Facebook Conformity and consensus<\/p>\n
Donthel Daniels<\/p>\n
Institution Affiliation<\/p>\n
Facebook Conformity and Consensus<\/p>\n
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.<\/p>\n
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 \u2013face 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. <\/p>\n
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).<\/p>\n
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. <\/p>\n
From social media pages, we have created a perception about ourselves out there. When you look at people\u2019s 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. <\/p>\n
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.<\/p>\n
Study One<\/p>\n
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\u2019s 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\u2019s action.<\/p>\n
Methods<\/p>\n
Participants<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0For 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 \u2013American (n=8) and 2.1%Native Indians (n=3). (See Appendix 1.)<\/p>\n
Materials and Procedures<\/p>\n
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\u2019s post. <\/p>\n
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\u2019 shoes. Lastly, the mixed condition has no consensus since among the eight responses some people opposed her actions while others gave her support.\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0After having read the scenario the participants were then required to proceed with part two of the questionnaire where they were required to rate Abigail\u2019s 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). The statements that participants were to evaluate on Abigail\u2019s behavior included, Abigail\u2019s behavior was wrong, Abigail\u2019s behavior was understandable, Abigail\u2019s behavior was reasonable, Abigail\u2019s behavior was unethical, Abigail\u2019s behavior was immoral, Abigail\u2019s behavior was appropriate, and Abigail\u2019s behavior was unacceptable. (See Appendix 2)\u00a0<\/p>\n
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\u2019s impression, there were three statements, I would advise Abigail to keep silent, I would try to comfort Abigail and I would give Abigail the same advice that her friends gave her. For the other two parts of the sections on the response and impressions the participants have in regards to Abigail\u2019s 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\u2019s 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.<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0While 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.<\/p>\n
Results<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Under the survey conditions (support vs. oppose vs. mixed) which were our independent variables and while focusing on the participant\u2019s assessment on Abigail\u2019s 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.<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0In 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\u2019s behavior had been wrong in relation to the support, oppose and mixed independent valuables, F (2,137) = 4.537, p <.05. The test confirmed that the participant\u2019s 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)<\/p>\n
Discussion<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0As 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\u2019s 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\u2019s 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\u2019s decision they are likely to just act the same way because until one faced a similar issue can their real intentions be revealed.<\/p>\n
Study Two<\/p>\n
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).<\/p>\n
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).\u00a0According 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.<\/p>\n
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\u00a0(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).<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0According to Winter, Kr\u00e4mer & Br\u00fcckner, (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, Kr\u00e4mer & Br\u00fcckner, 2015).<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0While 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\u2019s reaction to the post. The hypothesize of this study is that those participants who believe the effects of cheating are severe will oppose Abigail\u2019s action while those who see the effects of cheating as just a bump on the road will support her actions.Appendix 1<\/p>\n
Gender (1 = M, 2 = F)<\/p>\n
Frequency Percent Valid Percent Cumulative Percent<\/p>\n
Valid Male 72 51.4 51.4 51.4<\/p>\n
Female 68 48.6 48.6 100.0<\/p>\n
Total 140 100.0 100.0 Race<\/p>\n
Frequency Percent Valid Percent Cumulative Percent<\/p>\n
Valid Caucasian 36 25.7 25.7 25.7<\/p>\n
Hispanic 64 45.7 45.7 71.4<\/p>\n
Native Indian 3 2.1 2.1 73.6<\/p>\n
African American 29 20.7 20.7 94.3<\/p>\n
Asian American 8 5.7 5.7 100.0<\/p>\n
Total 140 100.0 100.0 Age<\/p>\n
Frequency Percent Valid Percent Cumulative Percent<\/p>\n
Valid 17.00 2 1.4 1.4 1.4<\/p>\n
