human-computer interaction (HCI) researcher was interested in examining whether humans are better able to use a joystick or a mouse to point a computer cursor.

Statistical Methods II Final Assignment

Overview

Submit a single word-processed document using appropriate word-processing software (e.g., Word, LibreOffice, LATEX, R Markdown). Embed all images in the document. Please submit the document in .doc, .docx, .odt, or .pdf file format (.pdf is preferred). Submit the document using the Final Assignment submission portal on the course eClass page. All data required to complete the assignment are available in text format throughout this document. To aid students who opt to use statistical software, all the text-formatted data in

this document have been duplicated into a series of .csv files also located in the Final Assignment folder. Each .csv file is intuitively labeled according to the Long Answer question for which it is intended (e.g., file “Q1.csv” contains the data for Long Answer question 1).

 Long Answer (70 marks)

For these questions, you are given brief summaries of research scenarios, specific hypotheses, and accompanying data. You are required to conduct appropriate statistical analyses of the data to draw conclusions for each research hypothesis. Write a brief report of the results in which you

·  Highlight all inferential statistical effects (e.g., There was a strong main effect of Factor A)

·  Discuss the qualitative nature of any such effects (e.g., Group A scored higher on

than group B)

Report the results of your statistical tests using the appropriate APA formatting for each respective statistical test (e.g., t[12] = 2.53, p = .026, d = 0.74, 95% CI = [1.34, 4.78])

Refer back to the research scenario to make a broad declarative statement about how well the the research hypothesis is supported 

For all analyses, assume two-tailed hypothesis tests with α = .05 unless otherwise specified. All parametric analyses must include effect sizes. Any t-tests must include 95% confidence intervals. Any analysis that includes an F-test must also include an accompanying ANOVA table. Any analysis that includes a correlation must also include the variances of the variables and their cross product (SP; covariability). Any analysis that includes linear regression must also include all relevant descriptive statistics (i.e., SSResidual, standard error of the residual, regression equation). Any non-parametric analysis must include the ranked scores and the sum of ranks (ΣR).

You are free to use statistical software (e.g., SPSS, SAS, R, sciPy, MATLAB) to conduct statistical analyses and construct data plots. However, you must provide screenshots of your output as part of your submission. Please embed these images in the word-processed document, rather than submitting them separately. If you would prefer to compute

 

If you opt to use statistical software, please report exact p values for each statistical test (e.g., p = .026). Otherwise, if you perform hand calculations, report p values in relation to (e.g., less than) the appropriate α-level (e.g., α = .05) and include the critical values from the relevant statistical tables provided in the textbook for each statistical test (e.g., p < .05; tcrit = 2.18).

 Question 1

(24 marks)

The human-computer interaction (HCI) researcher was interested in examining whether humans are better able to use a joystick or a mouse to point a computer cursor. She therefore constructed an experiment in which participants used either a joystick (joystick condition) or a mouse (mouse condition) to point a cursor to a target displayed on a computer monitor. She measured the time (seconds) that it took to place the cursor over the target (dependent variable referred to as time, where a higher time score indicates poorer performance). To determine whether potential benefits of using the joystick or mouse generalized to difficult HCI scenarios, the target was either stationary (static condition) or moved slowly across the computer screen at a constant velocity (motion condition). Participants were randomly and uniquely assigned to 1 of 4 conditions in the interface (levels: joystick, mouse) × target type (levels: static, motion) experimental design. Given the data collected by the researcher (see Table 1 below), what can she conclude about how easily humans interact these HCI interfaces and how are these HCI interfaces influenced by target type? Include a line graph of the means.

