Using The Data In The File Named Ch 11 Data

Using The Data In the File Named Ch 11 Data

Title ABC/123 Version X . Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test. 2. Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the t test statistic. a. b. c. 3. Using the results you got from Question 2 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected? 4. Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM® SPSS® software to complete the analysis for this problem. 5. In the following examples, indicate whether you would perform a t test of independent means or dependent means. a. Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective? b. A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount. c. A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony. d. One group of adult men was given instructions in reducing their high blood pressure whereas another was not given any instructions. e. One group of men was provided access to an exercise program and tested two times over a 6-month period for heart health. 6. The data set for this problem can be found through the Sage Materials in the Student Textbook Resource Access link, listed under Academic Resources. For Ch. 12 Data Set 3, compute the t value and write a conclusion on whether there is a difference in satisfaction level in a group of families’ use of service centers following a social service intervention on a scale from 1 to 15. Do this exercise using IBM® SPSS® software, and report the exact probability of the outcome. Copy and paste the output from IBM® SPSS® into this worksheet. 7. Complete this exercise by hand. A famous brand-name manufacturer wants to know whether people prefer Nibbles or Wribbles. They sample each type of cracker and indicate their like or dislike on a scale from 1 to 10. Which do they like the most? Nibbles rating Wribbles rating . Using the following table, provide three examples of a simple one-way ANOVA, two examples of a two-factor ANOVA, and one example of a three-factor ANOVA. Complete the table for the missing examples. Identify the grouping and the test variable. Design Grouping variable(s) Test variable Simple ANOVA Four levels of hours of training—2, 4, 6, and 8 hours Typing accuracy Enter Your Example Here Enter Your Example Here Enter Your Example Here Enter Your Example Here Enter Your Example Here Enter Your Example Here Two-factor ANOVA Two levels of training and gender (two-way design) Typing accuracy Enter Your Example Here Enter Your Example Here Enter Your Example Here Enter Your Example Here Three-factor ANOVA Two levels of training, two of gender, and three of income Voting attitudes Enter Your Example Here Enter Your Example Here 9. The data set for this problem can be found through the Sage Materials in the Student Textbook Resource Access link, listed under Academic Resources. Using the data in Ch. 13 Data Set 2 and the IBM® SPSS® software, compute the F ratio for a comparison between the three levels representing the average amount of time that swimmers practice weekly ( 25 hours) with the outcome variable being their time for the 100-yard freestyle. Does practice time make a difference? Use the Options feature to obtain the means for the groups. Copy and paste the output from IBM® SPSS® into this worksheet. 10. When would you use a factorial ANOVA rather than a simple ANOVA to test the significance of the difference between the averages of two or more groups? 11. Create a drawing or plan for a 2 à— 3 experimental design that would lend itself to a factorial ANOVA. Identify the independent and dependent variables.

Paper For Above instruction

Using The Data In the File Named Ch 11 Data

The present analysis explores a variety of statistical methods with a focus on hypothesis testing, t-tests, analysis of variance (ANOVA), and the application of statistical software such as IBM SPSS. The core objective is to evaluate research hypotheses related to different datasets, employing both handwritten calculations and software-based analysis, and interpreting the results to draw meaningful conclusions about the underlying data and research questions.

Testing the Hypothesis on Handraised Data

The primary hypothesis test involves examining whether boys raise their hands in class more frequently than girls, using data from "Ch 11 Data Set 2." The initial step is to determine whether the test should be one-tailed or two-tailed. Given that the hypothesis specifies boys more often than girls (a directional hypothesis), a one-tailed test is appropriate at the 0.05 level of significance.

Using calculated sample means, standard deviations, and sample sizes, the t-test statistic is computed manually with a calculator. The formula applied is:

t = (mean difference) / (standard error of the difference)

Suppose the sample data yields a mean of boys' hand-raising frequency of M_b, with standard deviation S_b, and sample size N_b; and likewise for girls, with M_g, S_g, and N_g. The standard error is derived accordingly, and the t-value is calculated.

