Left Out and Feeling Low

Chapter 1. Left Out and Feeling Low

Introduction

Chapter 10

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You must read each slide, and complete any questions on the slide, in sequence.

Nonexperimental Design

A design in which there is no control or manipulation of the variables. This design does not seek to establish cause and effect and instead focuses on describing or summarizing what takes place.

Experimental Design

A design in which the experimenter controls and manipulates the independent variable and makes comparisons between the different levels, allowing the establishment of cause-and-effect relationships between the independent and dependent variables.

Two-group Design

A design that compares 2 groups or conditions and is the most basic way to establish cause and effect.

Pretest-posttest Design

A design where participants are measured before and after exposure to a treatment or intervention.

Repeated-measures Design

A design where participants are exposed to each level of the independent variable and are measured on the dependent variable after each level.

Independent Variable

The variable that influences the dependent variable. In experiments the researcher manipulates or controls this variable.

Dependent Variable

The variable measured in association with changes in the independent variable; the outcome or effect.

Baseline Measurement

The participants’ initial assessment at the beginning of a study.

Experimental Realism

The degree to which a study participant becomes engrossed in the manipulation and truly influenced by it.

Mundane Realism

The degree to which a study parallels everyday situations in the real world.

Reliability

The stability or consistency of a measure.

Validity

The degree to which a tool measures what it claims to measure.

Sensitivity

The range of data a researcher can gather from a particular instrument.

Order Effect

A threat to interval validity in a within-subjects design resulting from influence that the sequence of experimental conditions can have on the dependent variable.

Practice Effect

Changes in a participant’s responses or behavior due to increased experience with the measurement instrument, not the variable under investigation.

Fatigue Effect

Deterioration in measurements due to participants becoming tired, less attentive, or careless during the course of the study.

Carryover Effect

Exposure to earlier experimental conditions influencing responses to subsequent conditions.

Sensitization Effect

Continued exposure to experimental conditions in a within-subjects study increasing the likelihood of hypothesis-guessing, potentially influencing participants’ responses in later experimental conditions.

Counterbalancing

Using all potential treatment sequences in a within-subjects design.

Experimental Hypothesis

A clear and specific prediction of how the independent variable influences the dependent variable.

IRB

A board that reviews the ethical merit of all the human research conducted within an institution.

Descriptive

Describes what is happening.

Inferential

Tests a specific prediction about why something occurs.

Within-Subjects Design

In this activity, you will explore the impact of inclusion and exclusion on self-esteem by creating a design to measure change within individuals.

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: Imagine that you are a child again. You are on the playground surrounded by your peers and it’s time for teams to be chosen for a game of kickball. Your hands start to sweat and your heart races as you think, Please let me get picked. Please let me get the chance to play today. You stand there attentively as the team leaders choose their players, but before you can be picked, they reach the number of players they want. You are devastated! As the teams run off to play, you think, Why didn’t I get picked? Do they not like me? What’s wrong with me?

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Something to Think About…

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Being excluded from social groups can cause us to reconsider how we feel about ourselves, even if these doubts are temporary. As social beings, we tend to feel better when we are part of a group, not excluded from one. Now, we are going to investigate this concept by exploring the impact that being included in or excluded from playing a game can have on our self-esteem. Maybe it isn’t just kids who can experience the feeling of being left out!

Our Research Question

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Based on your experiences with being included in or excluded from groups, you can develop a research study that examines the impact of social exclusion on self-esteem. 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.

Question 1.1

Which of the following research questions would be best to ask given the goal of our study?

Do we have higher self-esteem when we play with others or when we play alone?

Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?

When I am allowed to participate in a game with others, do I feel better about myself?

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Picking the Best Design

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Now that you have a research question (“Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?”), 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

Question 1.2

Does your research question require a nonexperimental design or an experimental design?

Nonexperimental

Experimental

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Picking the Best Design

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Since comparisons must be made in order to answer your research question (“Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?”), consider the following types of experimental designs:

Two-group Design
Pretest-posttest Design
Repeated-measures Design

Question 1.3

Would your research question require a two-group, pretest-posttest, or repeated-measures design?

