Facebook Conformity and Consensus

Facebook Conformity and consensus

Donthel Daniels

Institution Affiliation

Facebook Conformity and Consensus

We live in a world where social media has overtaken everything. We are no longer in the period where only the millennials are obsessed with the use of social media and a platform like Facebook attracts people of all ages. The use of Facebook has increased to billions of people and the amount of information people have opted to share out there in their lives is quite alarming. Just last year, Mark Zuckerberg before the US Congress as he needed to explain how Facebook aided Cambridge Analytica in obtaining people’s information. Although many agree that social media has been of huge impact, many people are also of the consensus of the numerous negative impacts that Facebook has had on top of the list being as it led to people getting severe polarized in key issues including the election.

According to a research article by Serena Coppolino, on Computer in Human Behavior, they clearly portray the polarization due to use of social network platforms (Perfumi, et.al, 2019). There is a great difference e that has been observed over the years when it comes to computer-mediated-communication as compared to face-to –face communication. AI which includes Facebook has resulted in people losing touch with fellow humans because all they are used to is chatting online. We currently live in a society where almost everybody wants to brand themselves as being anti-social, but this may not be the case rather it is because we have spent too much updating everything on our Facebook pages where strangers give us comments that may be true or not true.

Conformity in social media platforms results from people affiliation to what they tend to like. An individual is likely to follow pages they feel they can relate to and at times these pages become misleading from the content they share out there. It is for a fact that Facebook has been a useful tool when it comes to spreading news but to this good side, there has been a bad side. In order to get the engagement including the likes and the following some sites rely on spreading of Fake news (Kundu, 2013). News does not have to be reliable as it is a competition between the various news outlets on who will be the first to get the information out there. At times we find ourselves liking, commenting and even sharing content that we may not conform to (Winter, 2015).

According to an article by Winter (2015), it talks about how Facebook has been an important source of social marketing as people can easily get information of the products out there, however when comparing likes to comments, most people rely on comments while trying to regard if a product or content is reliable (Winter, 2015). Comment section will always indicate what people feel, and even when a product or a content out there is great, you may still find hateful as well as spiteful comments. Normative influence is likely to result in someone getting a huge following and with these followers the ones content may be liked, shared and commented on by people proving the conformity as well as the consensus that exists in Facebook.

From social media pages, we have created a perception about ourselves out there. When you look at people’s profiles, they are all different but mostly from these profiles people can make judgements on what they think about us. In a research conducted by Rom and Conway on Strategic Moral Self, the talk of the perception we tend to present to people which are times are false and misleading (Rom, 2018). On Facebook among other social media pages, you are likely to encounter individuals living their best lives, driving dream cars, going to the best holiday destination but behind the cameras they may be going through their worst depression. Kelly and Ngo interaction seems to also agree with the fact that online vs reality may actually not be the same for most of us (Kelly, 2017). On social media platforms, people are likely to put up content that is liked and relatable by many. The aspect of consensus results from concepts people have and what they reflect on their profile. The owners may actually possess certain features that are actually not close to their real personalities.

In the study conducted we hypothesized that the participants who read the support response were most likely to support her in that she should keep it to herself and take the win, for those who read the opposing comments were most likely to find her guilty and condemn her actions.

Study One

To shed light on Facebook conformity and how various comments on any issue may affect people perception, we conducted a study where we evaluated people reaction after reading comments made on a post. The scenario was a post by Abigail who was mistakenly given the answer sheet by the teacher as the question papers were being given and how she used since she always had difficult in the class and she ended up passing the test resulting to the teacher changing the curve method idea for the rest of the students. There were three conditions under the scenario; support, oppose and mixed. The support condition were those comments of people who found that Abigail’s behavior was appropriate and that the fault lied with the teacher, oppose condition were people against her actions who claimed she would have report. Lastly, the mixed condition was people who were both for and against Abigail’s action.

Methods

Participants

           For the study, 140 participants among them Florida University students were randomly selected. Of the 140 participants, 51.4% (n=72) were male and 48.6% (n=68) were female. Ages of the participants ranged from 18 years to 29 years with an average of 22.16 years (SD=3). The sample population consisted of 45.7% Hispanic (n=64), 25.7% Caucasians (n=36), 20.7%African-Americans (n=29), 5.7%Asian –American (n=8) and 2.1%Native Indians (n=3). (See Appendix 1.)

