The Impact of Culture on Divorce Rates
The data utilized entails 5% of the Microdata of the Public-Use sample collected from the United States census which was conducted in 2000. The sample included immigrants from Europe who moved to the United States when they were below five years of age. After the relocation, the immigrants became used to the US institutions, the laws, and the markets. However, the attitude reflected that of their ethnic groups. The sample does not include the conflict zones like Yugoslavia and Albania. To be precise, the final sample gathered contained 12,069 immigrants from several nations in Europe. The immigrants who moved to the United States below the age of five years had a high chance of practicing their parent's culture and that of their ethnic groups. However, when they were introduced to the US laws, the estimates of the home country divorce rates were affected by their culture. From the above, it is evident that the variations in the divorce rates are not linked with the cross-country divorce law variations, the welfare policies, and the economic conditions in Europe.
The Role of Age and Education in Divorce Rates
More research proved that age is one of the determinants of divorce. Young people are more likely to get a divorce compared to older people. Also, the study showed that women are less likely to divorce compared to men. The divorce rates among people with higher education levels are lower compared to the less educated. The conclusion is that the impact of the approximated home country divorce rates is both statistically significant and positive. The research question was meant to determine the culture's role in the making of decisions concerning divorce by finding the difference in the divorce rates among the countries of origin of the US immigrants. The findings were against the hypothesis since age, and the level of education has an impact on the divorce rates.
Attitudes Towards Capital Punishment and Marital Status
People have varying attitudes towards capital punishment depending on their marital status. Individuals who are unmarried tend to support capital punishment. On the other hand, married people do not support it since they have children who may face charges in court. They would feel a lot of pain if their children were prosecuted or if they faced capital punishment. In many occasions, the unmarried people lack emotional connection with the individuals who experience capital punishment. As a result, most of them end up supporting capital punishment.
Hypothesis Testing and Statistical Analysis
The significance level is set at 0.04. It is because 0.04 is enough to cater for any errors that may arise during the study. Since the significance level is below 0.05, it is vital to reject the null hypothesis. The hypothesis that was used stated that the unmarried people are most likely to be in support of capital punishment. The null hypothesis was not used since it was not useful for the current study. It is because the target population may not reflect married people. In the current statistics, most of the people are either single or divorced. Such people may not be identified as married. Therefore, the alternative hypothesis would accommodate the unmarried people like the widowed, single, and the divorced. The population would provide a clear understanding of how capital punishment and marital status are related. The findings will be used in the broader scope of the community. The first step in hypothesis testing is to state the null hypothesis. The null hypothesis is usually the opposite of what the researcher expects. For the case of capital punishment and marital status, the null hypothesis was that the unmarried people are not likely to support capital punishment (Goertz, 2012). The next step is to state the alternative hypothesis which indicates that the unmarried people are highly susceptible to support capital punishment. The next step is to set the alpha. Later on, the collection of data is carried out. Finally, it is important to calculate a test statistic, come up with an acceptance or regression regions and then conclude the null hypothesis.
Descriptive and Inferential Statistics
Elementary statistics provides the right foundation for specialized expertise in statistical analysis. Some of the key concepts include variables, correlational research, dependent and independent variables, measurement scales, the relationship between the variables, the p-value, and the importance of the relationships. Variables can be described as items that can be manipulated, controlled, and measured. They differ from one another due to their role in the specific research and the measures that can be used on them. In the correlational analysis, variables are measured, and relations are determined. In this research, dependent variables which can only be measured are used. The measurement scale used differentiates the variables. That is the amount of measurable information that the utilized measurement scale can offer. Each measurement has a measurement error which affects the amount of information that can be collected (Muijs, 2010). Two or more variables are said to be related if the observed values of the variables are consistently distributed. The relations need to be determined to give meaning to the research analysis.
