Introduction to the Design of Experimental and Observational Studies. Chapter 15 Lecture 5 Psych 791

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1 Introduction to the Design of Experimental and Observational Studies Chapter 15 Lecture 5 Psych 791

2 Is Experimental Design Important? A well designed study will make analysis easy. A poorly designed study can make even the biggest differences impossible to detect. The proper design of a study is often more important than the actual analysis. How are statistics and design related?

3 Relationship Stats & Design Thinking from a statistical consulting prospective, most people think they can just collect the data any way they want, and just tell someone to analyze it. Some of the biggest problems from a consulting prospective is having data that you just can t analyze. Your role as a statistician really begins when you plan your study.

4 Today s Lecture Today we are going to talk about some types of studies that exist, some buzz words and terminology related to experimental design, and just get you familiar with the different types of studies that exist. You will use this knowledge the rest of this semester (and your research career) in thinking about the proper way to analyze data. The way the data is collected plays a MAJOR role in the way you analyze it. We are going to learn some experimental design issues and the rest of the semester we will learn how to analyze data collected from these particular designs.

5 Experimental Studies Experimental studies designed to test a cause and effect relationship. Causality can be inferred from results. You alter levels of some IV to see how it effects your DV. Example: Give people a different number of beers (controlled) to see how their performance on a test changes.

6 Experimental Studies Buzz words Experimental group subjects with treatment (have beer). Control group subject absent of treatment (no beer). Treatments levels of the DV (number of beers). Experimental units pc for subjects, or whatever you are testing on.

7 Randomization This buzz word gets it s own page because it is just that important. Randomization is important for experimental studies so that you control for any other variable that may effect your DV. When you randomize, you KNOW the beer is what caused the changes in the test. Randomization is a necessity for any statistical analysis. If you don t randomize, you need to control for other factors in your model that may have an effect.

8 Beer Study Example Let s think of a good way to design this beer study. Now, let s think of some problems that could be created in our study if you fail to randomize.

9 Observational Study Main difference between obs and exp ---- Randomization. Basically, we study the way the IV and DV variables change together, but do not control the levels of the IV. How would we change our beer/test example into an observational study?

10 Mixing the two We can also do both at the same time. This would be just as it sounds, we would control and randomize some IV and observe some other variables. How could we alter our beer/test example to be a mixed design study.

11 Experimental Design The design of an experiment refers to the structure of the experiment and the following: Set of explanatory factors. Set of treatments. Set of experimental units. Rules and procedures for random assignment. Outcome measures.

12 Factors Factors big word for explanatory variable (IV) (Beer in our example). Experimental Factor controlled. Observational Factor not controlled. Qualitative Factor levels. Quantitative Factor measured. If you talk about a factor level, that is the particular level of the factor. If you measure gender, a factor level is female.

13 Crossed & Nested Factors Crossed Factor There are subjects in every level of every factor. Nested Factor There are not subjects in every level of every factor. Example Have people drink beer (either 2 or 4 beers) and Eat pretzels (either 1 bag or 2 bags). Crossed (2X2 design four conditions with subjects in them: drink 2 eat 1, drink 2 eat 2, drink 4 eat 1, drink 4 eat 2). Nested (have 2 conditions with subjects: drink 2 eat 1, drink 4 eat 2).

14 Treatments When designing a study, you need to determine how many treatments you want/need. Need to decide: Number of factors Number of levels of each factor Range of levels for quantitative factor Control treatment (if needed)

15 More buzz words Experimental units smallest unit which the experimental treatment can be assigned. Most of the time it a person (or intro psych undergrad). Sample Size number of subjects (or EU) that you have. This is often important for statistical considerations. Replication You often can give subjects more than one treatment. The replication of the order of the treatments is often a factor in your model.

16 Randomization Take 2 Randomization is so important it gets another slide. Again, randomization is important to run almost any statistical analysis. Need to make sure subjects are assigned to condition randomly, they are equally likely to be in any condition. If you put all the females into condition a and all the males into condition b, how do you know if it was the condition that caused the change or their gender? Blocking First, you divide subjects into blocks (some variable you want to control for), then run the same experiment on each block So, you make 2 blocks of subjects (1 male block, 1 female block), then you run the entire experiment on both of the blocks.