Materials and Procedures

The participants were informed of the study itself, the risks and benefits that it carried for the students, an overview of the information required and the time required as stipulated in the standardized guidelines for informed consent. The participants had to give their consent verbally after which they were given one of the three research study documents that contained both the primary independent and dependent variables for the study. The document also consisted of five parts, the first part had the Abigail Foster Facebook post, unfortunately the professor while handling the question paper to Abigail he also gave her the answer key and Abigail who was certain she was going to work in spite of working hard used the answers. After she did very well the professor who was to curve the results because most students had not done well changed his mind, the dilemma was with Abigail who was not sure what she should do hence, in search for advice she posted on Facebook. Among the three documents in spite of them having the same scenario the condition under each of them is different, the conditions are; support, oppose and mixed which are various reactions to Abigail’s post.

For the support condition, it outlined eight comments by people who believed that Abigail had done nothing wrong and should accept the grade while putting the blame on the professor who could have been more careful. The second condition oppose, also has eight comments from students who believed that Abigail should admit to her wrongdoing by putting herself in the other students’ shoes. Lastly, the mixed condition has no consensus since among the eight responses some people opposed her actions while others gave her support. 

           After having read the scenario the participants were then required to proceed with part two of the questionnaire where they were required to rate Abigail’s behavior based on some statements by either agreeing or disagreeing. They were to use an interval scale of 1(strongly disagree) to 6(strongly agree) (See Appendix 2).

In the third part the participant was to rate various statements on what advice they would offer Abigail, their response if they were in the same situation and their general Abigail’s impression, there were three statements. For the other two parts of the sections on the response and impressions the participants have in regards to Abigail’s behavior and their own, the interval scale of 1 (strongly disagree) or 6 (strongly agree) was used. In response to what they would do the participant was given two statements on whether they would confess or keep silent and on Abigail’s impressions it was either she seems warm, moral, sincere, competent, confident, competitive or good-natured. Additionally, the fourth part of the questions contained demographic questions that the participant had the choice of not answering if he or she deemed them private. The questions included gender, age, race, relationship status, whether they were FIU students and whether English was their first language. Lastly, in the fifth part the participant was supposed to give their feedback on whether the advice Abigail was given was either in support of her behavior, opposing it or was more of the both of them, they filled their response by marking an X.

           While the study has both dependent and independent variables, our focus was on the dependent variables which are warm-cold scale, either accepting or rejecting what she did and the self-ratings. The main variable was accepting or rejecting that was reliant on the responses the people wrote in relation to her post and the influence they would have on her. We hypothesized that the participants who read the support response were most likely to support her in that she should keep it to herself and take the win, for those who read the opposing comments were most likely to find her guilty and condemn her actions.

Results

           Under the survey conditions (support vs. oppose vs. mixed) which were our independent variables and while focusing on the participant’s assessment on Abigail’s behavior based on the responses in their scenarios, we arrived as various results. Those who had the suppose response were 46 which was 32.9%, oppose 45 which was 32.1% and mixed were 49 which was 35%. Just like predicted the participant who had the support responses described Abigail competent, moral, confident, warm and good-natured person. They believed that she deserved her grade hence she did not have to report it to the professor, they also termed her response after she was given the answer key to be appropriate and reasonable hence if there was a person to blame it would have to be the professor. However, those who read the opposing notions on the scenarios termed what Abigail had done to be wrong and that she should have reported, they went ahead to describe her as immoral and unacceptable and unethical of what she had done. They also referred her behavior in a not good way disagreeing with all notions in part three of the question. Those who had missed reactions assessment on Abigail tended to balance with a few agreeing while the others disagreed.

           In addition, for the ANOVA test which was our main analysis there was a significant difference in whether our dependent variable on accepting or rejecting Abigail’s behavior had been wrong in relation to the support, oppose and mixed independent valuables, F (2,137) = 4.537, p= .021. The test confirmed that the participant’s assessment would be affected by the response scenario they read. Lastly, the Post Hoc Tests analysis is used to show a significant difference among various groups. From the table, it is evident that there is a statistically significant difference between the reactions by those people who had both suppose and oppose response and those with suppose and mixed response which is (p=0.021) and (p=0.035) respectively while there was no difference with those with oppose and mixed response(p=0.969). Also, this test supported the hypothesis that participants were likely to support Abigail in the support condition (M = 4.2, SD = 0.773), the oppose condition was (M = 3.4 SD = 0.986, and the mixed condition (M = 3.816 SD = 0.727). (See Appendix 3)