Subject	Interface	Target	Time
1	Joystick	Static	2.53
2	Joystick	Static	2.24
3	Joystick	Static	3.16
4	Joystick	Static	1.50
5	Joystick	Static	3.06
6	Joystick	Static	2.76
7	Joystick	Static	2.57
8	Joystick	Static	4.03
9	Joystick	Static	1.70
10	Joystick	Static	0.70
11	Joystick	Motion	2.00
12	Joystick	Motion	2.54
13	Joystick	Motion	2.23
14	Joystick	Motion	0.10
15	Joystick	Motion	0.75
16	Joystick	Motion	1.30
17	Joystick	Motion	2.11
18	Joystick	Motion	2.60
19	Joystick	Motion	1.05
20	Joystick	Motion	2.05
21	Mouse	Static	1.39
22	Mouse	Static	0.67
23	Mouse	Static	1.62
24	Mouse	Static	1.69
25	Mouse	Static	0.29
26	Mouse	Static	2.29
27	Mouse	Static	0.93
28	Mouse	Static	1.32
29	Mouse	Static	0.33
30	Mouse	Static	1.67
31	Mouse	Motion	1.35
32	Mouse	Motion	1.29
33	Mouse	Motion	1.31
34	Mouse	Motion	2.48
35	Mouse	Motion	1.83
36	Mouse	Motion	1.03
37	Mouse	Motion	2.18
38	Mouse	Motion	2.01
39	Mouse	Motion	1.79

(11 marks)

A marketing team wants to determine which of two prospective product lines consumers might prefer. They randomly select subjects to participate in a quantitative focus group in which half of the participants were given product A, while the other half of participants were given product B. Participants completed a questionnaire that probed their affinity for the product they inspected during the focus group. Questionnaires items were collapsed into a continuous-valued composite index of product affinity. In a preliminary analysis, the marketing team ensured that the assumption of homogeneity of variance was met:

Given the product affinity data they collected (see Table 2 below), which product are consumers more likely to prefer? Show all relevant descriptive statistics.

  Table 2. Product affinity scores for Product A and product B.

   Subject     Product      Scores   

1	A	3.27
2	A	2.52
3	A	4.83
4	A	0.68
5	A	4.59
6	A	3.84
7	A	3.35
8	A	7.00
9	A	1.19
10	A	–1.32
11	B	2.69
12	B	4.04
13	B	3.26
14	B	–2.08
15	B	–0.44
16	B	0.93
17	B	2.97
18	B	4.17
19	B	0.30

20             B                           2.80     

(18 marks)

Sociologists investigated whether there is an association between salary and life enjoyment. They administered questionnaires to randomly selected participants who reported their salary in thousands of dollars (salary) and a battery of questionnaire items that probe life enjoyment. The life enjoyment items were collapsed into a continuous- valued composite index of life enjoyment (LE). Given their data (see Table 3 below), is there an association between salary and life enjoyment? If so, how does a change in salary quantitatively relate to a change in life enjoyment? Include a scatterplot of the data and the line of best fit. 

  Table 3. Salary in thousands of dollars (salary) and life enjoyment composite index (LE).

  Subject    salary        LE     

1	29	24
2	25	13
3	37	30
4	16	21
5	35	13
6	32	36
7	29	18
8	48	32
9	18	5
10	6	16
11	28	14
12	35	16
13	31	14
14	5	16
15	13	12
16	20	5
17	30	25
18	36	26
19	17	13

20           29                20     

(10 marks)

Cognitive psychologists are examining the effect of visuospatial cueing on perceptual processing speed. They designed an experiment in which a square randomly appeared on either the left or right side of a computer monitor and participants were required to push a button as soon as they detected the square. Each participant repeated this action hundreds of times. On half of the trials, a quick flash of light preceded the appearance of the square and it always appeared on the same side of the computer monitor as the square (Cued condition). On the other half of the trials, no such flash occurred (Control condition). The researchers measured the average time it took participants to detect the square on trials in both the Cued and Control conditions (see Table 4 below).

What can the researchers conclude about the effect of visuospatial cueing on perceptual processing speed? Show all relevant descriptive statistics. 

  Table 4. Mean subject reaction times (milliseconds) in the Cued and Control conditions.

           Condition          

   Subject                  Cued           Control

1	204	218
2	200	225
3	212	221
4	191	195
5	210	203
6	207	210
7	204	220
8	223	226
9	193	207

       10                      181                      219