Why do some people like New York more than others?
Problem: Do people who are living in New York City really “like” the place or they just wanted to be there because they “have to” do their family duties, job, education? Is there any significant difference between the likings of people to live in the city in terms of their gender, age, years in the city, education, love of living in city? Based on the study of Milgram S. (1970), most people wanted to live in cities because it has the density and numbers to support all kinds of activities. According to http://www.truehome.com, people tend to choose New York because of its capability to suffice the needs of different people.
Method: To evaluate the likings of the people to New York City, an anonymous survey questionnaire was given to 70 individual. The said survey questionnaire are comprises of 21 good questions. Most of the items are about demographics of the respondents including their response if they “like to” or “have to” live in NYC. From these 70 total population, 10 of it were randomly selected and to be evaluated in this paper.
Results: From the given survey report, 50% of the respondents stated that they “have to” live in New York City and another 50% of them agreed they really “like to” live in New York City. The survey also justifies that because of various capabilities of NYC to give the needs of people, “they have” to live in the place. From the data gathered to 10 randomly selected respondents, 60% of them are males while there are only 40% of females. The average age of these subjects is 39 years old. Actually, 50% of them are college, 20% from high school and 30% who are in graduate studies. With regards to ethnicity of the surveyed subjects, 60% of them are black, 20% are white, 10% are Hispanic and another 10% of them are Asian. Through the use of ANOVA, this report also identifies the significant differences between the preferences of people with respect their demographics. Table 1 shows the summary of ANOVA with respect to their preferences. From these results, we can say that there is no significant difference between the likings of people to live in New York with respect to gender, age, years in the city, education and the degree of their preferences if they love New York.
Discussions: The results of the analysis pertaining to preferences of respondents whether they “like to” or “have to” live in New York are presented in Table 1. In one-way ANOVA, each items of the total variation is partitioned into two components. Between Groups represents variation of the group means around the overall mean (Einstein, G. & Abernethy, K. 2000). Within Groups represents variation of the individual scores around their respective group means. Basically, .sig indicates the significance level of the F-test. Small significance values (<.05) indicate group differences (Einstein, G. & Abernethy, K. 2000). In our data, the significance level of most items are more than 0.05 except to love category. Actually less than 0.05 results signify that at least one of the regions differs from the others. From our results, it is evident to state that most people have to live in New York because of their love ones and not only because they just like the place. Their demographics such as gender, age, years in the city, education and the degree of their preferences if they love New York receives similar responses if they “like to” or “have to” live in New York City. Meaning to say, their demographic profile has no significant effect to their preferences whether they “like to” of “have to” live in New York.
With regards to their view pertaining to City life and preferences, Table 2 and Table 3 described the break down of the responses of the respondents. In Tables 2 and 3, the respondents were asked to rate each statements in accordance to their personal choice. Table 2 described how the people like to live in New York. From the results, it seems that majority of the respondents dislike to live in New York because of unpredictability of experiences and constant sounds in the city. Apparently, the variable pertaining to fast pace change and streets full of people receives an average neutral response. Actually, people like to live in New York because of people itself. Meeting new people and ethic diversity present in New York City are reasons of most surveyed individuals to live in the city.
In accordance to the personal preferences of the respondents included in the survey, majority of them stayed neutral on each statement except to statement 3. It states that “I like continually changing activities”. Actually, the average response for this statement is agree. Actually, these responses reflect that most of the surveyed respondents living in New York are agree or even disagree on the current lifestyle in New York. To verify if the personal preferences of the surveyed individual is correlated to their city life the use of correlation statistics was initiated. Table 4 shows the relationship of city life and personal preferences of the respondents. From the average respondents in each category, the computed correlation value (i.e. r = 0.684) justified that there is significant relationship between the variables. Actually, the correlations table displays Pearson correlation coefficients, significance values, and the number of cases with non-missing values. Pearson correlation coefficients assume the data are normally distributed (Einstein, G. & Abernethy, K. 2000). The Pearson correlation coefficient is a measure of linear association between two variables. The values of the correlation coefficient range from -1 to 1. The sign of the correlation coefficient indicates the direction of the relationship (positive or negative) (Einstein, G. & Abernethy, K. 2000). The absolute value of the correlation coefficient indicates the strength, with larger absolute values indicating stronger relationships (Einstein, G. & Abernethy, K. 2000). Einstein, G. & Abernethy, K. (2000) stated that the correlation coefficients on the main diagonal are always 1.0, because each variable has a perfect positive linear relationship with itself. Correlations above the main diagonal are a mirror image of those below. In our data, the correlation coefficient for the variable is 0.684. Since 0.684 is relatively close to 1, this indicates that the variables are positively correlated. Moreover, the significance of each correlation coefficient is also displayed in the correlation table. The significance level (or p-value) is the probability of obtaining results as extreme as the one observed. Since significance level is very small (less than 0.05) then the correlation is significant and these two variables are linearly related. Meaning to say, as the respondents personal preferences increases in the categories described in Table 3, their likings towards living in New York also increases.
Aside from these findings, it is also discovered that those people who choose to live New York because they “like to” is also similar to the responses of people who “have to” live in New York with respect to City life and Personal preferences (see Appendix for t-test results). Meaning to say, there is no distinction of responses whether they “like to” or “have to” live in New York with respect to City life and Personal preferences. Although the group statistics table in Appendix, shows that most the respondents who chooses to live in New York because they “like to” is better compared to those who are living in the city because they “have to” (refer to mean values in group statistics). Actually, the survey also justified that those people who love the city tend live in New York because they “like to”. The survey shows that those people who live in New York because they “have to” only shows 4.20 mean responses in accordance to the degree of love in the city. While those people who live in New York because they “like to” shows expressive averages in terms of love in the city i.e. 7.20 mean responses. From these results, we may say that those people who love to live in the city will live in New York because they “like to” and not they “have to”.
References:
Einstein, G. & Abernethy, K. (2000). Statistical Package for the Social Sciences (SPSS Version 10.0). Greenville, South Carolina: Furman University.
Milgram, S. (1970). The experience of living in the cities. Science, 167, 1461-68.
http://www.truehome.com
Appendix
The Independent-Samples T Test procedure compares means for two groups of cases. The mean values for the two groups are displayed in the Group Statistics table. If the significance value for the Levene test is high (typically greater that 0.05), we may use the results that assume equal variances for both groups. On the other hand, if the significance value for the Levene test is low, we need to use the results that do no assume equal variances for both groups.
A low significance value for the t test (typically less than 0.05) indicates that there is a significant difference between the two group means. If the confidence interval for the mean difference does not contain zero, this also indicates that the difference is significant. If the significance value is high and the confidence interval for the mean difference contains zero, then we cannot conclude that there is a significant difference between the two group means.
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