Chapter 1. Do Speed Daters Become Pickier the Later It Gets?

Introduction

This activity will ask you to create a design to test the impact of gender and time on perceived attractiveness. Within your design, you will investigate the combined effect of a between-subjects variable and a within-subjects variable on another variable.

Dr. Melanie Maggard

Dr. Natalie J. Ciarocco, Monmouth University

Dr. David B. Strohmetz, Monmouth University

Dr. Gary W. Lewandowski, Jr., Monmouth University

Something to Think About…

Scenario: Dating has changed drastically over the years, from chaperoned visits with family members to computerized matchmaking on dating Web sites. Speed dating, in particular, has exploded in popularity since its creation in 1998, when a rabbi started this event in a Los Angeles coffee shop as a way for busy adults to meet many potential partners in a short period of time. Though the effectiveness of speed dating for finding long-term mates is questionable, it does present us with an interesting opportunity in today’s fast-paced world.

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Something to Think About…

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We might wonder how exposure to so many potential partners, whether good or bad, might change how we perceive individuals as the night progresses. Similar to the “beer goggle effect,” where, the more alcohol we drink, the more likely we are to view people as attractive, is it possible that we become less picky or rate individuals as more attractive as the night goes on? Could fear of not finding a match at the end of the night make us judge those we meet as more attractive than we normally would?

Our Research Question

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These questions prompt us to develop a research study that examines the relationship between gender, time, and attractiveness. But first, you will need a framework to help you explore this topic. Research studies all start with a question, so here is your chance to ask one of your own.

Picking the Best Design

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Now that you have a research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), you must decide which type of research design will best answer your research question. To narrow things down, consider the following:

• Nonexperimental Design

• Experimental Design

Picking the Best Design

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Having decided that your research question requires a comparison between groups, you must determine the best comparison to make. However, keep in mind that there are multiple comparisons to be made: between genders and times of rating.

Picking the Best Design

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Picking the Best Design

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Now that you have selected an experimental design that compares males’ and females’ ratings of perceived attractiveness at early, middle, and late points in the speed-dating event, you can identify your independent and dependent variables.

• Independent Variable (IV)

• Dependent Variable (DV)

Picking the Best Design

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Picking the Best Design

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In this study, participants can only be in 1 of the 2 groups for gender; they are either male or female. Thus, gender is a between-subjects variable.

• Between-subjects Design

However, time of rating is being measured as how the participants’ ratings change over time. This means each participant gives 3 ratings, early, midway, and late in the speed-dating event. Time of rating is a within-subjects variable.

• Within-subjects Design

Picking the Best Design

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With the research question (“How do gender and time of rating during a speed-dating event influence perceived attractiveness?”), comparisons, and types of variables in mind, consider the following designs:

• Repeated-measures Design

• Factorial Design

• Mixed Design

Picking the Best Design

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You have developed an experiment with 2 independent variables (gender and time of rating) and 1 dependent variable (perceived attractiveness). Because we have 1 between-subjects variable (gender) and 1 within-subjects variable (time of rating), we have a mixed design.

• Mixed Design

Operationally Defining the Independent Variables

Next, we need to operationally define our independent variables. The first independent variable (IV), gender, is simply the gender of the participant, with the 2 groups being males and females. We will operationally define the second independent variable (IV), time of rating, by determining exactly how we will manipulate it. As we do, we’ll want to be sure our study has a high level of experimental and mundane realism.

• Experimental Realism

• Mundane Realism

Operationally Defining the Independent Variables

The task that is highest in experimental and mundane realism is participants rating the level of attractiveness of individuals they have been exposed to up to certain points early, midway, and late in the speed-dating event. Since we have 1 between-subjects variable (gender) and 1 within-subjects variable (time of rating), we will be measuring males at each of the 3 times and females at each of the 3 times.

Operationally Defining the Independent Variables

We have decided to compare individuals early, midway, and late in a speed-dating event. Since these events typically last about 2 hours, we will use this length for our study. Each participant will meet with a potential date for 5 minutes before rotating to the next person. At the times of the ratings, we will ask participants to rate the perceived attractiveness of the dates they have met thus far. However, before proceeding, we need to clarify exactly when the ratings will occur.

Operationally Defining the Independent Variables

The following table illustrates the 6 combinations in our 2 x 3 mixed design, where each person will be assigned to 1 of the gender conditions and all 3 of the time of rating conditions:

• A: Males + Early

• B: Males + Middle

• C: Males + Late

• D: Females + Early

• E: Females + Middle

• F: Females + Late

Operationally Defining the Dependent Variable

You have now established the key comparisons between the various conditions created by the 2 independent variables. Next, we need to specify the exact nature of our dependent variable, perceived attractiveness. First, consider the following:

Choosing the Best Measure

We know we want to use a self-report measure to measure perceived attractiveness. Now it is time to determine which type of self-report measure to use. Keep in mind how many and what types of questions, reliability, validity, and sensitivity would be appropriate for our study.

