| Chapter title |
Structural equation modeling.
|
|---|---|
| Chapter number | 27 |
| Book title |
Statistical Human Genetics
|
| Published in |
Methods in molecular biology, February 2012
|
| DOI | 10.1007/978-1-61779-555-8_27 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-554-1, 978-1-61779-555-8
|
| Authors |
Stein CM, Morris NJ, Nock NL, Catherine M. Stein, Nathan J. Morris, Nora L. Nock |
| Abstract |
Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of general pedigrees. Here, we review the theory of SEM for both unrelated and family data, the available software for SEM, and provide an example of SEM analysis. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Jamaica | 1 | <1% |
| Mexico | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 217 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 20% |
| Student > Master | 23 | 10% |
| Student > Doctoral Student | 16 | 7% |
| Researcher | 13 | 6% |
| Student > Bachelor | 10 | 5% |
| Other | 32 | 15% |
| Unknown | 83 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Business, Management and Accounting | 33 | 15% |
| Social Sciences | 20 | 9% |
| Computer Science | 12 | 5% |
| Engineering | 10 | 5% |
| Environmental Science | 9 | 4% |
| Other | 46 | 21% |
| Unknown | 90 | 41% |