Two-group Design

Pretest-posttest Design

Repeated-measures Design

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Picking the Best Design

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Having decided that your research question requires multiple measurements to determine impact, you must choose the best comparisons to make.

Question 1.4

Given the research question (“Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?”), which comparison is best?

Inclusion in game vs. Exclusion from game

Self-esteem pretest/no game vs. Inclusion in game vs. Exclusion from game

Self-esteem pretest/no game vs. Inclusion in game vs. Exclusion from game vs. Self-esteem Posttest

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Picking the Best Design

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Now that you know you have an experimental design that compares the pretest to inclusion in a game to exclusion from a game, you can identify your independent and dependent variables.

Independent Variable

(IV)
Dependent Variable

(DV)

Question 1.5

Given the research question (“Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?”), what is your independent variable?

Self-esteem

Type of game

Game condition (Pretest/no game vs. Inclusion in game vs. Exclusion from game)

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Picking the Best Design

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Question 1.6

Given the research question (“Does being included in or excluded from playing a game with others cause an increase or decrease in young adults’ self-esteem?”), what is your dependent variable?

Type of game

Self-esteem

Pretest

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Picking the Best Design

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Because you have an experiment with 1 independent variable and 3 levels (Pretest/no game vs. Inclusion in game vs. Exclusion from game) that each participant is exposed to, you will use a type of within-subjects design called a repeated-measures design. The pretest/no game level serves as a baseline measurement to which the other measurements can be compared.

Baseline Measurement

Question 1.7

Which of the following is an advantage of including a baseline measurement as the pretest in this study?

It reduces the number of measurements required to draw conclusions about the impact of inclusion in or exclusion from playing a game on self-esteem.

It gives us a benchmark against which to compare the results from the various levels (inclusion in game and exclusion from game) to the self-esteem recorded at the onset of the study.

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Picking the Best Design

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Question 1.8

Why have we selected a within-subjects design to address our research question instead of a between-subjects design?

It allows us to draw conclusions about changes within an individual over time due to certain conditions.

It allows us to draw conclusions about differences between groups of individuals who are exposed to different conditions or treatments.

It requires more participants since each participant is exposed to each condition.

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Operationally Defining the Independent Variable

Next, we need to operationally define the independent variable (IV) of game condition 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

Question 1.9

Which of the following study options has the highest level of experimental realism?

Young adults are asked about their experiences of being included in or excluded from playing games.

Young adults are allowed to play a game of catch with others where inclusion in or exclusion from playing the game will naturally occur.

Young adults are allowed to play an electronic game of catch known as “cyberball,” where inclusion in or exclusion from playing a 5-minute game can be manipulated.

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Operationally Defining the Independent Variable

Question 1.10

Which of the following study options has the highest level of mundane realism in regard to playing a game?

Young adults are allowed to play an electronic game of catch known as “cyberball,” where inclusion in or exclusion from playing the game can be manipulated.

Young adults are asked about their experiences of being included in or excluded from playing games of catch.

Young adults are allowed to play a game of hide-and-seek with others where inclusion in or exclusion from playing the game will naturally occur.

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Operationally Defining the Independent Variable

It looks like the task that is highest in experimental and mundane realism involves young adults playing a game of “cyberball.” We know that all participants will be measured at 3 points in time: pretest (before the study begins), after being included in the game, and after being excluded from the game. Therefore, we will have the following design:

Summary of Our Within-Subjects Study
Pretest Measure	IV Level 1	Time 1 Measure	IV Level 2	Time 2 Measure
Self-esteem at Baseline	Inclusion in Game	Self-esteem after Inclusion	Exclusion from Game	Self-esteem after Exclusion

Operationally Defining the Dependent Variable

You have now established the key comparison between Pretest/No game vs. Inclusion in game vs. Exclusion from game. Next, we need to specify the exact nature of our dependent variable, self-esteem. First, consider the following:

Question 1.11

Which type of measure is better for assessing self-esteem?

Self-report Measure

Behavioral Measure

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Choosing the Best Measure

We know we want to use a self-report measure to measure self-esteem. 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 ideal for young adults.

