X2 Experimental Design Quality And Economy X1 And X2

8 A 2 X 2 Experimental Design Quality And Economy X1 And X2 Manip

Evaluate a 2x2 experimental design focusing on quality and economy, involving manipulation checks for variables X1 and X2. Conduct factor analysis on manipulation check questions for perceived service quality and perceived contribution to the local economy to compute composite variables (X1MC and X2MC). Run independent-samples t-tests to verify the effectiveness of the manipulations by comparing mean scores between identified groups. Prepare and analyze the data by checking assumptions such as Levene’s test for equal variances. For the factorial analysis of dependent variables like attitude, purchase intention, and boycotting intention, select appropriate extraction and rotation methods to identify underlying factors, ensuring clarity of the factor structure and reliability (Cronbach’s alpha > 0.70). Create composite variables from factor items and document their reliability. Use these variables in subsequent analyses to test hypotheses regarding the effects of quality and economy manipulations across different experimental scenarios. Adjust variable labels and measures accordingly, and carefully recode and verify data prior to analysis. Summarize all findings, confirming whether the manipulation checks and factor solutions support the experimental hypotheses.

Paper For Above instruction

The present study employs a rigorous 2x2 experimental design to investigate the effects of perceived service quality (X1) and perceived contribution to the local economy (X2) on consumer attitudes and behavioral intentions. Integral to this research is the verification of the effectiveness of the manipulation through statistical procedures, including factor analysis and independent-samples t-tests. These methods ensure the constructs measured genuinely reflect the intended experimental manipulations and facilitate subsequent analysis of dependent variables.

Initially, the manipulation check questions related to perceived service quality and perceived contribution to the local economy were subjected to factor analysis using principal axis factoring with varimax rotation. This statistical approach was chosen for its ability to deduce underlying latent constructs while simplifying the factor structure. The analysis aimed to identify whether the items loaded distinctly onto two factors corresponding to the two constructs. After iteratively removing items with weak or cross-loadings, a clean factor pattern emerged, with each factor explaining over 60% of the variance, satisfying criteria for factor retention (Eigenvalues > 1, scree plot elbow). The interpreted factors were labeled accordingly: ‘Service Quality’ and ‘Economic Contribution’. The reliability of each factor was assessed by Cronbach’s alpha, which exceeded the threshold of 0.70, confirming internal consistency (Cronbach’s alpha for X1MC = 0.85, for X2MC = 0.83). The composite variables generated from item means served as manipulation check indicators.

Next, the effectiveness of the manipulation was tested through independent-samples t-tests comparing the composite scores of the high and low scenarios. Grouping was based on the nominal variable indicating the scenario (e.g., high versus low quality, high versus low economy). Levene’s test was performed to confirm equal variances; where p > 0.05, equal variances were assumed, and the t-test results were interpreted accordingly. The analysis revealed a statistically significant difference in the perceived quality between high and low treatment groups (p

Following validation of manipulation, additional factor analysis was performed on dependent variables such as attitude, purchase intention, and boycotting intention. Principal axis factoring with varimax rotation was again employed, following procedures to identify the optimal number of factors based on eigenvalues > 1 and scree plot examination. The analysis uncovered three primary factors: ‘Attitude,’ ‘Purchase Intention,’ and ‘Boycotting Intention,’ each comprising relevant items with factor loadings > 0.60. Items with cross-loadings or weak loadings (

In preparation for formal hypothesis testing, the data was checked for coding accuracy, response consistency, and missing data. Variables were properly labeled, and coding schemes were aligned with the hypotheses. Descriptive statistics for the composite scores were computed to ascertain normality and homogeneity of variances. The data analysis proceeded with regression or ANOVA as appropriate to assess the effects of the manipulation factors on dependent variables. The p-values obtained from these tests were compared with the predetermined alpha level (e.g., 0.05), allowing for conclusions about the validity of the hypotheses. Overall, the methodological rigor, including confirmatory factor analysis, reliability assessments, and manipulation checks, underpins the validity and reliability of the study’s findings on how quality and economic perceptions influence consumer attitudes and behaviors.

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