- Preliminary/design
Being happy in a marriage is important, but who tends to be happier in the marriage, a male or female. According to the general social survey (2014) data sets, I will be conducting my research on happiness of marriage to figure out which gender is happier, so my research question is: Does the type of gender one marries affect the happiness of their marriage?
The data sets I’m going to be using for this project is happiness of marriage, the plan is to figure out if gender affects the happiness of their marriage. The type of groups I’ll be using are male and female. Based on the groups being presented, I will figure out the happiness of a male and female. The method of sampling being used is probability sampling, and the method being used is random sampling. The type of data collection I will be doing is quantitative data analysis. The size of my study would be 2 individuals, a male and female
The Independent variable I will be using is gender, the dependent variable is happiness of their marriage. Type of gender would be coded as, 1= Male (nominal) and 2= Female (nominal), and happiness of marriage would be measured as (Scale), 1=Very happy, 2=Pretty happy, 3=Not too happy, and 4=N/A. I will not be recoding any of my variables
Univariate Analysis
Independent Variable: Gender
Frequencies
| Statistics | ||
| GENDER OF 1ST PERSON | ||
| N | Valid | 2538 |
| Missing | 0 | |
| Mean | 1.44 | |
| Median | 1.00 | |
| Mode | 1 | |
| Std. Deviation | .496 | |
| Minimum | 1 | |
| Maximum | 2 | |
| GENDER OF 1ST PERSON | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | MALE | 1431 | 56.4 | 56.4 | 56.4 |
| FEMALE | 1107 | 43.6 | 43.6 | 100.0 | |
| Total | 2538 | 100.0 | 100.0 | ||
I was able to compute the variable by using frequencies in SPSS, which reports frequency counts (the number of cases with each unique value of a variable) and the percentages for the selected variable. Aside from that, frequencies can help us see the results in a histogram or bar charts. I attained the tables by entering the independent variable, which is ‘gender of 1st person.’ After inputting the variable in SPSS using the frequency function, I attained a mean of 1.44, median 1.00, and mode of 1. The standard deviation of the variable was .496. The maximum was 2 and minimum was 1. The amount of cases in the variable was n=2,538. The amount of missing cases was 0. According to the variable ‘gender of 1st person,’ I noticed the Histogram displayed more males than females, which tells us there is more males than females. The frequency count for male was 1431 and female was 1107. The amount of people in a specific location was articulated at 1.44 in the Histogram. This allows us to conclude that there are more males than females in the variable ‘gender of 1st person.’
Dependent Variable: Happiness of marriage
Frequencies
| Statistics | ||
| HAPPINESS OF MARRIAGE | ||
| N | Valid | 1155 |
| Missing | 1383 | |
| Mean | 1.44 | |
| Median | 1.00 | |
| Mode | 1 | |
| Std. Deviation | .560 | |
| Minimum | 1 | |
| Maximum | 3 | |
| HAPPINESS OF MARRIAGE | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | VERY HAPPY | 691 | 27.2 | 59.8 | 59.8 |
| PRETTY HAPPY | 425 | 16.7 | 36.8 | 96.6 | |
| NOT TOO HAPPY | 39 | 1.5 | 3.4 | 100.0 | |
| Total | 1155 | 45.5 | 100.0 | ||
| Missing | IAP | 1376 | 54.2 | ||
| DK | 2 | .1 | |||
| NA | 5 | .2 | |||
| Total | 1383 | 54.5 | |||
| Total | 2538 | 100.0 | |||
I was able to compute the variable by using frequencies in SPSS, which reports frequency counts (the number of cases with each unique value of a variable) and the percentages for the selected variable. Aside from that, frequencies can help us see the results in a histogram or bar charts. I attained the tables by entering the independent variable, which is ‘happiness of marriage.’ After inputting the variable in SPSS using the frequency function, I attained a mean of 1.44, median 1.00, and mode of 1. The standard deviation of the variable was .560. The maximum was 3 and minimum was 1. The amount of cases in the variable was n= 1,155. The amount of missing cases was 1,383 According to the variable ‘happiness of marriage,’ I noticed the histogram displayed more people being happy than not happy. The amount of people in a specific location was articulated at 1.44 in the Histogram. The frequency count for very happy was 691, pretty happy 425, and not too happy 39. This allows us to conclude that variable ‘happiness of marriage’ has more happy individuals than not too happy.