18.00 22 15.7 15.7 17.1<\/p>\n
19.00 7 5.0 5.0 22.1<\/p>\n
20.00 6 4.3 4.3 26.4<\/p>\n
21.00 28 20.0 20.0 46.4<\/p>\n
22.00 13 9.3 9.3 55.7<\/p>\n
23.00 21 15.0 15.0 70.7<\/p>\n
24.00 3 2.1 2.1 72.9<\/p>\n
25.00 21 15.0 15.0 87.9<\/p>\n
26.00 2 1.4 1.4 89.3<\/p>\n
27.00 10 7.1 7.1 96.4<\/p>\n
28.00 1 .7 .7 97.1<\/p>\n
29.00 4 2.9 2.9 100.0<\/p>\n
Total 140 100.0 100.0 Frequency Table<\/p>\n
Condition (1 = Support, 2 = Oppose, 3 = Mixed)<\/p>\n
Frequency Percent Valid Percent Cumulative Percent<\/p>\n
Valid Support 46 32.9 32.9 32.9<\/p>\n
Oppose 45 32.1 32.1 65.0<\/p>\n
Mixed 49 35.0 35.0 100.0<\/p>\n
Total 140 100.0 100.0 Appendix 2<\/p>\n
Crosstab and Chi Square<\/p>\n
Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was wrong<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was wrong Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 28 14 1 46<\/p>\n
Oppose 7 6 23 9 45<\/p>\n
Mixed 4 16 19 10 49<\/p>\n
Total 14 50 56 20 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 26.198a 6 .000<\/p>\n
Likelihood Ratio 29.031 6 .000<\/p>\n
Linear-by-Linear Association 5.884 1 .015<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was understandable<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was understandable Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 1 6 19 20 46<\/p>\n
Oppose 9 16 13 7 45<\/p>\n
Mixed 1 15 25 8 49<\/p>\n
Total 11 37 57 35 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 29.497a 6 .000<\/p>\n
Likelihood Ratio 28.563 6 .000<\/p>\n
Linear-by-Linear Association 5.492 1 .019<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was reasonable <\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was reasonable Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 8 20 15 46<\/p>\n
Oppose 10 14 14 7 45<\/p>\n
Mixed 1 15 25 8 49<\/p>\n
Total 14 37 59 30 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 18.740a 6 .005<\/p>\n
Likelihood Ratio 18.576 6 .005<\/p>\n
Linear-by-Linear Association 1.097 1 .295<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was unethical<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was unethical Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 22 15 4 46<\/p>\n
Oppose 3 10 21 11 45<\/p>\n
Mixed 2 17 22 8 49<\/p>\n
Total 10 49 58 23 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 10.553a 6 .103<\/p>\n
Likelihood Ratio 10.811 6 .094<\/p>\n
Linear-by-Linear Association 3.862 1 .049<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was immoral<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was immoral Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 20 19 4 46<\/p>\n
Oppose 3 10 23 9 45<\/p>\n
Mixed 2 12 27 8 49<\/p>\n
Total 8 42 69 21 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 7.529a 6 .275<\/p>\n
Likelihood Ratio 7.498 6 .277<\/p>\n
Linear-by-Linear Association 3.760 1 .053<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part II: Abigail’s behavior was appropriate<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was appropriate Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 4 17 14 11 46<\/p>\n
Oppose 8 17 13 7 45<\/p>\n
Mixed 9 21 15 4 49<\/p>\n
Total 21 55 42 22 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 5.843a 6 .441<\/p>\n
Likelihood Ratio 6.131 6 .409<\/p>\n
Linear-by-Linear Association 4.557 1 .033<\/p>\n
N of Valid Cases 140 Crosstab<\/p>\n
Count <\/p>\n
Part II: Abigail’s behavior was unacceptable Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 8 23 13 2 46<\/p>\n
Oppose 7 15 16 7 45<\/p>\n
Mixed 6 19 19 5 49<\/p>\n
Total 21 57 48 14 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 5.711a 6 .456<\/p>\n
Likelihood Ratio 5.908 6 .434<\/p>\n
Linear-by-Linear Association 2.328 1 .127<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: I would advise Abigail to keep quiet<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: I would advise Abigail to keep quiet Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 6 19 16 46<\/p>\n
Oppose 3 22 15 5 45<\/p>\n
Mixed 2 17 24 6 49<\/p>\n
Total 10 45 58 27 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 20.731a 6 .002<\/p>\n
Likelihood Ratio 21.079 6 .002<\/p>\n
Linear-by-Linear Association 2.898 1 .089<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: I would try to comfort Abigail<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: I would try to comfort Abigail Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 7 23 16 46<\/p>\n
Oppose 2 13 20 10 45<\/p>\n
Mixed 0 8 26 15 49<\/p>\n
Total 2 28 69 41 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 8.488a 6 .204<\/p>\n
Likelihood Ratio 8.705 6 .191<\/p>\n
Linear-by-Linear Association .092 1 .761<\/p>\n
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<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: I would give Abigail the same advice that her friends gave her<\/p>\n
2.00 3.00 4.00 5.00<\/p>\n
Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 2 4 24 16<\/p>\n
Oppose 3 5 20 17<\/p>\n
Mixed 1 10 27 11<\/p>\n
Total 6 19 71 44<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Total<\/p>\n
Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 46<\/p>\n
