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Assistance with quantitative methods assignment on exploratory data analysis

With any analysis, it's integral that we describe the data that we are dealing with. In this regard, our methods assignment solver is obliged to assist you in data description by using an example and a dataset.

The data for this work is collected from the General Social Survey's cumulative data file. The GSS is a survey conducted by the NORC from 1972 to date. It is conducted every year from 1972 until 1994 with and from 1994. It was collected biennially to date. The subjects were a non-institutionalized person of over 18 years. The data set consists of 64,814 interviews spread over the years, with 2,867 of the interviews in 2016 and 2,348 in 2018). From the total number of datasets, we filter out all observations with missing values, which leave us with 941 observations, which is used as the sample for this analysis.

We used five of variables for this study: Gun law; Pres16; Degree; RaceHisp; Sex. For the Gun law variable, the respondents were asked if they would favor or oppose a law that required an individual to seek a permit before the individual buys a gun. The response was coded as one if the respondent favors or two if the respondent does not favor while if missing, it is either coded as 0, 8, or 9. For Pres16, the respondents were asked if they voted Clinton or Trump. The response was coded as one if the respondent voted Clinton, two if the respondent voted Trump, three if the respondent voted other candidates, and four if the respondent did not vote while missing values were either coded as 0, 8, or 9. For degree, candidates were asked if they ever did get a GED certificate or a high school diploma. Responses were labeled as 0 if respondents have less than high school; 1 if respondents finish high school; 2 if respondents finish junior college; 3 if respondents have bachelor degrees, and 4 if respondents have graduate degrees. Missing values were coded 7, 8 or 9. For RaceHisp, respondents were asked of their race, and the response is coded 1 for a White respondent, 2 for a Black respondent, 3 for a Hispanic respondent, and 4 from the other race. Missing data is coded as 9. Finally, for sex, respondents were asked of their sex, and the response is coded as 1 if it turns out that the respondent was male and 2 if it turned out that the respondent was a female.

Professional help with quantitative methods homework on hypothesis testing

For the purpose of this study, four hypotheses will be tested; they are

H01: voters who voted for Clinton are not more likely than those who voted Trump to favor a law which requires an individual to seek a permit before the individual buys a gun

H02: Those with a bachelor's degree or more are not more likely than those with less than a bachelor's degree to favor a law which requires an individual to seek a permit before the individual buys a gun

H03: Whites are not more likely than non-whites to favor a law which requires an individual to seek a permit before the individual buys a gun

H04: a female is not more likely than male to favor a law which requires an individual to seek a permit before the individual buys a gun

For all hypotheses, the dependent variable is "GUNLAW" while the independent variables are "PRES16", recoded "degree," recoded "racehisp" and "sex." For hypothesis one, we will restrict the sample to those who voted Trump or Clinton only. For hypothesis two, we will recode the variable "degree" into two categories; less than a bachelors' degree and bachelors' degree or more. For hypothesis three, we will also recode the racehisp variable to two levels; white and non-white.

The descriptive statistics (frequency, median, minimum, and maximum) were examined, and we found no evidence of distortion or entry error in the data. Thus, we do not present the descriptive statistics in the study. The quantitative methods homework helper tested all hypotheses were tested by cross-tabulation and chi-square. We set our significance level as 5%, meaning that when the p-value>0.05, we proceed to estimate the strength of the association. The results are presented in the following sections.

Results presentation by an online quantitative methods tutor

Table 1 shows the cross-tabulation of those who favor or oppose gun law and those who voted Clinton or not. The null hypothesis that voters who voted for Clinton are not more likely than those who voted Trump to favor a law which requires an individual to seek a permit before the individual buys a gun 21=99.510, p<0.001,phi=0.339). To buttress this finding, those who voted Clinton sowed a larger difference in percentage (68.4%), while those who voted Trump showed a smaller difference in percentage (6%). The phi value of 0.339 shows that there is a moderate association between voting patterns and whether one will favor or oppose gun law.
Table 1: Favor or oppose gun permits and vote Clinton or not vote Clinton or not

Favor or oppose gun permits Clinton Trump Total
favor 425(84.2) 192(53.0) 617(71.2)
oppose 80(15.8) 170(47.0) 250(28.8)
total 505(100) 362(100) 867(100)
column percentages in parenthesis
Pearson chi-square=99.510,df=1,p<0.001,phi=0.339

Table 2 presents the cross-tabulation of those who favor or oppose gun law and respondents' highest degree. The null hypothesis that those with bachelors' degree are not more likely than those with less than a bachelor's degree to favor a law which requires an individual to seek a permit before the individual buys a gun cannot be rejected 21=0.895, p=0.344). In fact, we see that the percentages of those with less than a bachelors' degree that favor (70.1%) is close to that of more than a bachelors' degree that favor (73.0%).
Table 2: Favoring or opposing gun permits and respondents highest academic record respondents highest academic record

Favoring or opposing gun permits less than a bachelor's degree bachelor's degree or more Total
favor 403(70.1) 267(73.0) 670(71.2)
oppose 172(29.9) 99(27.0) 271(28.8)
total 575(100) 366(100) 941(100)
column percentages in parenthesis
Pearson chi-square=0.895, degree of freedom=1, pvalue=0.344

Table 3 presents the cross-tabulation of those who favor or oppose gun law and if the respondent is white or non-white. The null hypothesis that Whites are not more likely than non-whites to favor a law which requires an individual to seek a permit before the individual buys a gun 21=15.526, p<0.001,phi=-0.128). However, contrary to what we expect, non-white are more favorable to gun law (80.4%) compared to white (67.5%). The phi value of -0.128 was enough to show our online quantitative methods tutor that there is a negative and weak association between race and favoring or opposing gun law.
Table 3: Favor or oppose gun permits and respondents race White or non-white

Favor or oppose gun permits White Non-White Total
favor 453(67.5) 217(80.4) 670(71.2)
oppose 218(35.2) 53(19.6) 271(28.8)
total 671(100) 270(100) 941(100)
column percentages in parenthesis
Pearson chi-square=15.526,df=1,p<0.001, phi=-0.128

Table 4 presents the cross-tabulation of those who favor or oppose gun law and gender of the respondents. The null hypothesis that female is not more likely than male to favor a law which requires an individual to seek a permit before the individual buys a gun is rejected 21=29.305, p<0.001,phi=-0.176). More females favor gun law (78.5%) compared to male (62.5%) which means that female are more likely than male to favor a law which requires an individual to seek a permit before the individual buys a gun. The phi value of -0.176 showed that there is a weak and negative association between favoring or opposing gun law and gender.
Table 4: Favoring or opposing gun permits and respondents' sex Male gender or female gender

Favoring or opposing gun permits male female Total
favor 268(62.5) 402(78.5) 670(71.2)
oppose 161(37.5) 110(21.5) 271(28.8)
total 429(100) 512(100) 941(100)
column percentages in parenthesis
Pearson chi-square=29.305,df=1,p<0.001, phi=-0.176