17 Measurement How you measure the variables is also important. You want to stay away from measurement bias a bias in the way a variable is measured that will alter your results. An experimenter may often be guilty of experimenter bias (I know it is the treatment so I think it will do better). Double-blind studies are often performed (experimenter and subject both do not know which condition they are in).

18 Standard Experimental Designs To follow are some more buzz words which you should become familiar with. To follow are some experimental designs that are often used and analyzed in psychology. The experimental design dictates the type of analysis that is/can be performed.

19 Completely Randomized Design Every subject is randomly assigned to one treatment condition (may have multiple factors and multiple levels of each factor). We then can look at a linear statistical model that accounts for this randomization: Y = [constant] + [treatment] + [error]

20 Factorial Experiments Completely randomized designs can have a single factor or multiple factors. Those with multiple factors are referred to as completely randomized factorial designs (ex. 2X2, 4X6X2). The structure of the linear model is as follows: Y = [constant] + [first order] + [interaction] + [error]

21 Randomized Complete Block Design Puts together the idea of a randomized design and a blocking design. So you create the blocks, and then randomize blocks into the treatment conditions (so subjects are in blocks, then the blocks are in the treatment conditions). Model for this would be: Y = [constant] + [treatment] + [block] + [error]

22 Nested Designs These are designs in which the subjects are nested within some of the factor levels. First, you have some factor, say classroom, and you want to look at the students in each classroom. The students are nested within the classroom (not randomized and you cannot have students cross over to the other classroom).

23 Repeated Measures Design This is a design where all subjects receive every experimental condition. You wear out the subjects. Another more complicated design includes randomization and repeated measures. Split-Plot design (popular at the University of Illinois at the morrow plots). Create treatments, randomize and replicate.

24 Morrow Plots

25 University of Illinois

26 Incomplete Block Design Take a Randomized Complete Block Design, then just take some of the blocks out of each condition. So, you block the subjects, then take each of the blocks and randomly assign them to a subset of the conditions (for example, you have 10 blocks and 5 conditions, but due to financial limitations you can only give each block 3 conditions, give each block 3 conditions so that you have an equal number of blocks in each condition)

27 Fractional Factorial Design You take a factorial design and then delete some of the levels to make data collection easier, but still test to factor levels. If you have a 2X2X5 design, you don t want to collect 20 conditions, so you carefully select an appropriate subset of conditions.

28 Response Surface Experiments For use when all factors are quantitative and you want to determine precisely the factor level that leads to the optimum response. You are truly looking at a response surface instead of a function because all variables are quantitative.

29 Observational Studies The next set of experiments are observational in nature and do not address a cause and effect relationship. Only establish a relationship between variables.

30 Cross-Sectional Study Observational measurements taken from one or more populations at a single time point. So, you can think of stopping time at some moment, then collecting all the observations you can (or are of interest to you) from your subjects at that moment. Not controlling the variables, simply observing them

31 Prospective Study One or more groups are formed in a nonrandom manner according to the levels of a factor, then these groups are observed over time. You think gender might cause some differences in behaviors over time, separate by gender, then just observe.

32 Retrospective study Defined by some outcome, then look back and collect data from earlier time point. Observe people who either ate cake or didn t, then look back at what led them to eat cake.

33 Matching Matching is the observational equivalent to blocking. You take out additional error in the model by matching subjects based on certain factors. For instance, you may match subjects in two groups based on gender, age, etc.

34 Example 1 An economist compiled data on productivity improvements last year for a sample of firms producing electronic computing equipment. The firms were classified according to the level of their average expenditures for research and development in the past three years (low, moderate, high). Is this study experimental, obervational, or mixed? What are the factors and factor levels?

35 Example 2 In a study to investigate the effect of color of paper (blue, green, orange) on response rates for questionnaires distributed by the windshield method in supermarket parking lots, four supermarket parking lots were chosen in a metropolitan area and 10 questionnaires of each color were assigned at random to cars in the parking lots. Is this study experimental, observational, or mixed? Identify all factors and factor levels.

36 Next Time Chapter 16 Single Factor Studies. Regression (but now with dummy variables).

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