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Descriptive Analysis of a Study of Low Income and Marriage

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This paper will be used to discuss the descriptive statistical analysis of a study based on marriages in low-income couples and families. Other aspects that require to be analyzed in this study include the demographic variables of location, age, education, and children in the marriage. Each of these variables can play a role in the success or failure of the marriage of a low-income couple. Knowing this it is important to analyze each variable in relation to the income level of the married couple.

The study evaluated ensures that these variables are recognized and explained in relation to the programs to help low-income couples succeed in their first marriages. Application of descriptive statistics in the article. Most of the information was given to the reader through frequency graphs including polygraphs and bar graphs. Frequency tables were also used on two occasions, as well as the tabulated means of each type of variables correlation to low-income and marriage.

This type of descriptive statistic makes it easy to see exactly what the numbers are saying in regard to nominal and ordinal variables. The data from several studies from 1980 to March 2003 were used to get a sample population with the necessary information to provide a better understanding of the larger population. While the graphs are good, the frequency tabulations work a little better and give more information quickly. This type of descriptive statistic makes it easy to see the information on several variables quickly.

The exhibit 8 of this study is a great way to see all of the variables that contribute to marriage and low income and how the demographics work. The table offers information on age, education, children under 6 years old and over 6, location in the nation, and employment status for both married men and women in low-income households. For example, using this graph and looking at non-Hispanic White women, it can be seen that 27% have 2 children, 37% have children under 6 years of age, 18% are in the south Atlantic states, 28% are between the ages of 25 and 34, and 53% are not in the labor force.

This same information can be looked at for each of the other ethnic groups and conclusions can be drawn. Other possible methods to describe the data Frequency tables are good as are graphs, but this only scratches the surface of descriptive statistical analysis. The fact is that the central tendency when used gives the average of the variable, both in nominal variables and ordinal variables. The average is always a good descriptive statistics because it can be assumed that the distribution of responses will equal 50% on each side and will create a normal distribution.

However, the fear of using the mean is always possible in cases where there are outliers, or extreme cases that skew the distribution causing either a ceiling or floor effect in one tail. When this occurs, rejecting the data from the extreme is perfectly acceptable, but must be accounted for in the finding and explained to prove that it would have hurt the data rather than help prove or disprove the hypothesis.

The standard deviation is also helpful when trying to understand the distribution of responses. The standard deviation is the amount from the mean of the distribution. An acceptable standard deviation would be <. 10 because anything higher could create inaccurate information. Inaccurate information not only hurts the credibility of the study, but also the credibility of the researcher.

Conclusion Descriptive statistics are wonderful and helpful. They have made parts of the world more understood. Even when studying concepts and nominal variable, the use of descriptive statistics can help give the researcher and the field of research a better understanding which in turn provides more help for the general population. These descriptive statistics work wonders in all scientific fields, but they need to be used appropriately and not misused to give inaccurate information. When using descriptive statistics it is important to give little room for error and be as credible as possible to ensure the study will gain the readership it needs to be useful.

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