Discussion

           As earlier predicted that depending on the response the participants read they would influence their view and eventually on how they would make inferences on Abigail have been proved. Participants who read the positive response were in support of Abigail’s actions and termed her behavior as appropriate, these same people too while asked of what they would have done they supported that they would keep quiet. Similarly, those participants who read the opposing responses arrived at the conclusion that Abigail did the wrong thing and that for the sake of her integrity she should have reported the matter. Lastly, there was no major difference in those who got both responses since just as predicted some opposed while others supported Abigail’s decision. However, it is clear that the difference in all those scenarios assessment by the participants was not major this could have been the fact that the participants all have an experience of how some classes can be tough and in the event, they find themselves in such a situation they are bound to make the same decision. Frankly, if the same situations were to happen to those people who opposed Abigail’s decision they are likely to just act the same way because until one faced a similar issue can their real intentions be revealed.

Study Two

With the advancement of technology that has been happening in the last few decades, the interaction among people has shifted from the face-face to digital. There have been multiple media channels that have facilitated this shift with the social media platforms taking the lead, the various mediums are currently, being used even for marketing and advertising business. Facebook is one of this channel that came into being in 2004 and despite other social media platforms emerging the number of active users keep increasing with the statistics of December 2019 is 2.50 billion people on a monthly basis (Noyes, 2020).

           Most users of Facebook users often are often observing the content on the site rather than posting, this was depicted when a study was conducted among students, also it was concluded that most people interact on Facebook with people they already have interaction with offline. Also, Facebook users are more likely to post on the public display with people they are regularly involved with, with parents this never occurred and rarely for strangers (Pempek, Yermolayeva & Calvert, 2009). According to Wang, Kraut & Burke, (2013), Facebook posts and comments vary among different genders where men tend to post more on money and work while females talk of relationships and people and in spite of the changing times also the languages employed by the two genders differ. Also, with the widespread use of emoticons, it has been found that women use those with hugs and smile and their posts and comments in most cases are usually shorten than men whose posts center more on external factors. In addition, the authors recognize that while it is evident that men get few comments across the media platform the comments are usually higher when the posts are on masculine issues and female comments are more when they defy the perception of the society. 

           According to the Engaging the Social News User article, various scholars believe that the lack anonymity of with the comments on Facebook has ensured that the comments are of higher quality and are more and the users rarely post offensive and abusive comments. The lack of this anonymity has been attributed to the reasons both Los Aneles Times and the digitalspy.co.uk shifted to using the Facebook commenting system (Hille & Bakker, 2014). The comments across social media platforms have been said to be the most common form of public online participation. There are also various factors that determine the quality and number of comments on each post, the comments displayed on each page and the criteria behind it and the message characteristics (Ziegele, Breiner & Quiring, 2014). 

           According to Winter, Krämer & Brückner, (2015), a study was conducted and it concluded that the negative comments in a post reduced the article persuasiveness, however, the positive comments hardly had any effect. Also, when the posts were termed as the readers as relevant they were more comments with various opinions and higher elaboration levels. The authors of the article have also affirmed that the comments influence the public opinions since the lack of anonymity limit identification since one can associate the cultural background and age and that the comments are usually connected with the profile which is usually visible to the public (Winter, Krämer & Brückner, 2015).

           While in study one we had support, oppose and mixed condition for the scenario in this study two we will only have support and oppose condition. The second independent variable is the effects of cheating and how they will influence the participant’s reaction to the post. The hypothesize of this study is that those participants who believe the effects of cheating are severe will oppose Abigail’s action while those who see the effects of cheating as just a bump on the road will support her actions.

Appendix 1

Gender (1 = M, 2 = F)

