Introduction

Chapter 1. If Having Children Increases Your Happiness?

How Would You Know
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You must read each slide, and complete any questions on the slide, in sequence.
confounding variables
In an experiment, a factor other than the independent variable that might produce an effect

How Would You Know If Having Children Increases Your Happiness?

By:

C. Nathan DeWall, University of Kentucky

David G. Myers, Hope College

REFERENCES

Blanchflower & Oswald, 2004; Deaton & Stone, 2013; Glenn & Weaver, 1979

Eibach & Mock, 2011

Nelson et al., 2013

Ross & Van Willigen, 1996; Kahneman et al., 2004

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Before you begin your study, you need to make a plan. A research design helps steer your study in the right direction. If you choose the correct design, it will guide you toward the clearest answer. But if you make the wrong selection, it might make it difficult to interpret your results. Or worse, you might not even be able to conduct your study at all!

So, choose your research design wisely.

Click on the research design your study will use to find out if having children increases your happiness.

A.
B.
C.
Correct.
Incorrect.
{true} setModel("research_design", qqMC1 )

Now that you’ve chosen your design, it’s time to pick your participant sample. Who will you select as your research participants? Click one of the options below.

A.
B.
C.
D.
Correct.
Incorrect.
{true} setModel("participant_sample", qq1 == true)

Below, there are three ways to measure happiness. Click the option you want to use.

A.
B.
C.
Correct.
Incorrect.
{true} setModel("measuring_happiness", qq1 == true)

Great job! You chose the correct research design, participants, and form of measurement.

Next

screen7
(model.research_design && model.participant_sample && model.measuring_happiness)

You chose the correct participants and form of measurement, but not the correct research design. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(!model.research_design && model.participant_sample && model.measuring_happiness)

You chose the correct research design and form of measurement, but not the correct participants. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(model.research_design && !model.participant_sample && model.measuring_happiness)

You chose the correct research design and participants, but not the correct form of measurement. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(model.research_design && model.participant_sample && !model.measuring_happiness)

You chose the correct research design, but not the correct participants or form of measurement. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(model.research_design && !model.participant_sample && !model.measuring_happiness)

You chose the correct form of measurement, but not the correct research design or participants. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(!model.research_design && !model.participant_sample && model.measuring_happiness)

You chose the correct participants, but not the correct research design or form of measurement. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(!model.research_design && model.participant_sample && !model.measuring_happiness)

You did not choose the correct research design, participants, or form of measurement. Watch the animated tutorial on research methods, and then try again.

View Animated Tutorial

screen6a
(!model.research_design && !model.participant_sample && !model.measuring_happiness)
screen6a
{true} player.show_question_again(2)
{true} player.show_question_again(3)
{true} player.show_question_again(4)

Watch this tutorial to improve your understanding of research methods.

Next

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In the previous activity, you chose to use a correlational study. You asked people to report whether they were a parent and then their level of happiness. And you replicated a previously found result: Parents were generally happier than non-parents.

But how can you be sure that parenting predicts greater happiness?

You need to rule out confounding variables—factors other than parenting that might explain why the parents were so happy.

measuringTraits

How would you measure the traits of your research participants? Please click on your selection.

A.
B.
C.
Correct.
Incorrect.
{true} setModel("measuringTraits", (qq1 == true))

Select all of the factors that might account for the relationship between parenthood and happiness.

Marital status
Favorite color
Age
Preference for coffee or tea
Socioeconomic status
Number of Miley Cyrus songs memorized
Table
Correct.
Incorrect.
{true} setModel("confoundingVariablesIsCorrect", (qq1 == true) && (qq2 == true) && (qq3 == true) && (qq4 == true) && (qq5 == true) && (qq6 = true))

Now that you've selected your confounding variables, how do you control for them? There are fancy ways to make sure confounding variables don't ruin your study, but it really comes down to a single decision. Click on an option below.

A.
B.
Correct.
Incorrect.
{true} setModel("controlling", (qq1 == true))

Excellent work! When you conduct a correlational study, you want to collect relevant personality and demographic factors. This way you’ll know more about the participants in your sample—and you’ll be armed with possible confounding variables that could explain your study’s results. Picking appropriate confounding variables can be difficult. You chose confounding variables that might relate to greater happiness or being a parent, such as age and socioeconomic status. Being in a long-term committed relationship also relieves the stress of parenting, so it’s good that you chose marital status as a potential confounding variable. Finally, you chose to include the confounding variables in your analysis. That’s a great decision. By including confounding variables in your analysis, you will know whether parenthood really does relate to being happier.

Next

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(model.measuringTraits && model.confoundingVariablesIsCorrect && model.controlling)

Good start. You chose the correct way to measure the traits of your research participants. Watch the animated tutorial on confounding variables, and then go back and try the other two questions again.

Next

screen11a
(model.measuringTraits && !model.confoundingVariablesIsCorrect && !model.controlling)

You’re so close! You chose the correct way to measure the traits of your research participants. You also picked the best confounding variables, but you didn’t make the correct decision about whether to include them in your analysis. Watch the animated tutorial on confounding variables, and then try again.

Next

screen11a
(model.measuringTraits && model.confoundingVariablesIsCorrect && !model.controlling)

You’re so close! You chose the correct way to measure the traits of your research participants. You also made the correct decision to include confounding variables in your analysis, but you didn’t choose the most appropriate confounding variables. Watch the animated tutorial on confounding variables, and then try again.

Next

screen11a
(model.measuringTraits && !model.confoundingVariablesIsCorrect && model.controlling)

That’s pretty good. You chose the correct confounding variables. Unfortunately, your other two answers were incorrect. Watch the animated tutorial on confounding variables, and then try again.

Next

screen11a
(!model.measuringTraits && model.confoundingVariablesIsCorrect && !model.controlling)

Nice work! You chose the correct confounding variables and made the correct decision to include them in your analysis. You didn’t choose the correct way to measure your participants’ traits. Watch the animated tutorial on confounding variables, and then go back try again.

Next

screen11a
(!model.measuringTraits && model.confoundingVariablesIsCorrect && model.controlling)

Good start. You chose to include confounding variables in your analysis. Watch the animated tutorial on confounding variables, and then go back and try the other two questions again.

Next

screen11a
(!model.measuringTraits && !model.confoundingVariablesIsCorrect && model.controlling)

Oops. None of your answers were correct. Watch the animated tutorial on confounding variables, and then try again.

Next

screen11a
(!model.measuringTraits && !model.confoundingVariablesIsCorrect && !model.controlling)
screen11a
{true} player.show_question_again(8)
{true} player.show_question_again(9)
{true} player.show_question_again(10)

Watch this tutorial to improve your understanding of confounding variables.

Now try answering the questions again.

Next

measuringTraits
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Which of the following statements is TRUE?

A.
B.
C.
D.
Correct.
Incorrect.

In your study, socioeconomic status was a confounding variable because:

A.
B.
C.
D.
Correct.
Incorrect.

Why was it good to account for confounding variables?

A.
B.
C.
D.
Correct.
Incorrect.

Your study showed that having children was related to higher levels of happiness. Your study also showed that:

A.
B.
C.
D.
Correct.
Incorrect.

How would you know if having children increases your happiness?
Use a correlational design, control confounding variables, and account for those confounding variables when analyzing your study responses.