Question

Researchers at the University of Houston decided to test the hypothesis that restaurant servers who squat to the level of their customers would receive a larger tip ("Effect of Server Posture on Restaurant Tipping," Journal of Applied Social Psychology [1993]: \(678-685\) ). In the experiment, the waiter would flip a coin to determine whether he would stand or squat next to the table. The waiter would record the amount of the bill and of the tip and whether he stood or squatted. a. Describe the treatments and the response variable. b. Discuss possible extraneous factors and how they could be controlled. c. Discuss whether blocking would be necessary. d. Identify possible confounding variables. e. Discuss the role of randomization in this experiment.

Short answer

a. The treatments are the postures of the server (standing or squatting) and the response variable is the tip size. b. Potential extraneous variables include the overall quality of service, size of the customer's party, and customer's mood. They can be controlled by keeping the experimental setting consistent. c. Blocking may not be necessary in this controlled setting. d. Confounding variables could be the size of the bill. e. A coin flip (a form of randomization) is used to determine the treatment each customer receives, reducing the effects of lurking variables and bias.

Step-by-step solution

Identifying treatments

The treatments refer to the conditions which are applied to each experimental unit. In this case, the treatments are the different postures of the waiter or server, which are either standing or squatting.

Identifying the response variable

The response variable is the outcome or the variable that you measure in an experiment. Here, the response variable to the treatments is the amount of tip the waiter receives.

Discussing potential extraneous variables

Extraneous variables are variables not of primary interest but can affect the response variable. In this experiment, such factors could be the overall quality of food and service, customer's mood, size of the customer's party, and customer's tipping habits, among others. They can be controlled by making the experiment as consistent as possible, such as by conducting the experiment with the same waiter at the same restaurant and keeping the quality of food and service consistent.

Discussing blocking

Blocking is a technique used in experiments to remove the effects of lurking variables. In this context, it may not be necessary as the experiment is conducted in a controlled setting where the server performs both the treatments. However, if the experiment includes multiple servers or locations, blocking could be used.

Identifying confounding variables

Confounding variables are extraneous variables that also vary with the independent variable, causing potential confusion. In this experiment, potential confounding variables could be the size of the bill, as larger bills might naturally attract larger tips.

Discussing the role of randomization

Randomization is crucial in experiments to reduce the effects of lurking variables and bias. Here, the server uses a coin flip to determine the treatment which is a form of random assignment. This allows each customer an equal chance of receiving either treatment and helps to generalize the results to a larger population.

Key concepts

Randomization in Experiments

When conducting experiments, randomization serves as a powerful tool to ensure impartiality and reliability in the results. By randomly assigning subjects to different treatment groups, researchers work to eliminate the influence of bias. For instance, in the University of Houston restaurant server study, the act of flipping a coin to decide whether the server would stand or squat helped to evenly distribute any unobserved characteristics among the groups. Such characteristics could include the day of the week or the time of day, which might unwittingly affect the tipping behavior.

Randomization is a cornerstone of experimental design and critical for establishing causal relationships. Its primary aim is to make the treatment groups as similar as possible, except for the treatment itself. Therefore, if a difference in tipping amounts is observed, it can be more confidently attributed to the server’s posture rather than other hidden factors.

Response Variable

In any experiment, the response variable is the particular quantity that researchers aim to measure. It captures the effect of the experimental treatments. In the server posture study, the response variable is the amount of the tip received by the waiter. This variable is directly influenced by the treatment — the server's posture, whether standing or squating next to the table.

A well-defined response variable is key, as it reflects the outcome researchers are interested in investigating. Accurately measuring this response is critical, as it determines whether the treatment has a statistically significant effect.

Extraneous Variables

Experiments often have extraneous variables, which are not the main concern of the study but might still affect the outcome. These are different from the independent variables being manipulated, such as the server's posture. In the referenced experiment, potential extraneous variables include the quality of the service, the customer’s mood, or how crowded the restaurant is at the time of service.

Even though these factors are not of primary interest, if not controlled, they can add variability to the response variable and potentially skew the results. Methods to manage extraneous variables include holding them constant for all experimental units, such as ensuring all customers receive comparable service or using statistical control techniques in the analysis phase.

Confounding Variables

Confounding variables are a type of extraneous variable that can muddle the relationship between the independent and response variables. They're essentially extraneous variables that change along with the independent variable. In the server study, confounding variables could include factors like the customer's previous experiences at the restaurant or their personal beliefs about tipping.

Importantly, confounding can lead to erroneous conclusions about cause-and-effect relationships. For example, if customers with larger bills are both more likely to receive squatting service and to give larger tips, the bill size could be a confounding variable that needs to be accounted for in the study's design or analysis.

Blocking in Experimental Design

Blocking is a technique used to control the effect of potential confounding variables identified before the experiment. It involves grouping experimental units into blocks based on these variables, and then randomly assigning treatments within each block. Blocking ensures that each treatment is applied in a balanced way across each block, thus controlling for the confounding variable's effects.

In the case of the University of Houston study, blocking might not be necessary since the experiment involves only one server in a consistent environment. However, if the researchers decided to include variations, such as different types of restaurants or multiple servers with different personal serving styles, blocking could help to isolate the effect of the server’s posture on tipping away from these potential confounders.