Frequency Percent Valid Percent Cumulative Percent

Valid Male 72 51.4 51.4 51.4

Female 68 48.6 48.6 100.0

Total 140 100.0 100.0 Race

Frequency Percent Valid Percent Cumulative Percent

Valid Caucasian 36 25.7 25.7 25.7

Hispanic 64 45.7 45.7 71.4

Native Indian 3 2.1 2.1 73.6

African American 29 20.7 20.7 94.3

Asian American 8 5.7 5.7 100.0

Total 140 100.0 100.0 Age

Frequency Percent Valid Percent Cumulative Percent

Valid 17.00 2 1.4 1.4 1.4

18.00 22 15.7 15.7 17.1

19.00 7 5.0 5.0 22.1

20.00 6 4.3 4.3 26.4

21.00 28 20.0 20.0 46.4

22.00 13 9.3 9.3 55.7

23.00 21 15.0 15.0 70.7

24.00 3 2.1 2.1 72.9

25.00 21 15.0 15.0 87.9

26.00 2 1.4 1.4 89.3

27.00 10 7.1 7.1 96.4

28.00 1 .7 .7 97.1

29.00 4 2.9 2.9 100.0

Total 140 100.0 100.0 Frequency Table

Condition (1 = Support, 2 = Oppose, 3 = Mixed)

Frequency Percent Valid Percent Cumulative Percent

Valid Support 46 32.9 32.9 32.9

Oppose 45 32.1 32.1 65.0

Mixed 49 35.0 35.0 100.0

Total 140 100.0 100.0 Appendix 2

Crosstab and Chi Square

Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was wrong

Crosstab

Count

Part II: Abigail’s behavior was wrong Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 28 14 1 46

Oppose 7 6 23 9 45

Mixed 4 16 19 10 49

Total 14 50 56 20 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 26.198a 6 .000

Likelihood Ratio 29.031 6 .000

Linear-by-Linear Association 5.884 1 .015

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was understandable

Crosstab

Count

Part II: Abigail’s behavior was understandable Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 1 6 19 20 46

Oppose 9 16 13 7 45

Mixed 1 15 25 8 49

Total 11 37 57 35 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 29.497a 6 .000

Likelihood Ratio 28.563 6 .000

Linear-by-Linear Association 5.492 1 .019

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was reasonable

Crosstab

Count

Part II: Abigail’s behavior was reasonable Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 8 20 15 46

Oppose 10 14 14 7 45

Mixed 1 15 25 8 49

Total 14 37 59 30 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 18.740a 6 .005

Likelihood Ratio 18.576 6 .005

Linear-by-Linear Association 1.097 1 .295

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was unethical

Crosstab

Count

Part II: Abigail’s behavior was unethical Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 22 15 4 46

Oppose 3 10 21 11 45

Mixed 2 17 22 8 49

Total 10 49 58 23 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 10.553a 6 .103

Likelihood Ratio 10.811 6 .094

Linear-by-Linear Association 3.862 1 .049

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was immoral

Crosstab

Count

Part II: Abigail’s behavior was immoral Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 20 19 4 46

Oppose 3 10 23 9 45

Mixed 2 12 27 8 49

Total 8 42 69 21 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 7.529a 6 .275

Likelihood Ratio 7.498 6 .277

Linear-by-Linear Association 3.760 1 .053

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was appropriate

Crosstab

Count

Part II: Abigail’s behavior was appropriate Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 4 17 14 11 46

Oppose 8 17 13 7 45

Mixed 9 21 15 4 49

Total 21 55 42 22 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.843a 6 .441

Likelihood Ratio 6.131 6 .409

Linear-by-Linear Association 4.557 1 .033

N of Valid Cases 140 Crosstab

Count

Part II: Abigail’s behavior was unacceptable Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 8 23 13 2 46

Oppose 7 15 16 7 45

Mixed 6 19 19 5 49

Total 21 57 48 14 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.711a 6 .456

Likelihood Ratio 5.908 6 .434

Linear-by-Linear Association 2.328 1 .127

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: I would advise Abigail to keep quiet

Crosstab

Count

Part III: I would advise Abigail to keep quiet Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 6 19 16 46

Oppose 3 22 15 5 45

Mixed 2 17 24 6 49

Total 10 45 58 27 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 20.731a 6 .002

Likelihood Ratio 21.079 6 .002

Linear-by-Linear Association 2.898 1 .089

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: I would try to comfort Abigail

Crosstab

Count

Part III: I would try to comfort Abigail Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 7 23 16 46

Oppose 2 13 20 10 45

Mixed 0 8 26 15 49

Total 2 28 69 41 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.488a 6 .204

Likelihood Ratio 8.705 6 .191

Linear-by-Linear Association .092 1 .761

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: I would give Abigail the same advice that her friends gave her

