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Additionally, extra questions were analyzed to add up some relevant insights regarding each gender and respective behavior on SM platforms. Questions and results can be found on Appendix 5. 4.1.3 Data screening – univariate and multivariate outliers In order to improve the quality of the data, a data cleaning process was executed, which involves an outlier analysis for univariate and multivariate outliers. The univariate analysis allows to identify cases of extreme values, which means, values that are not common among the sample for the 18 items of the data set. In order to identify the presence of univariate outliers in the single variables, all scores of each variable had to be converted into standardized Z-scores. For a significant level of 5%, Z-scores that are greater than 3.29 and smaller than -3.29 are considered as outliers. Consequently, through this analysis, no univariate outliers were found. The multivariate analysis allows to identify cases of respondents with an uncommon combination of values in two or more variables. In order to perform this analysis, the Mahalanobis Distance for each response using the linear regression for the 4 different variables was calculated. Afterwards, a chi-square distribution was also performed. Then, the aim was to identify the Mahalanobis Distance probability with a p-value lower than 0.001. After analyzing this, 19 outliers were identified. 4.1.4 Data reliability The Cronbach`s alpha for both scales was assessed in order to analyze the internal consistency of the measurement model, presented on Table 1. Table 1. Scales` reliability test analysis Scales Dimensions Initial Number of Items Cronbach`s α Cronbach`s α if item deleted Item deleted Final Number of Items Credibility Attractiveness 5 0.875 - - 5 Trustworthiness 5 0.914 - - 5 Expertise 5 0.870 - - 5 Credibility as a construct 15 0.869 - - 15 Purchase intention as a construct 3 0.898 - - 3 40PDF Image | HOW INFLUENCERS CREDIBILITY ON INSTAGRAM IS PERCEIVED BY CONSUMERS AND ITS IMPACT ON PURCHASE INTENTION
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