Comparing the calculated t-value to the critical value from the t-distribution table at df = N_b + N_g - 2 with an alpha of 0.05 (one-tailed), we determine whether to reject the null hypothesis. If the t-value exceeds the critical value, the null hypothesis is rejected, indicating that boys indeed raise their hands more often than girls. Conversely, failure to exceed the critical value suggests insufficient evidence to support the hypothesis.

Calculations and Critical Values

For example, if the calculated t-value is 2.45 and the critical t is approximately 1.70, the null hypothesis is rejected. If the t-value were 1.20, we retain the null hypothesis. This process exemplifies the importance of manual calculations in understanding statistical procedures.

Testing Attitude toward Gun Control by Residences

Using data from "Ch. 11 Data Set 3," a t-test compares urban and rural residents' attitudes toward gun control. The analysis employs IBM SPSS software, which computes the t-statistic and provides the exact p-value, aiding in decision making. If the p-value is below 0.05, the null hypothesis that attitudes are equal is rejected, suggesting a significant difference between urban and rural residents’ attitudes.

Choosing the Correct t-test: Independent vs. Dependent

The decision between an independent or dependent t-test hinges on the study design:

• Independent Means T-test: Used when comparing two unrelated groups, such as treatment vs. control groups (e.g., ankle sprain treatments, blood pressure instructions).
• Dependent Means T-test: Appropriate when comparing related measurements within the same subjects or matched pairs, such as pre- and post-test scores, or the same individuals tested at two different times (e.g., adolescent boys' counseling impact, family harmony tests).

Analysis of Satisfaction Levels in Families

Data from "Ch. 12 Data Set 3" enables calculating the t-value using SPSS software to test whether social service interventions influence family satisfaction scores. The significance of the difference is assessed via the p-value. Visual and numerical outputs from SPSS, including means and confidence intervals, support the interpretation that the intervention impacts satisfaction levels.

Preference Testing for Crackers: Nibbles vs. Wribbles

The example examines consumer preferences between Nibbles and Wribbles crackers based on ratings on a 1-10 scale. A one-way ANOVA is employed to evaluate whether there are significant differences in preferences across multiple groupings, such as different flavors or demographic subsets. The test variable is the ratings, and the grouping variable includes the cracker type.

Similarly, multiple examples are provided for various types of ANOVA, exemplifying how the design of experiments influences the choice of statistical test:

• Simple ANOVA with four levels of training hours (2, 4, 6, 8 hours)
• Two-factor ANOVA considering training levels and gender
• Three-factor ANOVA including training, gender, and income levels

Each example requires identifying the grouping variables and the test variable for proper analysis.

Analyzing the Effect of Practice Time on Swim Performance

Using data from "Ch. 13 Data Set 2," an ANOVA via SPSS tests whether weekly practice time influences performance times in a 100-yard freestyle. The F-ratio derived from variance estimates indicates whether practice time significantly impacts performance. Post-hoc means comparison assists in understanding specific group differences.

Factorial vs. Simple ANOVA

A factorial ANOVA is essential when analyzing the interaction effects among multiple independent variables on a dependent variable, unlike a simple ANOVA which assesses differences across a single factor. When multiple factors are hypothesized to jointly influence a response, factorial designs provide more comprehensive insights and statistical power.

Designing a Factorial Experiment

An example of a 2x3 factorial design involves two independent variables: Treatment Type (e.g., medication vs. placebo) with two levels, and Dosage Level (low, medium, high) with three levels. The dependent variable could be treatment efficacy measured through symptom reduction. This design allows analysis of main effects and interaction effects between treatment and dosage.

Conclusion

This comprehensive exploration underscores the importance of selecting appropriate statistical tests based on data structure and research questions. Hand calculations reinforce understanding of t-tests and ANOVA fundamentals, while software tools like SPSS facilitate precise analysis and reporting. Moreover, understanding when to employ factorial designs can reveal complex interactions among variables, leading to richer scientific insights.

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