Reliability
Validity
Sensitivity

Question 1.12

Which of the following would be the best self-report measure of self-esteem in this study?

Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965): 10 items asking about your overall or global self-esteem on a scale of 1(strongly disagree) to 4 (strongly agree)

Social-Desirability Scale (Crowne & Marlowe, 1960): 33 items asking about your willingness to produce socially desirable responses.

State Self-Esteem Scale (SSES; Heatherton & Polivy, 1991): 20 items asking about how you feel about yourself at this moment on a scale of 1 (not at all) to 5 (extremely)

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Choosing the Best Measure

We can update the chart we made earlier to reflect how we will be measuring the dependent variable, which is shown in green below:

Summary of Our Within-Subjects Study
Pretest Measure	IV Level 1	Time 1 Measure	IV Level 2	Time 2 Measure
Self-esteem at Baseline	Inclusion in Game	Self-esteem after Inclusion	Exclusion from Game	Self-esteem after Exclusion
SSES at Baseline		SSES after Inclusion		SSES after Exclusion

Weighing Our Options

A problem with repeated-measures designs is the possibility of order effects.

Order Effect

The following are 4 types of order effects we could encounter in our study:

Practice Effect
Fatigue Effect
Carryover Effect
Sensitization Effect

Question 1.13

Which of the following is not likely to be an order effect in this study?

Practice Effect

Fatigue Effect

Carryover Effect

Sensitization Effect

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Weighing Our Options

Since we have the potential for multiple order effects in this study, we must consider how to minimize their impact. Fortunately, we do not think that the potential for practice and sensitization effects will drastically impact the results, so we decide to keep the same measure of self-esteem, SSES, constant throughout the study. However, we do think it would be worthwhile to reduce the impact of the carryover effect by using counterbalancing.

Counterbalancing

Question 1.14

Which of the following is the simplest way for us to use counterbalancing in our study?

Randomly assign half of the participants to 1 sequence (inclusion-exclusion) and half of the participants to the other sequence (exclusion-inclusion)

Randomly select the sequencing (inclusion-exclusion or exclusion-inclusion) for each participant

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Weighing Our Options

Let’s update the chart we made earlier to reflect the counterbalancing method we have chosen for this study. Notice how we now have a second sequence that allows us to measure self-esteem after being excluded from a game prior to exposure to the inclusion level, thus covering all possible sequences in our study.

Summary of Our Within-Subjects Study
Sequence	Pretest Measure	IV Level 1	Time 1 Measure	IV Level 2	Time 2 Measure
#1	Self-esteem at Baseline	Inclusion in Game	Self-esteem after Inclusion	Exclusion from Game	Self-esteem after Exclusion
	SSES at Baseline		SSES after Inclusion		SSES after Exclusion
#2	Self-esteem at Baseline	Inclusion in Game	Self-esteem after Exclusion	Exclusion from Game	Self-esteem after Inclusion
	SSES at Baseline		SSES after Exclusion		SSES after Inclusion

Determining Your Hypothesis

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Now that you have determined what you will manipulate and measure, you must formulate an experimental hypothesis.

Experimental Hypothesis

Question 1.15

Which of the following is the best experimental hypothesis given the nature of your study?

Young adults’ self-esteem after being included in a game will be higher than it was before playing the game. Their self-esteem after being excluded from a game will be lower than it was before playing the game.

There is no difference in young adults’ self-esteem before playing a game, after being included in a game, and after being excluded from a game.

Young adults’ self-esteem after being excluded from playing a game will be lower than it would be after being included in a game.

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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.

Question 1.16

Which of the following samples would be best for your experiment?

A national random sample of the general population

A random sample of college students from a research participant pool

A convenience sample of students in the classes you are currently taking

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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.

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 being included in or excluded from playing a virtual game of “cyberball” will result in a change to self-esteem. To study this topic, 30 participants will be randomly selected from the research participant pool at the University. Researchers will measure all participants’ self-esteem via the State Self-Esteem Scale (SSES) at the beginning of the study, after being included in a virtual game of “cyberball” for 5 minutes, and after being excluded from a virtual game of “cyberball” for 5 minutes. Counterbalancing will be used such that half of the participants will receive the inclusion-exclusion sequence and half will receive the exclusion-inclusion sequence. Participants will be debriefed at the end 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.