• Reliability

• Validity

• Sensitivity

Determining Your Hypotheses

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Now that you have determined what you will manipulate and measure, you must formulate an experimental hypothesis. Because we have multiple independent variables, we will also need multiple hypotheses: main effect hypotheses and interaction effect hypotheses.

• Main Effect Hypothesis

• Interaction Effect Hypothesis

Determining Your Hypotheses

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Determining Your Hypotheses

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Now that we have our main effect hypotheses, we must create the interaction effect hypothesis.

Finding a Sample

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Before you can conduct your experiment, you need to determine exactly whom you want to study and where you can find this target sample.

Submitting to the IRB

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Now that you have determined how you will collect your data and your intended sample, you must submit your research procedure to the Institutional Review Board (IRB) for ethical approval. The IRB or ethics board will determine whether or not your study meets all ethical guidelines.

• IRB

Each IRB has its own protocol which conforms to the national standard when a researcher submits an application for proposed research to be reviewed. In addition to the appropriate paperwork and other information submitted to the IRB, the board would consider the following description during their evaluation of your proposed experiment:

• The purpose of this research is to determine whether there is an interaction effect of gender (males or females) and time of rating (early, middle, or late) on perceived attractiveness. To study this topic, participants who are registered for a local speed-dating event will be told upon arrival that a study is being conducted on the attractiveness of individuals who participate in speed-dating events. Participants will be asked to rate the attractiveness of the daters they have met thus far 15 minutes after the event starts (early), 1 hour after (middle), and at the end of the 2-hour event (late). Participants will be debriefed at the end of the event regarding the true nature of the study.

Responding to the IRB

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The IRB reviewed your submission and has 1 concern. Although the study appears to present less than minimal risk to participants, there is no mention of informed consent and voluntary participation.

You must now determine how to respond to the IRB, keeping in mind the ethics of respect for persons and autonomy.

Running the Study

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Now that we have secured the IRB’s approval, we should determine what the entire study will look like. Below are the steps of the study; can you place them in the proper order?

Collecting Data

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Now that you have a sense of how to conduct this study, it is time to see what data from this study might look like.

If you were to run a full version of this study, you would want to have at least 30 participants in each between-subjects group (males and females). Because you have a within-subjects design, each participant will be exposed to all levels of the time of rating independent variable. Thus, we need 60 participants for our study.

Example Data Set

This is an example of what your data set would look like. The top row shows the variable names; the other rows display the data for 10 participants.

In the “Gender” column, a 1 = Male, and a 2 = Female. The ratings of attractiveness recorded after 15 minutes into the speed-dating event are located under “Early,” after 1 hour under “Middle,” and after 2 hours under “Late.” Ratings range from 1 (very unattractive) to 10 (highly attractive).

Selecting the Proper Tool

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Now that you have collected your data, you must decide the best way to summarize your findings. The decisions you made about how to collect your data dictate the statistics you can use with your data now. First, you need to consider if your study is descriptive or inferential.

• Descriptive

• Inferential

Tutorial: Evaluating Output

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The following is an example of output for another mixed design ANOVA where participants experienced only multiple conditions. This study was about how hours slept at night (6 hours, 8 hours, and 10 hours) and gender (male or female) influence self-reported happiness. In this study, we recorded happiness after each night of sleep for each participant; thus, hours slept is a within-subjects variable and gender is a between-subjects variable.

Tutorial: Evaluating Output

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To report these numbers in a results section, put the numbers in as follows:

Tutorial: Evaluating Output

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Your Turn: Evaluating Output

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Below is the output from your study:

Your Turn: Evaluating Output

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Below is the output from your study:

Your Turn: Evaluating Output

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Below is the output from your study:

Your Turn: Evaluating Output

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Below is the output from your study:

Your Turn: Evaluating Output

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Based on the results of your statistical analyses, match the correct number in the “Answer” column to the term requested under “Prompt”:

Activity: Graphing Results

Move the bars to represent the data in the table.

Your Turn: Results

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Now that you have worked with your data, you must determine the best way to express your findings in written form. You must be sure that how you describe your findings accurately represents the data.

Take Home Message

Now that you have determined how to express your findings in a scientifically responsible way, you also need to be able to talk about what your findings mean in everyday terms so that the world can benefit from your science.

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Congratulations! You have successfully completed this activity.

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