Descriptive and inferential statistics are broad categories in the field of statistics. Some statistical measures are the same; however, they may have different methodologies and goals. Descriptive statistics are used to summarize data for a given sample and graph it. The process enables the researcher to identify the specific set of observations. Since it describes a sample, descriptive statistics is considered straightforward. It entails recording data about a particular sample then utilizing summary statistics and graphs to present the properties of the chosen sample. With descriptive statistics, the researcher is sure about the results because it entails describing the items or people who have been measured. Here, the researcher does not infer properties over a more significant population. The process involves reducing the data collected into several meaningful summary values and graphs. With this procedure, the researcher gains more insight and can visualize the data by going through the rows and columns.
Descriptive statistics use several statistical measures to describe a specific sample. Some of the statistical tests include dispersion, central tendency, and skewness. In the central tendency, mean or median is used to find the center of the data set. The measure informs the researcher where most of the values lie (Brannen, 2017). Dispersion explains how far the data extends from the center. To measure dispersion, range or standard deviation is utilized. When there is a low dispersion, it means that most of the values are close to the center. When there is a high dispersion, it implies that the data points are far away from the center. The data received here can be used to come up with a frequency distribution graph. The skewness measure informs the researcher whether the data is symmetric or skewed. It is represented using graphs and numbers.
Inferential statistics use the data collected from a sample to infer to a larger population. The primary goal of this form of statistics is to come up with conclusions about a specific sample and generalize it. In such a case, the researcher needs to be confident that the sample chosen reflects the population correctly. It entails defining the population being studied, coming up with a representative sample for the population and utilizing analyses that implement the sampling era. The sampling process is done randomly to ensure that the population is well represented. It is a primary method employed when finding samples that mirror a larger population. From the sampling, statistics are collected like the mean that is usually not too high or too low. The results from the random sample are generalized to be the characteristics of a broader population. Unfortunately, getting an accurate random sample is quite difficult. The tools used in inferential statistics include hypothesis tests, regression analysis, and confidence intervals. The inferential methods may produce the same values as the descriptive methods. For instance, they may give the equal standard deviation and mean.
Relationship Between Capital Punishment and Marital Status
When establishing the relationship between the variables, capital punishment, and marital status, the table below was extracted from the SPSS.
From the original martial variable, it was found that the two variables do not relate in any way.
From analyzing the Gamma results, little difference is observed. It indicates that in a different situation there is a better indication of whether or not the marital status of a person has an impact on how they view the issue of capital punishment. The Pearson's R results show the value of R as 0.01-0.09. The results indicate that the variables have no association. The information above is supported by the figure below from SPSS.
It provides a naught reading using the Lambda result.
The last experiment shows how gamma results improved. The data collected is dichotomous and nominal using only two variables in the N-Mar. It is different from the multiple choice variable that was used before. The results from the experiment are 0.05 or 0.245 x 0.245. The reading shows that Pearson's R relates well up-to negative or -0.10 to 0.28 which is considered the moderate association area.
The Importance of Quantitative Research
Quantitative techniques focus on measurement and statistical analysis of data gathered through surveys or questionnaires. The data collected would assist in the explanation of a certain phenomenon. When conducting quantitative research, the primary objective is to identify the type of relationship between the chosen variables. The frequent use of hierarchies of evidence that evaluates research studies based on their quality has indicated that not all forms of evidence have the same level of trustworthiness. The contraindications and indications of the different kinds of evidence need to be understood. Also, the evaluation of the studies should never be conducted in isolation.
Conclusion
In conclusion, it is evident that quantitative research is essential in the determination of the relationship between two variables. After the statement of the null and alternative hypothesis, the study is conducted, and the numerical data collected is analyzed. From the above research, it was concluded that the null hypothesis was wrong and had to be rejected. The adopted hypothesis was that unmarried people are highly likely to support capital punishment.
References
Brannen, J. (2017). Mixing methods: Qualitative and quantitative research. Routledge.
Muijs, D. (2010). Doing quantitative research in education with SPSS. Sage.
Goertz, G., " Mahoney, J. (2012). A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton University Press.