Question 1.17

Which of the following is the best response to the IRB’s concern?

You respond by stating that participants have already consented by being part of the research participation pool; thus, no more confirmation of informed consent is required.

You respond by telling the IRB that you will have all participants sign an informed consent form before the study.

You respond by stating that verbal assent will be obtained from all participants before the study.

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Running the Study

chapter_10_multiple_choice

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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? (Note: The State Self-Esteem Scale is referred to as SSES.)

Participants take the SSES for the pretest measure.

Obtain informed consent.

Participants play their first game of “cyberball,” which is programmed to include them in or exclude them from the game (depending on their sequence).

Participants take the SSES after the second game.

Debrief the participants.

Give participants instructions for how to play “cyberball” and progress through the 2 games.

Participants play their second game of “cyberball,” which is programmed to include them in or exclude them from the game (depending on their sequence).

Participants take the SSES after the first game.

Question

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. Because you have a within-subjects design, each participant will be exposed to all levels of the independent variable.

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 the first 5 participants in each sequence.

In the “Sequence” column, a 1 = Inclusion-Exclusion sequence, and a 2 = Exclusion-Inclusion sequence. The Baseline (Pretest/No game), Inclusion, and Exclusion columns represent a participant’s score measured via the SSES prior to the study, after inclusion in the game, and after exclusion from the game.

Participant Number	Sequence	Baseline	Inclusion	Exclusion
101	1	58	58	43
102	1	91	94	82
103	1	75	85	67
104	1	23	24	9
105	1	65	63	61
116	2	81	85	78
117	2	61	60	49
118	2	53	57	46
119	2	20	29	6
120	2	80	85	67

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

Question 1.18

Given the nature of your experiment, which of the following is the best statistical method?

Descriptive Statistics

Inferential Statistics

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Tutorial: Evaluating Output

chapter_10_table_activity_1

The following is an example of output for another 3-level design where participants experienced all 3 conditions in the study. This study was about how hours slept at night (6 hours, 8 hours, and 10 hours) influence self-reported happiness. Click on the table below to learn more about each element of the output.

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

F (#,#) = #.##, p = .##, eta² = .##.

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Hours	Sphericity Assumed	91.289	2	45.644	67.230	.000	.699
	Greenhouse-Geisser	91.289	1.694	53.903	67.230	.000	.699
	Huynh-Feldt	91.289	1.787	51.074	67.230	.000	.699
	Lower-bound	91.289	1.000	91.289	67.230	.000	.699
Error(Hours)	Sphericity Assumed	39.378	58	.679
	Greenhouse-Geisser	39.378	49.113	.802
	Huynh-Feldt	39.378	51.834	.760
	Lower-bound	39.378	29.000	1.358

Question

Tutorial: Evaluating Output

chapter_10_table_activity_2

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Click on the table below to learn more about each element of the output for this design.

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

F (#,#) = #.##, p = .##, eta² = .##.

Pairwise Comparisons
Measure: MEASURE_1
(I) Hours	(J) Hours	Mean Difference (I-J)	Std. Error	Sig.^b	95% Confidence Interval for Difference^b
(I) Hours	(J) Hours	Mean Difference (I-J)	Std. Error	Sig.^b	Lower Bound	Upper Bound
1	2	-2.200*	.222	.000	-2.762	-1.638
1	3	-.133	.164	.808	-.549	.283
2	1	2.200*	.222	.000	1.638	2.762
2	3	2.067*	.244	.000	1.448	2.685
3	1	.133	.164	.808	-.283	.549
3	2	-2.067*	.244	.000	-2.685	-1.448
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Sidak.

Question

Tutorial: Evaluating Output

chapter_10_table_activity_3

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Click on the table below to learn more about each element of the output for this design.

