![]() ![]() ![]() This is a major problem that concerns any statistical analysis. However, there are wider implications inherent in the cases when studies with smaller samples make inferences to larger populations. This need has been highlighted by several researchers in the field who noted that statistical results from small sample studies might be biased and this has serious implications for the stability of findings. In order to obtain replicable empirical evidence applied linguistics researchers need to be aware of a number of issues associated with the choice for a statistical method. In other words, repeated violations of underlying distributional assumptions in a small sample study have large scale negative consequences. If a researcher disregards the necessity to satisfy each and every of the associated assumptions the resultant study may inadvertently fill the scholarly literature with nonreplicable findings and eventuate in a replication crisis. Therefore, a statistical analysis cannot be based on the law of large numbers because the underlying distributional assumptions might not be met in a small sample study. In such studies the number of observations tends to be small. This can be a language classroom, a workplace, a laboratory or even a longitudinal population sample. Quantitative research in applied linguistics often takes place in restricted settings. Besides the applied linguistics research, the BQR method can be used in a variety of the human sciences research where a sample size is small. In addition, these attitudes were found to be the most constant determinant of the integrative orientation. ![]() The findings indicated that there was a statistically significant relationship between the students’ attitudes toward the target language country and their integrative orientation. It examined the relationships between the students’ language learning motivation (specifically, integrative orientation), the students’ images or stereotypes about Japan and their global attitudes toward the target language country and its people. The current study employed a moderately small sample ( N = 27) of students learning the Japanese language in a Malaysian public university. Importantly for applied linguistics research, the BQR method could help to deal with methodological difficulties inherent in small sample studies. The main aim of the current paper is to give a detailed explanation of methodological and practical implications inherent in a robust statistical method called bootstrapped quantile regression (BQR) analysis. In such settings the samples tend to be small, which raises several methodological problems. Quantitative applied linguistics research often takes place in restricted settings of an intact language classroom, workplace, phonetics laboratory or longitudinal sample. ![]()
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