Oppose 45<\/p>\n
Mixed 49<\/p>\n
Total 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 6.419a 6 .378<\/p>\n
Likelihood Ratio 6.485 6 .371<\/p>\n
Linear-by-Linear Association 1.469 1 .225<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: If I received the answers, I would keep silent<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: If I received the answers, I would keep silent Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 11 4 14 17 46<\/p>\n
Oppose 18 9 14 4 45<\/p>\n
Mixed 14 3 17 15 49<\/p>\n
Total 43 16 45 36 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 14.258a 6 .027<\/p>\n
Likelihood Ratio 15.338 6 .018<\/p>\n
Linear-by-Linear Association .237 1 .627<\/p>\n
N of Valid Cases 140 Crosstab<\/p>\n
Count <\/p>\n
Part III: If I received the answers, I would confess Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 21 10 8 7 46<\/p>\n
Oppose 11 10 6 18 45<\/p>\n
Mixed 22 9 5 13 49<\/p>\n
Total 54 29 19 38 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 9.701a 6 .138<\/p>\n
Likelihood Ratio 10.103 6 .120<\/p>\n
Linear-by-Linear Association .349 1 .555<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems warm<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems warm Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 3 5 21 17 46<\/p>\n
Oppose 9 7 10 19 45<\/p>\n
Mixed 6 3 20 20 49<\/p>\n
Total 18 15 51 56 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 9.182a 6 .164<\/p>\n
Likelihood Ratio 9.616 6 .142<\/p>\n
Linear-by-Linear Association .013 1 .911<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems good-natured<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems good-natured Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 6 24 16 46<\/p>\n
Oppose 6 10 12 17 45<\/p>\n
Mixed 1 6 23 19 49<\/p>\n
Total 7 22 59 52 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 15.237a 6 .018<\/p>\n
Likelihood Ratio 16.012 6 .014<\/p>\n
Linear-by-Linear Association .006 1 .937<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems confident <\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems confident Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 22 9 9 6 46<\/p>\n
Oppose 28 10 4 3 45<\/p>\n
Mixed 32 9 4 4 49<\/p>\n
Total 82 28 17 13 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 5.786a 6 .448<\/p>\n
Likelihood Ratio 5.586 6 .471<\/p>\n
Linear-by-Linear Association 3.447 1 .063<\/p>\n
N of Valid Cases 140 Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems competitive Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 5 4 19 18 46<\/p>\n
Oppose 12 4 13 16 45<\/p>\n
Mixed 4 3 22 20 49<\/p>\n
Total 21 11 54 54 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 8.288a 6 .218<\/p>\n
Likelihood Ratio 7.966 6 .241<\/p>\n
Linear-by-Linear Association .244 1 .622<\/p>\n
N of Valid Cases 140 Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems sincere Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 0 0 15 31 46<\/p>\n
Oppose 1 2 11 31 45<\/p>\n
Mixed 7 5 13 24 49<\/p>\n
Total 8 7 39 86 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 17.373a 6 .008<\/p>\n
Likelihood Ratio 20.162 6 .003<\/p>\n
Linear-by-Linear Association 11.332 1 .001<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems moral<\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems moral Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 10 7 14 15 46<\/p>\n
Oppose 25 12 8 0 45<\/p>\n
Mixed 24 8 14 3 49<\/p>\n
Total 59 27 36 18 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 32.026a 6 .000<\/p>\n
Likelihood Ratio 34.821 6 .000<\/p>\n
Linear-by-Linear Association 12.940 1 .000<\/p>\n
N of Valid Cases 140 Condition (1 = Support, 2 = Oppose, 3 = Mixed) * Part III: Abigail seems competent <\/p>\n
Crosstab<\/p>\n
Count <\/p>\n
Part III: Abigail seems competent Total<\/p>\n
2.00 3.00 4.00 5.00 Condition (1 = Support, 2 = Oppose, 3 = Mixed) Support 24 9 7 6 46<\/p>\n
Oppose 29 8 6 2 45<\/p>\n
Mixed 32 10 4 3 49<\/p>\n
Total 85 27 17 11 140<\/p>\n
Chi-Square Tests<\/p>\n
Value df Asymp. Sig. (2-sided)<\/p>\n
Pearson Chi-Square 4.392a 6 .624<\/p>\n
Likelihood Ratio 4.347 6 .630<\/p>\n
Linear-by-Linear Association 2.869 1 .090<\/p>\n
N of Valid Cases 140 Appendix 3<\/p>\n
Descriptive<\/p>\n
Part II: Abigail’s behavior was wrong <\/p>\n
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean<\/p>\n
Lower Bound Upper Bound<\/p>\n
Support 46 3.2826 .62050 .09149 3.0983 3.4669<\/p>\n
Oppose 45 3.7556 .95716 .14269 3.4680 4.0431<\/p>\n
Mixed 49 3.7143 .88976 .12711 3.4587 3.9699<\/p>\n
Total 140 3.5857 .85651 .07239 3.4426 3.7288<\/p>\n
Descriptive<\/p>\n
Part II: Abigail’s behavior was wrong <\/p>\n
Minimum Maximum<\/p>\n
Support 2.00 5.00<\/p>\n
Oppose 2.00 5.00<\/p>\n
Mixed 2.00 5.00<\/p>\n
Total 2.00 5.00<\/p>\n
ANOVA<\/p>\n
Part II: Abigail<\/p>\n","protected":false},"excerpt":{"rendered":"
Facebook Conformity and consensus Donthel Daniels Institution Affiliation Facebook Conformity and Consensus We live in a world where social media<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-49249","post","type-post","status-publish","format-standard","hentry"],"yoast_head":"\n