A Researcher Predicts That Watching A Film On Institutional

A Researcher Predicts That Watching A Film On Institutionalization Wil

A researcher predicts that watching a film on institutionalization will change students' attitudes about chronically mentally ill patients. The researcher randomly selects a class of 36 students, shows them the film, and gives them a questionnaire about their attitudes. The main score on the questionnaire for these 36 students is 70. The score for a similar class of students who did not see the film is 75. The standard deviation is 12. Using the five steps of hypothesis testing and the 5% significance level (i.e., alpha = .05), does showing the film change students' attitudes towards the chronically mentally ill?

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Introduction

Hypothesis testing is a fundamental statistical method used to determine whether there is enough evidence to support a specific claim about a population parameter. In this scenario, the researcher aims to assess whether viewing a film about institutionalization has an impact on students' attitudes towards mentally ill individuals. The primary statistical question revolves around whether the observed difference in attitude scores between the film-viewing group and the non-viewing group is statistically significant, considering the variability within the data and using an established significance level.

Step 1: Setting Alpha at 0.05

The significance level, denoted as alpha (α), indicates the threshold for determining whether a result is statistically significant. Setting α at 0.05 means there is a 5% risk of rejecting the null hypothesis when it is actually true (Type I error). Essentially, if the p-value obtained from the test is less than 0.05, the researcher can conclude that the effect observed is unlikely due to random chance alone, and the null hypothesis will be rejected. This threshold balances the risk of making a false positive conclusion against the desire to detect a true effect.

Step 2: Formulating the Null and Alternative Hypotheses

The null hypothesis (H₀) posits that there is no difference in attitudes between students who watched the film and those who did not. Mathematically, it can be expressed as:

H₀: μ₁ = μ₂

where μ₁ is the mean attitude score of the film group, and μ₂ is the mean score of the non-film group.

The alternative hypothesis (H₁) suggests that watching the film influences students' attitudes, leading to a difference in mean scores:

H₁: μ₁ ≠ μ₂

This is a two-tailed hypothesis because the researcher is interested in detecting any difference, whether positive or negative, caused by the intervention.

Step 3: One-Tailed or Two-Tailed Hypothesis?

Given the research question, which seeks to determine whether viewing the film changes attitudes in either direction, the hypothesis is two-tailed. Specifically, the researcher is testing for the possibility of a decrease or increase in attitude scores due to watching the film. A two-tailed test is appropriate because it captures the potential for both outcomes.

Step 4: Calculating the Critical Z Value

Using the standard normal distribution table at α = 0.05 for a two-tailed test, the critical z-value is approximately ±1.96. This means that if the calculated z-score falls beyond ±1.96, the result is statistically significant at the 5% level, and the null hypothesis will be rejected.

Step 5: Comparing the Obtained Z to the Critical Z

Suppose the computed z-value from the sample data is -2.5. Since -2.5 is less than -1.96, it lies in the rejection region of the distribution. Therefore, we reject the null hypothesis at the 0.05 significance level, indicating that the difference in attitudes observed between the groups is statistically significant.

Step 6: Interpreting the Findings

Based on the analysis, there is sufficient evidence at the 5% significance level to conclude that watching the film has a statistically significant effect on students’ attitudes towards chronically mentally ill patients. Specifically, the data suggest that the students who watched the film had different attitudes compared to those who did not, and the observed difference is unlikely to have occurred by random chance alone. Since the sample mean for the film group (70) is lower than that of the non-film group (75), it indicates that watching the film may have led to more negative attitudes, reflecting the intervention’s impact on perceptions.

Conclusion

In conclusion, the hypothesis testing indicates that the film about institutionalization significantly influences students’ attitudes towards the mentally ill. This underscores the importance of media and educational interventions in shaping societal perceptions and highlights the potential for films to be effective tools in attitude change. Future research could explore whether the effect persists over time or varies across different demographic groups, providing a deeper understanding of how media influences mental health stigma.

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