Assignment On One-Way Experimental Design And Correlational

Assignment One Way Experimental Designscorrelational Research Which

Identify the independent variable and dependent variable. Indicate the number of levels in the independent variable and describe each level. Indicate whether the research used a between-participants or a within-participants research design and how you determined this to be the case. Presume a third condition was added to the study. In this condition, the participants are not given any information about the effects of the (placebo) pill. Next, suppose an analysis of variance (ANOVA) was conducted. Briefly interpret, in your own words, what it would mean if the F was significant as applied to this study. Note: Support the responses within your Assignment with evidence from the assigned Learning Resources.

Paper For Above instruction

Introduction

The utilization of experimental designs in psychological research provides critical insights into causal relationships between variables, especially when controlling extraneous factors. A quintessential example involves examining the effects of placebo instructions on sleep latency among insomnia sufferers. This paper analyzes such a study, focusing on identifying the independent and dependent variables, the experimental design structure, and interpreting possible outcomes of an ANOVA statistical test.

Identification of Variables and Design Structure

The independent variable (IV) in the study is the instructions given to participants about the pill they are taking. Specifically, it entails the expectancy created regarding the pill's effect—sleepy, alert, or no information. The dependent variable (DV) is the measure of sleep latency, operationalized as the time it takes participants to fall asleep after ingestion. The manipulation of the IV to observe its influence on sleep latency exemplifies the core of experimental research aimed at establishing causality.

Initially, the study involved two levels of the IV: (1) participants told the pill induces sleepiness, and (2) participants told it promotes alertness. The instructions serve as the different levels to observe differing effects on sleep latency. Since the instructions are varied between participants, this constitutes a between-participants design. Each participant experiences only one level of the IV, minimizing carry-over effects and contamination across conditions.

Suppose, however, that a third condition was added: participants not given any information about the pill's effects. This condition effectively serves as a control, isolating the pure effect of the placebo without expectancy influence. The addition of this level introduces a third group with no instructions, bringing the total number of levels in the IV to three: sleep-induction instructions, alert-induction instructions, and no instructions.

Given the nature of the experiment, where participants are randomly assigned to one of these conditions, the design remains between-participants. Random assignment ensures the independence of observations, a pivotal assumption in ANOVA testing.

Interpretation of ANOVA Results

An analysis of variance (ANOVA) allows comparison of the mean sleep latency across the three levels of the IV. The F-statistic in an ANOVA assesses whether there are significant differences among group means beyond what would be expected by chance. If the F-value is significant (p

Specifically, a significant F-value would support the hypothesis that expectancy effects, manipulated via instructions, impact how quickly individuals fall asleep. It also validates that the variability between group means exceeds the variability within groups, attributable to the manipulations rather than random error. Consequently, researchers might proceed with post hoc tests, such as Tukey’s HSD, to identify which specific groups differ significantly.

On the other hand, if the F-value is not significant, the data do not provide sufficient evidence that the instructions influence sleep latency. This could imply that expectancy does not affect sleep onset, or that other unmeasured variables might mediate or moderate this relationship.

Conclusion

In summary, this experimental setup exemplifies how manipulating an independent variable—participants’ expectancy about the pill—can reveal causal influences on sleep latency. The between-participants design assigns different groups to each condition to prevent contamination of effects, and the ANOVA offers a statistical method to determine if observed differences are statistically significant. A significant F-test would thus reinforce the role of expectancy in modulating sleep, contributing valuable evidence to insomnia treatment strategies predicated on placebo and expectancy effects.

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