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How to Develop and Write Hypotheses
Published in Lisa Chasan-Taber, Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
In epidemiology and preventive medicine, the independent variable is termed the exposure variable. This term is used broadly to encompass both risk factors and protective factors for some type of outcome (typically a disease). A common misperception is to view exposures as referring to adverse factors (e.g., cigarette smoking, drug use), but the definition is actually more broad. Specifically, the independent variable is any factor that may lead to a health outcome. In a similar fashion, dependent variables or outcomes in epidemiology and preventive medicine are often diseases but can also be positive outcomes such as psychological well-being.
Quantitative analysis
Published in Jeremy Jolley, Introducing Research and Evidence-Based Practice for Nursing and Healthcare Professionals, 2020
We need to be clear about the relationship between a ‘group’ of data and ‘variables’. We already know that there are two broad categories of variable; that is ‘independent variable’ and ‘dependent variable’. The independent variable might be ‘treatment type’ and the dependent variable might be ‘recorded pain level’. However, although this study might have only one independent variable (‘treatment type’), this might be divided into three groups or ‘conditions’ (when these are categories, they are sometimes called ‘factors’). These conditions might be ‘drug-A’, ‘drug-B’ and ‘control’ (no drug). So, here we have one independent variable with three groups (three conditions). These three ‘parts’ of the independent variable are going to produce three sets of data (groups of data).
Fundamentals
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Another important factor in a statistical analysis is the correlation of two variables x and y. If a variable y varies in the same direction as an independent variable x, then the variable y is positively correlated with variable x. It is used to study the relationship between independent variables and dependent variables in regression analysis. Independent variables are controlled parameters in an experiment. Dependent variables are the measured outcomes. The metrics to study the correlation of two variables x and y is called covariance. Covariance measures the spread of the points in a two-dimensional plane and is defined as the average deviation of all the points from the centroid given by (μx, μy) as shown in Equation 2.12.
Statin adherence in patients with high cardiovascular risk: a cross-sectional study
Published in Postgraduate Medicine, 2023
Yusuf Cetin Doganer, Umit Aydogan, Umit Kaplan, Suat Gormel, James Edwin Rohrer, Uygar Cagdas Yuksel
Statistical analysis was performed using SPSS software (version 12.0; SPSS Inc., Chicago, IL, USA). The dependent variable of the study was determined as the level of medication adherence. The independent variables were socioeconomic characteristics, chronic diseases, drug use characteristics (cholesterol treatment duration, number of drugs, etc.), general health assessment, and depressive symptoms. Data were expressed as mean ± SD and/or percent (%). Descriptive statistics for numerical variables (mean, median, standard deviation, minimum and maximum) and frequency tables were given for categorical variables. The Kolmogorov–Smirnov test was applied to determine whether the normal distribution assumption for continuous variables was provided. Quantitative data were evaluated using an unpaired t-test or the Mann-Whitney U test, as appropriate. A comparison of categorical variables was performed using the chi-square test. p-value <0.05 was considered statistically significant. Group comparisons for each explanatory variable have a single p-value since comparisons between groups were analyzed by One-way ANOVA tests and Chi-square tests.
Procedural Integrity Reporting in the Journal of Organizational Behavior Management (2000–2020)
Published in Journal of Organizational Behavior Management, 2022
Daniel Cymbal, David A. Wilder, Nelmar Cruz, Grant Ingraham, Mary Llinas, Ronald Clark, Marissa Kamlowsky
Procedural integrity, or treatment fidelity or procedural fidelity, refers to the extent to which the independent variable is implemented as described, and is also important for internal validity. Ideally, researchers collect formal data on the accuracy of implementation of the independent variable and report these data when publishing their research, similar to the way in which data on interobserver agreement on the dependent variable are reported. Of course, in some research (e.g., laboratory-based research) independent variables may be implemented mechanically or digitally. In these studies, the independent variable implementation is less subject to error, so reporting data on procedural integrity may be less important. However, most applied behavior analytic research is conducted in the “field” and interventions are typically delivered by the experimenter, therapist, parent, manager, or consultant, possibly making these independent variables more at-risk for implementation errors.
Experimental Research Methodologies in Organizational Behavior Management
Published in Journal of Organizational Behavior Management, 2021
Tyler G. Erath, Azure J. Pellegrino, Florence D. DiGennaro Reed, Sandra A. Ruby, Abigail L. Blackman, Matthew D. Novak
The goal of science is to understand phenomena and their underlying causes – that is, to discover nature’s truths (Cooper, Heron, & Heward, 2020). The role of the scientist, then, is to discover truths through observation and controlled investigation. Scientific knowledge is obtained through experimentation, which broadly involves identifying relations between two or more events (Kazdin, 2011; Skinner, 1965). A researcher demonstrates a functional relation when one event (i.e., independent variable) reliably and consistently produces an effect on another event (i.e., dependent variable). Through controlled experimentation, a researcher rules out variables other than the independent variable (i.e., extraneous variables or confounding variables) as potential explanations for the observed relation, thereby increasing the researcher’s confidence in the findings (Johnston & Pennypacker, 2009).