I am stuck on question 5 of "B. Data Analysis" and questions 1-5 of C. Post-DataAnalysis.

Thanks

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This is an application of bivariate correlation and involves a fictitious replication of Bell, Robertson, and Wagner's (1995) study, which examined the relationship between pilot experience and aviation safety. Bell et al. defined pilot experience as pilots' total flight hours in all aircraft, and they defined aviation safety as scores on the Aviation Safety Index (ASI), which consisted of five safety-related statements that correspond to behaviors associated with preflight preparation. The five statements are given below.

1. I do a thorough walk-around inspection of an aircraft before I fly it.

2. I check the weather thoroughly before I fly (even on local flight).

3. I compute fuel requirements with an eye toward a 30- or 45-minute fuel reserve.

4. I use a checklist for interior and exterior inspection of an aircraft.

5. I compute takeoff and landing distances as well as runway lengths at airports of intended use for each flight.

Respondents were asked to self-assess the degree to which they performed each behavior using a 7-item bipolar adjective scale with "Never" at one end of the scale, which was scored 1, and "Always" at the other end of the scale, which was scored 7. Thus, aggregate scores could range from 5 to 35, with higher scores indicating more safety conscious pilots. A fictitious set of data related to this study is given in the Excel file "Data R." Import this data set into your statistical software program and do the following.

A. Pre-DataAnalysis

1. What is the research question and corresponding operational definitions?

• What is the relationship between pilot experience and aviation safety?
• Correlational research design. This method investigates relationships between variables without the researcher controlling or manipulating any of them.

2. Whatis the research methodology/design andwhyis this methodology appropriate

• Correlational research design.
• This method investigates relationships between variables without the researcher controlling or manipulating any of them. In this case pilot flight experience (flight hours) and aviation safety index measured using a 7-item bipolar adjective scale.

3. Conduct an a priori power analysis to determine the minimum sample size needed. Is

thegiven data set sufficient relative to this minimum sample size? In what way do you

thinkthesizeof the given samplewillimpact the results?

• The a priori power analysis produced a sample size of 67. The data set provides a sample size of 178 which more than doubles the sample size of 67. Because we believe that there is a positive relationship between pilot experience and aviation safety a one tailed will be used. The sample size of 67 is not sufficient to demonstrate a relationship between pilot experience and aviation safety.

File Edit View Tests Calculator Help Central and noncentral distributions |Protocol of power analyses critical r = 0.202673 3 2 -0.3 -0.2 -0.1 0 0. 1 0.2 0.3 0.4 0.5 0.6 Test family Statistical test Exact Correlation: Bivariate normal model Type of power analysis A priori: Compute required sample size - given &, power, and effect size Input Parameters Output Parameters Tail(s) One Lower critical r 0.2026735 Determine => Correlation p H1 0.3 Upper critical r 0.2026735 o err prob 0.05 Total sample size 67 Power (1-B err prob) 0.80 Actual power 0.8032714 Correlation p HO 0 Options X-Y plot for a range of values CalculateNormal Q-Q Plot of Y= Aviation Safety Index N Expected Normal -1 -2 20 22 24 26 29 30 32 Observed ValueNormal Q-Q Plot of Total Flight Hours Expected Normal 3mm spun Observed Value spun 12mm