Crosstab

Count

Part III: I would give Abigail the same advice that her friends gave her

2.00 3.00 4.00 5.00

Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 2 4 24 16

Oppose 3 5 20 17

Mixed 1 10 27 11

Total 6 19 71 44

Crosstab

Count

Total

Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 46

Oppose 45

Mixed 49

Total 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 6.419a 6 .378

Likelihood Ratio 6.485 6 .371

Linear-by-Linear Association 1.469 1 .225

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: If I received the answers, I would keep silent

Crosstab

Count

Part III: If I received the answers, I would keep silent Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 11 4 14 17 46

Oppose 18 9 14 4 45

Mixed 14 3 17 15 49

Total 43 16 45 36 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 14.258a 6 .027

Likelihood Ratio 15.338 6 .018

Linear-by-Linear Association .237 1 .627

N of Valid Cases 140 Crosstab

Count

Part III: If I received the answers, I would confess Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 21 10 8 7 46

Oppose 11 10 6 18 45

Mixed 22 9 5 13 49

Total 54 29 19 38 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 9.701a 6 .138

Likelihood Ratio 10.103 6 .120

Linear-by-Linear Association .349 1 .555

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems warm

Crosstab

Count

Part III: Abigail seems warm Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 5 21 17 46

Oppose 9 7 10 19 45

Mixed 6 3 20 20 49

Total 18 15 51 56 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 9.182a 6 .164

Likelihood Ratio 9.616 6 .142

Linear-by-Linear Association .013 1 .911

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems good-natured

Crosstab

Count

Part III: Abigail seems good-natured Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 6 24 16 46

Oppose 6 10 12 17 45

Mixed 1 6 23 19 49

Total 7 22 59 52 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 15.237a 6 .018

Likelihood Ratio 16.012 6 .014

Linear-by-Linear Association .006 1 .937

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems confident

Crosstab

Count

Part III: Abigail seems confident Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 22 9 9 6 46

Oppose 28 10 4 3 45

Mixed 32 9 4 4 49

Total 82 28 17 13 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.786a 6 .448

Likelihood Ratio 5.586 6 .471

Linear-by-Linear Association 3.447 1 .063

N of Valid Cases 140 Crosstab

Count

Part III: Abigail seems competitive Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 4 19 18 46

Oppose 12 4 13 16 45

Mixed 4 3 22 20 49

Total 21 11 54 54 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.288a 6 .218

Likelihood Ratio 7.966 6 .241

Linear-by-Linear Association .244 1 .622

N of Valid Cases 140 Crosstab

Count

Part III: Abigail seems sincere Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 0 15 31 46

Oppose 1 2 11 31 45

Mixed 7 5 13 24 49

Total 8 7 39 86 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 17.373a 6 .008

Likelihood Ratio 20.162 6 .003

Linear-by-Linear Association 11.332 1 .001

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems moral

Crosstab

Count

Part III: Abigail seems moral Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 10 7 14 15 46

Oppose 25 12 8 0 45

Mixed 24 8 14 3 49

Total 59 27 36 18 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 32.026a 6 .000

Likelihood Ratio 34.821 6 .000

Linear-by-Linear Association 12.940 1 .000

N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems competent

Crosstab

Count

Part III: Abigail seems competent Total

2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 24 9 7 6 46

Oppose 29 8 6 2 45

Mixed 32 10 4 3 49

Total 85 27 17 11 140

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.392a 6 .624

Likelihood Ratio 4.347 6 .630

Linear-by-Linear Association 2.869 1 .090

N of Valid Cases 140 Appendix 3

Descriptive

Part II: Abigail’s behavior was wrong

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean

Lower Bound Upper Bound

Support 46 3.2826 .62050 .09149 3.0983 3.4669

Oppose 45 3.7556 .95716 .14269 3.4680 4.0431

Mixed 49 3.7143 .88976 .12711 3.4587 3.9699

Total 140 3.5857 .85651 .07239 3.4426 3.7288

Descriptive

Part II: Abigail’s behavior was wrong

Minimum Maximum

Support 2.00 5.00

Oppose 2.00 5.00

Mixed 2.00 5.00

Total 2.00 5.00

ANOVA

Part II: Abigail’s behavior was wrong

Sum of Squares df Mean Square F Sig.

Between Groups 6.334 2 3.167 4.537 .012

Within Groups 95.637 137 .698 Total 101.971 139 Post Hoc Tests

Multiple Comparisons

Dependent Variable: Part II: Abigail’s behavior was wrong

Tukey HSD

(I) Condition (1 = Support, 2 = Oppose, 3 = Mixed) (J) Condition (1 = S