Open Access Epidemiology Research

Debate the pros and cons of open access publishing of research studies.
Select an epidemiological study that analyzes data using one of the statistical measures listed in table 4-11. What is the purpose of that test in that study? What do the findings indicate? Make sure to include an APA reference for the study.

Table 4-11
Selected Statistical Measures of Association

Pearson correlation coefficient (denoted by r)
Measures the strength of the association between two variables measured on a quantitative scale. The
method assumes both variables are normally distributed and that a linear association exists between the
variables. When the latter assumption is violated, the investigator may choose to apply the correlation
measure over a subsection of the data where linearity holds. The correlation coefficient ranges between
1 and +1.

Coefficient of determination (denoted by r2)
Represents the proportion of the total variation in the dependent variable that is determined by the
independent variable. If a perfect positive or negative association exists, then all of the variation in the
dependent variable would be explained by the independent variable. Generally, however, only part of
the variation in the dependent variable can be explained by a single independent variable.

Spearmans rank correlation coefficient (denoted by rs)
An alternative to the Pearson correlation coefficient when outlying data exist such that one or both of
the distributions are skewed. This method is robust to outliers.

Simple regression model y = b0 + b1x1
A statistical analysis that provides an equation that estimates the change in the dependent variable (y)
per unit change in an independent variable (x). This method assumes that for each value of x, y is

normally distributed, that the standard deviation of the outcomes y do not change over x, that the
outcomes y are independent, and that a linear relationship exists between x and y.

Multiple regression y = b0 + b1x1 + + bkxk
An extension of simple regression analysis in which there are two or more independent variables. The
effects of multiple independent variables on the dependent variable can be simultaneously assessed.
This type of model is useful for adjusting for potential confounders.

Logistic regression Log(odds) = b0 + b1x1
A type of regression in which the dependent variable is a dichotomous variable. Logistic regression is
commonly used in epidemiology because many of the outcome measures considered involve nominal

Multiple logistic regression Log(odds) = b0 + b1x1 + + bkxk
An extension of logistic regression in which two or more independent variables are included in the
model. It allows the researcher to look at the simultaneous effect of multiple independent variables on
the dependent variable. As in the case of multiple regression, this method is effective in controlling for
confounding factors.

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