Go To My Saint Leo Portal Click On Module 4 Smile Mixed An

Go To My Saint Leo Portal Click Onto Module 4 Smile Mixed Anova H

Go to My Saint Leo Portal, click onto Module 4 Smile (mixed ANOVA) HOMEWORK AND COMPLETE THE TASK BELOW Smile (Mixed ANOVA) homework This activity will take you outside of the course. Once you have finished reading, please click the course page to continue. Click HERE FOR SMILE ACTIVITY Please do the quick activity above about spotting the real versus fake smiles - this is just so you are familiar with the task and the two factors (gender of smiler - within subjects; gender of participant - between subjects). Write down your answers for both male and female stimuli (not the total number correct; It is recommended that you use a tally and counting those you get right in each gender). The SPSS file attached is made up of data from this task.

Once you have followed the instructions, run a mixed ANOVA, with repeated measures factor of male/female stimulus smile detection accuracy/# of correct smiles for men and women x between factor Participant Gender). (see tutorials above). Copy your SPSS output into a MS word file along with an APA-style interpretation and results write-up and upload it to this assignment dropbox by Sunday at 11:59pm. Be sure to include a report of each effect, factor 1 main effect, factor 2 main effect, and the interaction. You should report an F-statistic, p-value, and effect size for each effect, indicate whether it is significant, and explain what each effect shows. Include mean and SD in your reporting.

Paper For Above instruction

The purpose of this research task is to analyze the accuracy of smile detection across different conditions using a mixed ANOVA. Specifically, the study examines how the gender of the stimulus (male or female) and the gender of the participant influence the ability to correctly identify genuine smiles. This analysis is essential in understanding whether perception varies depending on the gender of both the individual displaying the smile and the observer.

The data collection involved participants completing a quick activity where they identified whether smiles were real or fake for both male and female stimuli. Participants recorded their responses, typically by tallying correct identifications for each gender of stimulus. This data was then used to assess differences in detection accuracy, with the data structured for analysis via a mixed ANOVA. The within-subjects factor was the gender of the stimulus (male vs. female), while the between-subjects factor was the participant's gender (male vs. female). This design allows for examining main effects of each factor and their interaction.

The statistical analysis begins by exploring the main effect of stimulus gender. This effect indicates whether, overall, there is a significant difference in smile detection accuracy for male versus female stimuli. A significant main effect would suggest that participants find it easier or more challenging to identify genuine smiles depending on the stimulus's gender. Effect sizes are calculated using eta-squared (η²), and the F-statistic assesses the variance explained by the factor relative to the residual variance. An example of reporting the main effect might be: "The main effect of stimulus gender was significant, F(1, 58) = 9.34, p = .003, η² = .139, with higher accuracy for female stimuli (M = 8.2, SD = 1.5) than male stimuli (M = 7.4, SD = 1.7)."

Next, the main effect of participant gender is examined. This effect reveals whether male and female participants differ in their overall ability to detect genuine smiles. A significant result would imply a general difference in detection accuracy based on the participant's gender. For example: "The main effect of participant gender was significant, F(1, 58) = 4.56, p = .037, η² = .073, with female participants (M = 8.0, SD = 1.4) outperforming male participants (M = 7.6, SD = 1.6)."

The interaction effect assesses whether the difference in detection accuracy between stimuli genders depends on the participant's gender. A significant interaction suggests that the effect of stimulus gender on smile detection varies for male and female participants. This might be reported as: "The interaction between stimulus gender and participant gender was not significant, F(1, 58) = 2.03, p = .16, η² = .034."

In reporting the results, include means and standard deviations for each condition, interpret the significance and effect size of each effect, and describe what the findings imply about gender differences in smile perception. For example, if the interaction is significant, further post hoc analysis would clarify how the different groups perform relative to each other.

In conclusion, the results from this analysis illuminate whether gender influences smile detection accuracy. Findings that show significant main effects or interactions could suggest biases or perceptual differences that are relevant for understanding social communication and emotional recognition. This analysis exemplifies how mixed ANOVA can be used to disentangle complex factors influencing psychological responses.

References

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