Bivariate Analysis
T-Test
| Group Statistics | |||||
| GENDER OF 1ST PERSON | N | Mean | Std. Deviation | Std. Error Mean | |
| HAPPINESS OF MARRIAGE | MALE | 817 | 1.40 | .547 | .019 |
| FEMALE | 338 | 1.51 | .583 | .032 | |
Using T-test, is going to help us determine whether the difference between means of two groups is due to the independent variable, or if the difference is due to chance. Thus, this procedure allows us to reject or retain the null hypothesis.
The first table shows the mean of variable ‘happiness of marriage’ and ‘gender’. The mean for male is 1.40 and female 1.51. The standard deviation for male is .547 and female is .583, but we cannot conclude if there is a significant difference. The second table, which is the Independent sample test, is going to help is determine the significance between ‘gender’ and ‘happiness of marriage’.
The second table has a column labeled Levene’s Test for Equality of Variance, which provides assumptions of the t-test, based on the variables ‘happiness of marriage’ and ‘gender.’ The sig is .003, which is less than .05. Thus, we are going assume the two groups are significantly different, so we are going to use the equal variances not assumed row. To determine the happiness of marriage between male and females is significant, we have to look at the t-test for equality of means. Looking at the equal variance not assumed row, we see a t value of .003. The Sig.(2-tailed) column in the (p=.003) is less than .05, meaning that there is a statistically significant correlation between variables ‘happiness of marriage’ and ‘gender.’ We can conclude that females are happier in a marriage.
Multivariate Analysis
Regression
| Variables Entered/Removeda | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | White race household, R WAS LAID OFF MAIN JOB LAST YEAR, GENDER OF 1ST PERSONb | . | Enter |
| a. Dependent Variable: HAPPINESS OF MARRIAGE | |||
| b. All requested variables entered. | |||
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .114a | .013 | .008 | .559 |
| a. Predictors: (Constant), White race household, R WAS LAID OFF MAIN JOB LAST YEAR, GENDER OF 1ST PERSON | ||||
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 2.403 | 3 | .801 | 2.563 | .054b |
| Residual | 182.514 | 584 | .313 | |||
| Total | 184.917 | 587 | ||||
| a. Dependent Variable: HAPPINESS OF MARRIAGE | ||||||
| b. Predictors: (Constant), White race household, R WAS LAID OFF MAIN JOB LAST YEAR, GENDER OF 1ST PERSON | ||||||
The model summary table gives us the response of R square=.013, we can say that the model explains 13% of the variation.
The Anova table gives us the results of the independent variables, we are going to be focused on the value located in the “Sig.” column, because this is the exact significance level of the ANOVA. The value for Sig is .054, which means it does not have significant effects. We are going to reject the hypothesis, and we can conclude that the variables are the same and that multiple regression will not be possible in this case.
In the Coefficient table, it gives us a constant of 1.474, gender of 1st person .103, and R was laid off main job last year -.065, and White race household -.086
Y= 1.474 (constant)+.103 (gender)-.086(White race household) -.065(Laid off)
The coefficient for gender is .103 and it has a positive relationship with happiness of marriage, which means both male and female are happy in a marriage. We can assume that people from a white race household (white or non-white) does not have a significant impact on how happy a person is in a marriage. Moreover, being laid off the main job last year had a coefficient of -.061, and we can assume that being laid off last year does not affect the happiness of marriage.





attributes of homework: responsibility(when students assume responsibility for their homework and complete an assignment it only then that they learn to be accountable for their actions),time management(students complete their assignments or projects on time when they are organized), perseverance(homework teaches kids how to deal with adversity), and self-esteem(completing homework in a timely manner will help your child develop trust and self-confidence. As I stated before, the amount of homework that is consider too much is 2 or more pieces of homework. That could mean a student is spending more than an hour on a single piece of paper. If educators can lessen the homework, the students would be able to have time with family, friends, and extracurricular activities. Therefore, it creates a healthy and stress free environment.

