Social media has not always been a traditional form of business strategy for the fashion industry, therefore a study on the role of social media on user engagement for different tiers of fashion brands was conducted. A combination of multiple regression models, ANOVA analysis, and hashtag analysis was done to understand various aspects of the research question. Tests were run against different post types to gain deeper insights on engagement levels and statistical significance. Post frequency and correlation analysis was conducted to understand how followers respond to the content. Overall, reels and carousel media were the most successful in increasing and maintaining user engagement. Prada has the most inactive users and ineffective social media strategies to increase engagement. While they have a high following they are unable to sustain engagement levels through their posts. Whereas, Teddy Fresh, despite being a smaller brand has been successful in maintaining engagement levels through their niche target market. Lastly, SKIMS has the fastest growth rate and has been able to increase following through their high frequency post schedule. For each of the brands, this information can be used to further strategize the marketing content. Social media is dynamic and therefore the approach for curating content will differ; being able to understand which types of posts are doing well is helpful for the brands as they can continue to run analysis when needed.

Social media has not always been a traditional form of business strategy for the fashion industry, therefore a study on the role of social media on user engagement for different tiers of fashion brands was conducted. A combination of multiple regression models, ANOVA analysis, and hashtag analysis was done to understand various aspects of the research question. Tests were run against different post types to gain deeper insights on engagement levels and statistical significance. Post frequency and correlation analysis was conducted to understand how followers respond to the content. Overall, reels and carousel media were the most successful in increasing and maintaining user engagement. Prada has the most inactive users and ineffective social media strategies to increase engagement. While they have a high following they are unable to sustain engagement levels through their posts. Whereas, Teddy Fresh, despite being a smaller brand has been successful in maintaining engagement levels through their niche target market. Lastly, SKIMS has the fastest growth rate and has been able to increase following through their high frequency post schedule. For each of the brands, this information can be used to further strategize the marketing content. Social media is dynamic and therefore the approach for curating content will differ; being able to understand which types of posts are doing well is helpful for the brands as they can continue to run analysis when needed.

This article looks closely at two types of errors children have been shown to make with universal quantification—Exhaustive Pairing (EP) errors and Underexhaustive errors—and asks whether they reflect the same underlying phenomenon. In a large-scale, longitudinal study, 140 children were tested 4 times from ages 4 to 7 on sentences involving the universal quantifier every. We find an interesting inverse relationship between EP errors and Underexhaustive errors over development: the point at which children stop making Underexhaustive errors is also when they begin making EP errors. Underexhaustive errors, common at early stages in our study, may be indicative of a non-adult, non-exhaustive semantics for every. EP errors, which emerge later, and remain frequent even at age 7, are progressive in nature and were also found with adults in a control study. Following recent developmental work (Drozd and van Loosbroek 2006; Smits 2010), we suggest that these errors do not signal lack of knowledge, but may stem from independent difficulties appropriately restricting the quantifier domain in the presence of a salient, but irrelevant, extra object.