A Sports Psychologist Gave A Questionnaire About Healthy Eat ✓ Solved

A sports psychologist gave a questionnaire about healthy eating

ASSIGNMENT 08 PS390 Statistical Reasoning in Psychology: A sports psychologist gave a questionnaire about healthy eating habits to randomly selected professional athletes. The results are displayed below. Using the .05 significance level, is there a difference in healthy eating habits among professionals in the three sports? Baseball Players, Basketball Players, Football Players.

1. a. Make a graph for the data set. b. Use the five steps of hypothesis testing (report results in APA format). c. Figure the effect size of this study. d. Conduct a planned contrast for Baseball versus Football players (using Tukey’s HSD).

2. A researcher is interested in the effects of sleep deprivation and caffeine intake on mood. Participants were randomly assigned to a sleep condition (normal or deprived) and a caffeine condition (0 cups, 2 cups, or 4 cups). After the manipulations, mood was measured (such that higher numbers indicated better mood).

3. The results were as follows: Normal Condition Deprived Condition 0 cups 2 cups 4 cups 0 cups 2 cups 4 cups. Analyze these data using a factorial analysis of variance and including R2 for each effect.

4. A researcher was interested in whether college GPA (X) would predict starting salary after college (Y). (For simplicity, salary was converted to a 100-point scale.) The participants' scores were: X Y 2.75 M = 50 a. Report the correlation and linear prediction equation. b. Make a graph with the regression line. c. Figure the standardized regression coefficient.

5. An advertising firm wanting to target people with strong desires for success conducted a study to see if such people differed in the types of television shows they watched. Randomly selected participants recorded the shows they watched for a week, then their desire for success was assessed, and finally they were divided into two groups. Low Success seekers watched 8 comedies, 15 romances, 6 documentaries, 13 dramas, and 3 news shows. High Success seekers watched 3 comedies, 3 romances, 9 documentaries, 7 dramas, and 8 news shows. Using the .05 significance level, is the distribution of type of shows watched different for participants having high and low desires for success? a. Use the five steps of hypothesis testing. b. Figure a measure of effect size and indicate whether it is small, medium, or large.

Paper For Above Instructions

The question of whether healthy eating habits differ among professional athletes across three sports—baseball, basketball, and football—is a critical concern in sports psychology, especially in how these habits can influence performance. The data collected from questionnaires can offer insights into these variations. The first step in addressing this question involves establishing a clear hypothesis: it is posited that there are significant differences in healthy eating habits among athletes of different sports. To analyze this, we will utilize a one-way ANOVA test, given that we are comparing means across three distinct groups.

In conducting hypothesis testing, the five steps are as follows:

1. State the Null Hypothesis (H0): There is no difference in healthy eating habits among baseball, basketball, and football players.
2. State the Alternative Hypothesis (H1): At least one sports group has different healthy eating habits compared to the others.
3. Set the Significance Level: The significance level is set at .05, which is commonly used in social science research.
4. Collect Data: Data from the questionnaires would be gathered, ensuring they are complete and correctly formatted for analysis.
5. Conduct the ANOVA Test: Using the data, we compute the F-statistic to determine if the null hypothesis can be rejected. If the calculated p-value is less than .05, we reject the null hypothesis and accept the alternative hypothesis.

Next, to determine the effect size for this study, we can calculate eta-squared (η²) which quantifies how much of the total variance is attributed to the independent variable. A small effect is generally considered to be .01, medium .06, and large .14. Once we have conducted the ANOVA and found a significant result, we can use Tukey’s HSD (Honestly Significant Difference) to perform a planned contrast between baseball and football players' eating habits. This method allows for multiple comparisons while controlling the overall type I error rate.

Moving on to the second scenario concerning sleep deprivation and caffeine on mood, a factorial ANOVA will be employed. This analysis will be essential to understand the interaction effect between sleep deprivation and caffeine intake on mood scores. Given conditions for sleep (normal vs. deprived) and varying caffeine levels (0, 2, 4 cups), we can analyze the main effects and interaction effects for both independent variables.

The third question involves predicting starting salary based on GPA. The appropriate method for this is regression analysis. We will start by presenting the correlation coefficient that indicates the strength and direction of the relationship between GPA (X) and salary (Y). Using the data points provided, we formulate the linear regression equation in the form of Y = mX + b, where m is the slope and b is the Y-intercept, and create a graphical representation with the regression line plotted against the observed data points. Additionally, the standardized regression coefficient will provide insights on the effect of GPA on starting salary in terms of standard deviation units.

Finally, assessing the impact of desire for success on television viewing habits using the chi-square test can shed light on whether exhibit differences in their preferences. An analysis of the types of shows watched by both groups (low versus high success seekers) will reveal if the distributions are significantly different, allowing us to determine the effect size, categorizing it as small, medium, or large.

To summarize, statistical analyses such as ANOVA, regression analysis, and chi-square tests are powerful tools used in sports psychology and other fields to make evidence-based conclusions. Through structured data collection and careful hypothesis testing, researchers can uncover meaningful patterns and relationships.

References

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