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Introductory Statistical Experimental Designs
Published in Jiro Nagatomi, Eno Essien Ebong, Mechanobiology Handbook, 2018
Julia L. Sharp, Patrick D. Gerard
In the previous section on completely randomized designs with subsampling, subsampling was introduced as one type of sampling design that can be employed in completely randomized, randomized complete block, or factorial experiments. If subsampling is utilized, each experimental unit is measured in an unstructured manner more than once. Another type of sampling design involves measuring experimental units repeatedly over predetermined times (or space) with the objective of evaluating treatment effects over time, including whether treatment effects are consistent across time (i.e., a treatment-by-time interaction). This type of study is often referred to as a repeated measures study, or equivalently as a longitudinal study. Note that when destructive testing or measurement is involved, this precludes repeated measures in that the same experimental unit cannot be measured over time. Two examples are provided below.
Understanding users’ continuous content contribution behaviours on microblogs: an integrated perspective of uses and gratification theory and social influence theory
Published in Behaviour & Information Technology, 2020
Xiaodan Liu, Qingfei Min, Shengnan Han
This paper makes three contributions to the literature on the antecedents of CCCB on microblogs. First, drawing on U&G and SIT, we developed an integrated model to explain users’ CCCB on microblogs in which we argued that users’ CCCB can be motivated by both themselves and others. Previous research has applied U&G in the context of users’ adoption (Anabel and Young 2010; Islam 2016; Smock et al. 2011) or continuance intention (Hui, Weiguo, and Chau 2014; Ku, Chen, and Zhang 2013; Lin, Lee, and Giang, 2016). We extended U&G to users’ actual continuance behaviours on microblogs. Second, the impacts of social influence on CCCB are not fully understood in the literature. Most of the extant research on social influence is formed predominantly through the measurement of subjective norms with self-reported data (Ku, Chen, and Zhang 2013; Venkatesh et al. 2003; Venkatesh and Davis 2000). However, a microblog is a platform with many weak ties, and a subjective norm may not be a dominating social influence factor. The large number of followers (the number of users who subscribe to receive the contributor’s contents) means a great deal of support and attention, which will motivate more CCCB from the contributor (Goes, Lin, and Yeung 2014; Wang, Meister, and Gray 2013). Using natural data, we explored how such an audience affects the behaviours of the content contributors. Third, we used a longitudinal sample with both subjective and objective measures to explain why users continuously contribute content on microblogs. The longitudinal study provides more validity for causal inferences. By measuring actual CCCB as the dependent variable, this model can provide a deeper and more comprehensive understanding of users’ CCCB, along with more practical management implications.