Quantitative Research Report on the Frequency of Occurrence of Divorce - Essay Example

Quantitative Research Report on the Frequency of Occurrence of Divorce By [Full [Lecturer’s and Number] Introduction Family change is the one of the most prominent social trends of recent times in developed countries such as Britain. Under family change we have mainly have marriage and divorce. There has been change in attitudes in recent decades concerning issues like unmarried cohabitation that used to be considered a major social deviation. This issue affects marriage statistics and in turn, divorce. This essay is aimed at analysing statistical data on divorce that has become a prevalent issue in today’s society. Divorce takes place because of one or several major failures in marriage hence the essay will also include sections on marriage and marriage statistics. In addition, unmarried cohabitation will be mentioned in relation to how it affects divorces statistics. The main hypothesis is that the frequency of divorce is due to attitude shifts concerning marriage and the rate of divorce is not affected by number of marriages but is rather a factor of it. Problem Definition The purpose of this essay is to test the hypothesis using regression analysis and significance tests, all of which are part of the SPSS (Statistical Package for Social Sciences). The aim of regression analysis is to estimate the effect or impact of a given independent variable on variation in the dependent variable. The divorce data to be handled will not include time series data. Specifically, this paper will cover: ages of husband and wife at marriage; marital status of husband and wife at the time of marriage; ages of both husband and wife at the time of divorce; civil partnership formations by sex and age groups; and civil partnership dissolutions by sex and age groups. Ages at Marriage Age of Husband at Marriage Age of wife at marriage All ages Under 20 20-24 25-29 30-34 35-39 40-44 45-49 50 and over All Ages 118,140 2,710 26,783 36,697 23,983 13,146 7,246 3,830 3,745 Under 20 9,195 1,753 5,028 1,694 468 152 66 24 10 20-24 37,445 765 16,315 13,811 4,443 1,384 436 161 130 25-29 34,055 127 3,982 15,553 9,260 3,288 1,181 416 248 30-34 18,393 43 986 3,971 6,691 4,029 1,669 626 378 35-39 9,569 14 312 1,121 2,155 2,795 1,806 847 519 40-44 5,056 3 103 370 660 1,051 1,399 831 639 45-49 2,556 4 42 124 223 326 509 649 679 50 and over 1,871 1 15 53 83 121 180 276 1,142 Table 1: Ages of husband and wife at marriage in combination 1. Regression analysis Simple linear regression is used to model the relationship between one independent variable and one dependent variable. The model is: Yi = β0 + β1Xi + εi Here, (Xi, Yi), i = 1, …, n are the sample values of the response and exploratory variables and εi are random disturbance terms assumed to be normally distributed with mean zero and variance σ2. The intercept parameter, β0, is the value predicted for the response variable Y when the explanatory variable X takes the value zero. The slope parameter, β1, is the change in the response variable predicted when the explanatory variable is increased by one unit. The parameters, also known as regression coefficients, can be estimated by least squares. The results of linear regression between the ages of wives and the ages of husbands is as follows Regression Statistics Multiple R 0.917344202 R Square 0.841520384 Adjusted R Square -1.333333333 Standard Error 5525.520588 Observations 1 ANOVA   df SS MS F Significance F Regression 8 972722327.4 121590290.9 31.85975866 #NUM! Residual 6 183188266.6 30531377.77 Total 14 1155910594       2. Significant tests T-test t-Test: Two-Sample Assuming Unequal Variances   Variable 1 Variable 2 Mean 14767.5 14767.5 Variance 165130084.9 195321678.3 Observations 8 8 Hypothesized Mean Difference 0 Df 14 t Stat 0 P(T Read More