A Bestiary of Experimental and Sampling Designs

A Bestiary of Experimental and Sampling Designs

The design of an experiment • The details of: • replication, • randomizations, • and independence

We cannot draw blood from a stone • Even the most sophisticated analysis CANNOT rescue a poor design

Categorical variables • They are classified into one or more unique categories • Sex (male, female) • Trophic status (producer, herbivore, carnivore) • Habitat type (shade, sun)

Continuous variables • They are measured on a continuous numerical scale (real or integer values) • Size • Species richness • Habitat coverage • Population density

Dependent and independent variables • The assignment of dependent and independent variables implies an hypothesis of cause and effect that you are trying to test. • The dependent variable is the response variable • The independent variable is the predictor variable

Ordinate (vertical y-axis) Abscissa (horizontal x-axis)

Four classes of experimental design

The analysis of covariance • It is used when there are two independent variables, one of which is categorical and one of which is continuous (the covariate)

Regression designs • Single-factor regression • Multiple regression

Single-factor regression • Collect data on a set of independent replicates • For each replicate, measure both the predictor and the response variables. • Hypothesis: seed density (the predictor variables) is responsible for rodent density (the response variable)

Plots Variables

Single-factor regression • You assume that the predictor variable is a causal variable: changes in the value of the predictor would cause a change in the value of the response • This is very different from a study in which you would examine the correlation (statistical covariation) between two variables

In regression (Model I) • You are assuming that the value of the independent variable is known exactly and is not subject to measurement error

Assumptions and caveats • Adequate replication • Independence of the data • Ensure that the range of values sampled for the predictor variable is large enough to capture the full range of responses by the response variable • In some cases, is useful to ensure that the distribution of predictor values is approximately uniform within the sample range, but…

Multiple regression • Two or more continuous predictor variables are measured for each replicate, along with the single response variable

Assumptions and caveats • Adequate replication • Independence of the data • Ensure that the range of values sampled for the predictor variables is large enough to capture the full range of responses by the response variable • Ensure that the distribution of predictor values is approximately uniform within the sample range

Multiple regression • Ideally, the different predictor variables should be independent of one another. This collinearity makes it difficult to estimate accurately regression parameters and to tease apart how much variation in the response variable is accurately associated with each of the predictor variables

Multiple regression • As always replication becomes important as we add more predictor variables to the analysis. • In many cases it is easier to measure additional predictor variables than is to obtain additional independent variables • Avoid the temptation to measure everything that you can just because it is possible

Multiple regression • It is a mistake to think that a model selection algorithm can identify reliably the correct set of predictor variables

ANOVA designs • Analysis of Variance • Treatments: refers to the different categories of the predictor variables • Replicates: each of the observations made

ANOVA designs • Single factor designs • Randomized block designs • Nested designs • Multifactor designs • Split-plot designs • Repeated measurements designs • BACI designs

Single factor designs • It is one of the simplest, but most powerful, experimental designs, and it can readily accommodate studies in which the number of replicates per treatment is not identical (unequal sample size)

Single factor designs • In a single factor design, each of the treatments represent variation in a single predictor variable or factor • Each value of the factor that represents a particular treatment is called a treatment level

Good news, bad news: • It does not explicitly accommodate environmental heterogeneity and we need to sample the entire array of background conditions • It means the results can be generalized across all environments, but… • If the noise is much stronger than the signal of the treatments , the experiment have low power, and the analysis may not reveal treatment differences unless there are many replicates

Randomized block designs • One effective way to incorporate environmental heterogeneity • A block is a delineated area or time period within which environmental conditions are relatively homogeneous • Blocks can be placed randomly or systematically in the study area, but should be arranged so that the environmental conditions are more similar within blocks than between them

Randomized block designs • Once blocks are established, replicates will still be assigned randomly to treatments, but a single replicate from each of the treatments is assigned to each block

Caveats • Blocks should have enough room to accommodate a single replicate of each of the treatments, and enough spacing between replicates to ensure their independence • The blocks themselves also have to be far enough apart from each other to ensure independence of replicates among blocks

Randomized block designs Valid blocking Invalid blocking

Advantages • It can be used to control for environmental gradients and patchy habitats • It is useful when your replication is constrained by space or time • Can be adapted for a matched pair lay-out

Disadvantages • If the sample size is small and the block effect weak, the randomized block design is less powerful than the simple one-way layout • If blocks are too small you may introduce non-independence by physically crowding the treatments together • If any of the replicates are lost, the data from the block cannot be used unless the missing values can be estimated indirectly

Disadvantages • It assumes that there is no interaction between the blocks and the treatments • Replication within blocks will indeed tease apart main effects, block effects, and the interaction between blocks and treatments. It will also address the problem of missing data from within a block

Nested designs • It is any design in which there is subsampling within each of the replicates • In this design the subsamples are not independent of one another • The rational of this design is to increase the precision with which estimate the response of each replicate

Advantages • Subsampling increases the precision of the estimate for each replicate in the design • Allows to test two hypothesis: • First: Is there variation among treatments? • Second: Is there variation among replicates within treatments? • Can be extended to a hierarchical sampling design

Disadvantages • They are often analyzed incorrectly • It is difficult or even impossible to analyze properly if the sample sizes are not equal • It often represents a case of misplaced sampling effort Subsampling is not solution to inadequate replication

Randomized block designs • Strictly speaking, the randomized block and the nested ANOVA are two-factor designs, but the second factor (blocks, or sub samples) is included only to control for sampling variation and is not the primary interest

Multifactor designs • the main effects are the additive effects of each level of one treatment average over all levels of the other treatment • the interaction effects represent unique responses to particular treatment combinations that cannot be predicted simply from knowing the main effects.

Multifactor designs • In a multifactor design, the treatments cover two (or more) different factors, and each factor is applied in combination in different treatments. • In a multifactor design, there are different levels of the treatment for each factor

Multifactor designs • Why not just run two separate experiments? • Efficiency. It is more cost effective to run a single experiment than to run two separate experiments • It allows to test for both main effects and for interaction effects

Interactions 60 50 40 West 30 North 20 10 0 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

Orthogonal • The key element of a proper factorial design is that the treatments are fully crossed or orthogonal : every treatment level of the first factor must be represented with every treatment level of the second factor • If some of the treatment combinations are missing we end with a confounded design

Two single-factor design

Advantages • The key advantage is the ability to tease apart main effects and interactions between factors, The interaction measures the extent to which different treatment combinations act additively, synergistically, or antagonistically

A Bestiary of Experimental and Sampling Designs