Table 3.
Sample Characteristics.
| Author (Year) | Sample Size | Gender | Age (Mean: In Years) | Race/Ethnicity |
|---|---|---|---|---|
| Avidor et al. (2017) | n=615 | Male 62.0% Female 38.0% | German 100% | |
| Bai et al. (2016) | n=954 | Male 51.0% Female 49.0% | 72.73 | Chinese 100% |
| Fernandez-Ballesteros et al. (2017) | n=2005 | Male 43.0% Female 57.0% | 71.8 | German 30.0% Mexican 39.2% Spanish 30.8% |
| Garstka et al. (2004) | n=60 | Male 37.0% Female 63.0% | 75.0 | White 78.4% Others 21.6% |
| Kim (2015) | n=3991 | Male 39.8% Female 60.2% | 75.45 | Caucasian 86.3% African American 11.9% Other 1.8% |
| Kim et al. (2015) | n=812 | Male 49.0% Female 51.0% | 72.86 | Korean 100% |
| Lee and Kim (2016) | n=207 | Female 100.0% | 77.42 | Korean 100% |
| Lyons et al. (2018) | n=2119 | Male 68.0% Female 32.0% |
66.71 | Australian 100% |
| Sabik (2013) | n=244 | Female 100.0% | 63.44 | European American 66.7% African American 33.3% |
| Shin et al. (2018) | n=439 | Male 64.0% Female 36.0% |
70.8 | Korean 100% |
| Siguaw et al. (2017) | n=543 | Male 64.0% Female 36.0% |
69.5 | American or Canadian (not specified) |
| Zhang et al. (2018) | n=279 | Male 51.3% Female 48.7% |
67.09 | Chinese 100% |
| Zhang et al. (2019) | n=331 | Male 51.7% Female 48.3% |
67.93 | Chinese 100% |