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?

Something to Think About…

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

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

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Picking the Best Design

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

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Picking the Best Design

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

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Picking the Best Design

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

Question 1.4

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Picking the Best Design

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

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Picking the Best Design

Question 1.6

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3
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Picking the Best Design

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

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2
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Picking the Best Design

Question 1.8

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3
Try again.
Correct.
Incorrect.

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

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3
Try again.
Correct.
Incorrect.

Operationally Defining the Independent Variable

Question 1.10

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3
Try again.
Correct.
Incorrect.

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

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2
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Correct.
Incorrect.

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

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3
Try again.
Correct.
Incorrect.

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

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4
Try again.
Correct.
Incorrect.

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

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2
Try again.
Correct.
Incorrect.

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

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

  • Experimental Hypothesis

Question 1.15

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
3
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Correct.
Incorrect.

Finding a Sample

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

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
3
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Correct.
Incorrect.

Submitting to the IRB

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 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

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

ojhex8CcToPXFnn8hLMRd7DWAZEfhsfN1FEJTZLGl5Unz1cSNoAerJYwG0IFWTxUNW4wQENwrkQjR7LI0YEuyZamqm2nQGHkoCE1uL2MZ4P9ardPLqECfaAwdh02tSuFoDrd1UoLkYWwuA9MIBtJPVM++bnTnEjDBlxMSb0lEH9QsNrCkuRtWSYxKJkbeK6pl9kKqkIHFfiYQed5ry41d/YdE7YRduS6zCRAqVtI11bheCS/dWrgWFnGTLUMAzkyjAAuvZXivG2n7LY3YcmHpT1yXcpn6OywYGFxfkNvhnYQCeiWAi5tgrjOfxGs1TWwTT4GGb6Vm6hXIDtkBuzzQUANxtT9CVvnKHe5fQ13v/j3uuLDwOEYu28ss/sS1lZOvfNKg55XfQwXihI2Kw0LqRCrbvU1qQRgC06Oy5NAiwaReIDyt4Z6Q7RZV6cBVAnNyVODFKn/Aark4rMyqykMN7n2cWN8t2Xshp4EDQY9pAZOTmWN5lu8ztuE+nEuBeUKSXhvWWwGOI2aG3vZ54L5RI4akCytB1cj0fNZaU6y3jo66vSRyyK09vF/LbCAzcaXOq9mSKfV2FKts2XB8yY33dy1wmGCE0qrQjL1IQJbmClOVT09mfiezBKsbF67vaD27GGY7t2oLqXNbs8JuPygv8XGRCJchhzGL14j93BvwCSkKYCJB2ENwy5OOPv/PAAntwKgygnE+DSDwhysZ7cSWV/mzffW0+/63KERS5ZqFOsmEp65C37owvm+WR8cLoEmjzBtPZglHTPreANns+2YXFm4xH7YgsjgK2QOEf2vVa2xlFc6pVScceiXH01yNRJVqf5rdeZTrvzkHnWY6nTXcICu3AbKUiPK6N2IFfWYXXF75u+NByScZ4dhFg+CKob7d+334KBc8F/sUTNxChbF3fXRifgc0pybUBVeIBuBwt9MVCQfBB67SnVOsySYHSmVmrBvYRgnLXfbtRye1rxgckoETsjTIEvPXYNFoesEK91rXChKXc5bSUHr+aMLdN5EZ+nF35+DS3XutSoxoXaRXPBuU651idtvw9s0jYgvFfJhh/FgQKoPg3qC1e7pMS52U3djLoSd87PpmKyqMd/PQ3LVziQ93nDG49oCrRgMUkZnLVhTKnB8MOPx57zC1u9QHo8bZbZ1zmPq4tEqobXNnWTlCE0zXMDL
3
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Correct.
Incorrect.

Running the Study

chapter_10_multiple_choice

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.)

A.
B.
C.
D.
E.
F.
G.
H.
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.

Collecting Data

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

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

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
2
Try again.
Correct.
Incorrect.

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.

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

This is the df or degrees of freedom. An ANOVA has 2 dfs, one for the main effect (within-groups) and one for the error (residual).

This is the F statistic. It represents the size of the difference between condition means compared to the size of the residual error.

This is the p level or the significance level. It represents the probability or likelihood that the results happened by chance. The lower the p level, the less likely the result happened by chance. This would be reported as p < .001 in the results.

The F score and p level will only tell you whether there is a significant difference. To determine which means are different, and the nature or direction of those differences, you need to look at the means via a post-hoc test.

The eta squared (eta2 ) is the effect size. It tells us the proportion of change in the dependent variable that is associated with being in the different groups of the independent variable.

Tutorial: Evaluating Output

chapter_10_table_activity_2

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 Differenceb
Lower Bound Upper Bound
1 2 -2.200* .222 .000 -2.762 -1.638
3 -.133 .164 .808 -.549 .283
2 1 2.200* .222 .000 1.638 2.762
3 2.067* .244 .000 1.448 2.685
3 1 .133 .164 .808 -.283 .549
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.

The results presented here are from the post-hoc test, which compares each of the groups’ means to all of the other groups’ means.

This is the difference between the mean happiness rating for the 6 hours of sleep and 8 hours of sleep conditions.

This is the difference between the mean happiness rating for the 6 hours of sleep and 10 hours of sleep conditions.

This is the difference between the mean happiness rating for the 8 hours of sleep and 10 hours of sleep conditions.

Happiness was different in the 8 hours sleep condition from the 6 hours and 10 hours sleep conditions, which had similar ratings.

The post-hoc test tells us which comparisons between the means were significant. The p level tells us the significance level of that comparison.

Tutorial: Evaluating Output

chapter_10_table_activity_3

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

This is the average or mean (M) happiness rating after 6 hours of sleep.

This is the standard deviation (SD) of happiness rating after 6 hours of sleep.

This is the average or mean (M) happiness rating after 8 hours of sleep.

This is the standard deviation (SD) of happiness rating after 8 hours of sleep.

This is the average or mean (M) happiness rating after 10 hours of sleep.

This is the standard deviation (SD) of happiness rating after 10 hours of sleep.

In this case, the means tell us that happiness ratings were highest after 8 hours of sleep. The results from the post-hoc test support the finding that happiness ratings were similar for 6 and 10 hours of sleep, but increased significantly with 8 hours of sleep.

Your Turn: Evaluating Output

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

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 Differenceb
Lower Bound Upper Bound
1 2 -3.133* .728 .001 -4.979 -1.288
3 8.267* .788 .000 6.271 10.262
2 1 3.133* .728 .001 1.288 4.979
3 11.400* .944 .000 9.008 13.792
3 1 -8.267* .788 .000 -10.262 -6.271
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

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
101.918
2
58
0.00
3.133
0.001
8.267
0.00
11.400
0.00
0.778

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

Your Turn: Results

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

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4
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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

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4
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Correct.
Incorrect.
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