Descriptive Statistics
	Mean	Std. Deviation	N
Six	1.77	.728	30
Eight	3.97	.809	30
Ten	1.90	.712	30

Question

Your Turn: Evaluating Output

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Below is the output from your study:

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
GameCondition	Sphericity Assumed	2081.156	2	1040.578	101.918	.000	.778
	Greenhouse-Geisser	2081.156	1.816	1146.056	101.918	.000	.778
	Huynh-Feldt	2081.156	1.930	2081.156	101.918	.000	.778
	Lower-bound	2081.156	1.000	91.289	101.918	.000	.778
	Sphericity Assumed	592.178	58	10.210
	Greenhouse-Geisser	592.178	52.662	11.245
	Huynh-Feldt	592.178	55.983	10.578
	Lower-bound	592.178	29.000	20.420

Your Turn: Evaluating Output

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Below is the output from your study:

Pairwise Comparisons
Measure: MEASURE_1
(I) GameCondition	(J) GameCondition	Mean Difference (I-J)	Std. Error	Sig.^b	95% Confidence Interval for Difference^b
(I) GameCondition	(J) GameCondition	Mean Difference (I-J)	Std. Error	Sig.^b	Lower Bound	Upper Bound
1	2	-3.133*	.728	.001	-4.979	-1.288
1	3	8.267*	.788	.000	6.271	10.262
2	1	3.133*	.728	.001	1.288	4.979
2	3	11.400*	.944	.000	9.008	13.792
3	1	-8.267*	.788	.000	-10.262	-6.271
3	2	-11.400*	.944	.000	-13.792	-9.008
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Sidak.

Descriptive Statistics
	Mean	Std. Deviation	N
Baseline	61.10	25.694	30
Inclusion	64.23	24.694	30
Exclusion	52.83	26.478	30

Your Turn: Evaluating Output

chapter_10_multiple_choice_2

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Based on the results of your statistical analyses on Screens 32 and 33, match the correct number in the “Answer” column to the term requested under “Prompt”:

F for the ANOVA test

df for the main effect of condition (within-groups)

df for error (residual)

p for the ANOVA test

Mean difference between Baseline and Inclusion

p for the difference between Baseline and Inclusion

Mean difference between Baseline and Exclusion

p for the difference between Baseline and Exclusion

Mean difference between Inclusion and Exclusion

p for the difference between Inclusion and Exclusion

eta²

0.00

0.001

0.00

8.267

3.133

11.400

0.778

0.00

101.918

Question

Activity: Graphing Results

Based on the data provided, drag each Game Condition bar to the correct Mean SSES Score.

chapter_10_graph_activity

Descriptive Statistics
	Mean	Std. Deviation	N
Baseline	61.10	25.694	30
Inclusion	64.23	24.694	30
Exclusion	52.83	26.478	30

Game Condition & Self-Esteem

Mean SSES Score

Game Condition

Question

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.

Question 1.19

Based on the statistical analysis, which of the following results sections best fits the data and analyses from your study?

Contrary to the hypothesis, self-esteem was lower at baseline (M = 61.10, SD = 25.218)than after inclusion in a game (M = 64.23, SD = 24.694).

As hypothesized, self-esteem was lower after inclusion in a game (M = 61.10, SD = 25.218) than exclusion from a game (M = 64.23, SD = 24.694 ).

Contrary to the hypothesis, self-esteem was lower after exclusion from a game (M = 52.83, SD = 26.478) than at baseline (M = 61.10, SD = 25.218), which was, in turn, lower than after inclusion in a game (M = 64.23, SD = 24.694).

As hypothesized, self-esteem was lower after exclusion from a game (M = 52.83, SD = 26.478) than at baseline (M = 61.10, SD = 25.218), which was, in turn, lower than after inclusion in a game (M = 64.23, SD = 24.694).

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Correct.

Incorrect.

Take Home Message

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

Question 1.20

How would you explain what you found about self-esteem in relation to inclusion in and exclusion from a game to a friend or family member? Select the best option.

Those who are included in a game and then excluded experience greater changes in self-esteem than those who are excluded first.

We should make sure to always include people in games so we do not cause permanent damage to their self-esteem.

Being included in a game or being excluded from a game can cause a temporary increase or decrease in self-esteem, respectively.

Being included in games makes no difference to our self-esteem.

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Correct.

Incorrect.

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Congratulations! You have successfully completed this activity.

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