SAGE Journal Articles
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Cost-effective and targeted prevention, intervention and treatment programs for hard-to-reach populations at risk for HIV and other infections rely on the collection of quality data through biological and behavioral surveillance surveys (BBSS). Over the past decade, there has been a global expansion of BBSS to measure the prevalence of HIV and other infections, and related risk behaviors among people such as injecting drug users, males who have sex with males, and female sex workers. However, a major challenge to sampling these hard-to-reach populations is that they are usually stigmatized and/or practice illegal behaviors which, in turn, make them difficult to access and unwilling to participate in research efforts. Over the past decade, respondent-driven sampling (RDS) has become recognized as a viable option for rigorous sampling of hard-to-reach populations. This paper introduces RDS methods and describes some of the advantages and challenges to implementing and analyzing surveys that use RDS.
Questions to Consider
1. Summarize some of the advantages and challenges to implementing and analyzing surveys that use RDS.
Cognitive Domain: Comprehension
Difficulty Level: Medium
2. Describe some of the challenges by using “snowball” sampling. How might bias be a problem with snowball sampling?
Cognitive Domain: Knowledge
Difficulty Level: Medium–Hard
3. Compare and contrast snowball sampling bias with respondent driven solutions.
Cognitive Domain: Analysis, Comprehension
Difficulty Level: Hard
The American Psychological Association (APA) Task Force on Statistical Inference was formed in 1996 in response to a growing body of research demonstrating methodological issues that threatened the credibility of psychological research, and made recommendations to address them. One issue was the small, even dramatically inadequate, size of samples used in studies published by leading journals. The present study assessed the progress made since the Task Force’s final report in 1999. Sample sizes reported in four leading APA journals in 1955, 1977, 1995, and 2006 were compared using non-parametric statistics, while data from the last two waves were fit to a hierarchical generalized linear growth model for more in-depth analysis. Overall, results indicate that the recommendations for increasing sample sizes have not been integrated in core psychological research, although results slightly vary by field. This and other implications are discussed in the context of current methodological critique and practice.
Questions to Consider
1. How can sample size hinder or threaten the credibility of psychological research?
Cognitive Domain: Knowledge
Difficulty Level: Medium
2. How well have recommendations for increasing sample sizes been integrated in core psychological research? Explain.
Cognitive Domain: Comprehension
Difficulty Level: Medium
3. Discuss some of the recommendations the authors make and how this relates to power and sample size.
Cognitive Domain: Comprehension, Application
Difficulty Level: Medium
An important question in the design of experiments is how to ensure that the findings from the experiment are generalizable to a larger population. This concern with generalizability is particularly important when treatment effects are heterogeneous and when selecting units into the experiment using random sampling is not possible – two conditions commonly met in large-scale educational experiments.
Questions to Consider
1. Define and explain and then discuss the importance of generalizability.
Cognitive Domain: Comprehension, Analysis
Difficulty Level: Medium
2. How can sample selection strategy be bias-robust? Give an example.
Cognitive Domain: Knowledge, Analysis
Difficulty Level: Hard
3. What is the goal of the balanced-sampling framework?
Cognitive Domain: Comprehension
Difficulty Level: Medium–Hard
While the use of inferential statistics is a nearly universal practice in the social sciences, there are instances where its application is unnecessary or, worse, misleading. This is true for most research on the Relative Age Effect (RAE) in sports. Given the limited amount of data needed to examine RAE (birth dates) and the availability of complete team rosters, RAE researchers are in a unique position – inference is not needed when interpreting findings because the data is from a population. We reveal, over the course of 5 years, the misapplication of inferential statistics using census data in 10 of 13 RAE studies across 12 sports journals. Thus, perhaps by inertia, the majority of RAE researchers use inferential statistics with their census data, misusing analytic techniques and, in some cases, undervaluing meaningful patterns and trends.
Questions to Consider
1. The authors argue that in sports psychology the use of census data represents the population and that inferential statistics are not needed. Provide support for or against this argument.
Learning Objective: Populations versus samples
Cognitive Domain: Analysis
Difficulty Level: Hard
2. Inferential statistics are used to: (a) make estimates about a population from a sample, (b) make estimates about what will happen to a population, (c) make estimates about how a population impacts a sample, (d) make estimates about how well a sample represents a population.
Learning Objective: Inferential statistics
Cognitive Domain: Knowledge
Difficulty Level: Easy
3. On p. 117, Diggs et al. argue that only descriptive statistics are required because: (a) random sampling of athletes was not used, (b) inferential statistics can be misleading, (c) population data only needs to be described, (d) representative sampling was used.
Learning Objective: Inferential statistics
Cognitive Domain: Knowledge
Difficulty Level: Medium
The rise of social media websites (e.g., Facebook) and online services such as Google AdWords and Amazon Mechanical Turk (MTurk) offers new opportunities for researchers to recruit study participants. Although researchers have started to use these emerging methods, little is known about how they perform in terms of cost efficiency and, more importantly, the types of people that they ultimately recruit. Here, we report findings about the performance of four online sources for recruiting iPhone users to participate in a web survey. The findings reveal very different performances between two types of strategies: those that “pull in” online users actively looking for paid work (MTurk workers and Craigslist users) and those that “push out” a recruiting ad to online users engaged in other, unrelated online activities (Google AdWords and Facebook). The pull-method recruits were more cost efficient and committed to the survey task, while the push-method recruits were more demographically diverse.
Questions to Consider
1. How is convenience sampling different from random sampling? What are some of the problems that the authors are concerned about from online convenience sampling?
Learning Objective: Selecting a sample
Cognitive Domain: Comprehension
Difficulty Level: Hard
2. Which of the following online recruitment sources allow researchers to carefully target participants based on demographic information? (a) Google AdWords and MTurk, (b) Facebook and Google AdWords, (c) MTurk and Craigslist, (d) Craigslist and Facebook.
Learning Objective: Convenience sampling
Cognitive Domain: Knowledge
Difficulty Level: Medium
3. Which online recruitment strategy did they find provided the most representative sampling of the population? (a) MTurk, (b) Craigslist, (c) Google AdWords, (d) Facebook.
Learning Objective: Representative sample
Cognitive Domain: Knowledge
Difficulty Level: Medium