Within-Subjects set-up (hint - will have 10 participants):

When participants judged the distance of the WARM paintings, they gave (from 1st to last participant) the following judgments of distance (in feet):

10, 15, 19, 16, 20, 11, 14, 9, 13, 19

When participants judged the distance of the COOL paintings, they gave (from 1st to last participant) the following judgments of distance (in feet):

30, 22, 25, 23, 27, 29, 21, 24, 24, 27

More hints:

For the Between-Subjects dataset:

You will set up one column for the Independent Variable (e.g., "type of painting") and then identify the different levels of this Independent Variable under the "variable view" screen as "values" (e.g., "warm" and "cool").

Next, you will set up one column for the Dependent Variable (e.g., "distance"). Then, for each participant, and you'll enter the value of the dependent variable attributable to the participant in the Dependent Variable column.

For the Within-Subjects dataset:

You will set up one column for each Independent Variable LEVEL (e.g., "warm" and "cool") that your participant is experiencing. Then, you will enter the DEPENDENT variable data (their ratings) for each participant in the appropriate LEVEL column.

II. Run the appropriate t-Tests (either a Paired-Samples t-Test or an Independent Samples t-Test under "Analyze" &#61664; "Compare Means") based on the set-up used for each data set. Remember, you will be running BOTH t-Tests.

III. Print out the output for each analysis and indicate where to locate the response to each question as well as filling in below.

1.) The means for each level of the Independent Variable

2.) The standard deviation of each level of the Independent Variable

3.) The t values and degrees of freedoms for the t-test performed for each data set

4.) What is the p-value or significance level calculated for each test and is that p-value significant or non-significant?

IV. Create a BOXPLOT for the Between-Subjects design (Hint: variable - distance and category axis - type of painting painting) and attach. Please label values.

V. Report your findings for each t-test below. Make sure to include if the t-test was significant and at what level (p-value).

VI. Interpretation of SPSS data.

Scenario: A teacher was interested in knowing which students preferred different snack options and if the snack options had a relationship with favorite activity. So, the teacher asked the students what their favorite snacks were. She separated the snacks into three groups (fruit, granola bars, and chocolate). She then asked students what their favorite activity was. She separated the activities into four groups (playing sports, watching TV, playing video games, and reading a book).

1) The teacher conducted a Between-Subjects t-test using snack option as the independent variable and activity as the dependent variable. She decided to look at students who ate either chocolate or fruit. Here is the output.

What is the t-test value ?
What is the significance level ?

What do the results of this t-test indicate?

2) Second, the teacher conducted a t-test of independent samples to test if there was a difference between the activities students choose based on students who reported their favorite snack was fruit compared to those who said their favorite snack was a granola bar. Here is the output.

What is the t-test value ?
What is the significance level?

What do the results of this t-test indicate?

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