Experimental Designs

By A. Baron. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “A natural approach to observational studies is the matching of treated and control units such that underlying background covariates are balanced (Chapin, 1947; Greenwood, 1945).”
On the Equivalence between Quadrature Rules and Random Features (2015, methodology): “When optimizing the points at which the function is evaluated (adaptively or not), the approximation problem is often referred to as experimental design (Cochran and Cox, 1957; Chaloner and Verdinelli, 1995).”
For objective causal inference, design trumps analysis (2008, methodology): “For may years, causal inference based on randomized experiments, as described, for example, in classic texts by Fisher (1935), Kempthorne (1952), Cochran and Cox (1950) and Cox (1958), was an entirely distinct endeavor than causal inference based on observational data sets, described, for example,…”
DISCO analysis: A nonparametric extension of analysis of variance (2010, background): “Inference requires that random error is normally distributed with mean zero and constant variance (see, e.g., Cochran and Cox (1957), Scheffé (1953), Searle, Casella and McCulloch (1992), Hand and Taylor (1987), or Mardia, Kent and Bibby (1979)).”
Decision-making processes following damage to the prefrontal cortex. (2002, methodology): “This is calculated by the formula from Cochran and Cox (1957): (MSe/n 1 ) + (MSe/n 2 ) + (MSe/n 3 ). . ., where MSe is the mean square error of the interaction term, and n is the number of subjects per group.”
Statistical Analysis in Climate Research (2000, unknown): “Cochran and Cox [87] provide a classical treatment.”
The Relative Validity of Inferences About Mediation as a Function of Research Design Characteristics (2008, methodology): “As noted by Stone-Romero (2006c), a more appropriate distinction is between SP and NSP settings.”
Misunderstandings between experimentalists and observationalists about causal inference (2008, background): “We begin with the specific features of these designs and common statistical assumptions and then discuss the research designs themselves.”
Allelopathy: a tool for weed management in forest restoration (2012, background): “Robust conclusions about the importance of allelopathic interactions in field systems require extensive and interlinked experimental bioassays, biochemical characterization, and field experiments (Inderjit and Callaway 2003).”
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models (2001, unknown): “Normality-based statistical models have been used successfully in nearly all branches of science for decades (Cochran and Cox, 1957; Snedecor and Cochran, 1980; Searle et al ., 1993; Steel et al ., 1997; Federer and Wol (cid:142) nger, 1998).”
Reliability of higher seeding rates of wheat for increased competitiveness with weeds in low rainfall environments (2004, methodology): “In a split-plot, sub-unit treatments are randomized independently within each unit, whereas in a strip-plot treatments are arranged in strips across each replication (Cochran & Cox 1957).”
Genetic parameters of growth and wood quality traits in Picea abies (2004, methodology): “E, 80 m elevation), 3-yr-old seedlings were planted at a spacing of 1.5 /1.5 m in a simple lattice design (Cochran & Cox 1966) with multiple-tree plots (4 /8 trees).”
Experimental design for gene expression microarrays. (2001, background): “Because of confounding, we account for effects that are not of interest without using additional degrees of freedom (Cochran and Cox, 1992).”