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Describe and apply the concepts and logic of elementary statistics.
Conduct statistical analysis in SPSS (Statistical Package for the Social Sciences).
Compare and contrast different types of data and the statistics that can be used to analyze them
Examine the differences between descriptive and inferential statistics and their use in the social sciences.
Form critical interpretations of quantitative research literature in sociology and other social sciences.
Complete and interpret descriptive and inferential statistical data analysis
Critically evaluate the quality of research design and evidence in published social research.

Answer:

Introduction

From history there has been evidence of gender disparity in employments opportunities with men acquiring high position in society than women. Women have been traditionally been associated with home position. This has been improving as time goes by but this stereotype is still among the society. With introduction of formal education females has tried to push their way in much position in the society; however gender disparity in employment is there in many organizations.

Job opportunities are affected by different factors such as level of education, experience level, age and gender. In recent research to identify cause-effect relation between education level in women and job opportunities, researchers have done study to identify if as the women increase their education up to PHD level is there likelihood of obtaining a job. Women representation job opportunities are still lower both in management level, education position and many other sectors. When starting the difference between the two genders is small it then accumulates with time with many disadvantages to female, (Schuster & Finkelstein, 2006).

In science technology and mathematics there is large disparity in position by gender, female are few as compared to male. In teaching position female are more as compared to men, (Larivière et al, 2013). There is evidence that gender affect job position with many females held in right position and male in higher position. Also previous studies have identified gender difference in research with high number of male holding those positions, (Hango, 2013).

This job significance disparity by gender is one of contemporary issues that need immediate solution as they are main challenges facing the society. Many people have said that this needs to be addressed in order for the society to attain job equality. This has been a burning issue mainly among female as they see themselves underrepresented in various positions, (Prpi?, 2002).

Research hypothesis

The main research problem of this study is


Do women have fewer opportunities in the workforce than men? The study tries to identify if there is gender disparity in job employments in USA.  If men are more likely to be employed as compared to women?

The research hypotheses are:

The null hypothesis: woman has higher likelihood to obtain a job or promotion in the workforce when competing with other men for the same job or promotion.

Alternative hypothesis: Women do not have higher likelihood to obtain a job or promotion in the workforce when competing with other men for the same job or promotion. 

Research Design

The researcher adopted secondary data sources. Secondary data is useful in defining the sample and identifying the population, (Alderman & Salem, 2010). The data were obtained from General Social Survey which is an organization that conducts social survey in USA and provides many scholars, policy makers and politicians with necessary information (GSS, 2017). Initial the study adopted descriptive survey to obtain quantitative and qualitative data. The study carried out a quantitative design where both descriptive and inferential analysis was used. Data was collected from typical American men and women, through face to face interview, interview through the phone and computer assisted interviews surveys, (Kothari, 2004).

Sampling technique

The population under study is the American men and women. The participants of the researcher American men and women who are current working adults or searching for employment in the workforce from all races that Hispanic, Indian , Asian, Jewish, Arabs, White and Black. Currently living in United States of America and can speak, read or write in English to be able to fully participates in the survey. The sampling technique that was used was stratified sampling technique because the population was heterogeneous in nature. The American population which is composed off different nationality and race, but the target was working class and those looking for opportunities. The target population consisted of two groups that are male and female. First the population was divided into two. Then simple random sampling was used to select those who participated in the sample. This method allowed the sample to be representative of the population and enable each individual in the target population to have equal chance to be in the sample thus eliminating researcher’s bias. A sample of 1974 was selected who fully participated in the research, (Neuman, 2014).

Data analysis method

The methods of data analysis utilized in this study were inferential and descriptive statistics. The data that was obtained was quantitative in nature making it possible to compute various statistical analysis and inferences (Perry & Perry,  2014).Descriptive statistics are used to describe the distribution of data and summary of the sample under study. They organize, summarize and present data in way which is simple to understand and to make conclusion. The most commonly used descriptive statistics are measure of central tendency that is mean, mode and median which shows the location where most observations falls, measure of dispersion such as variance, range and quartiles which show how observed variables differs from the means  and frequency charts and diagrams. Inferential statistics are used to make inferences of the population from the sample. It makes use of sample instead of population to make conclusion about the data and support insights that were made using descriptive statistics. Inferential statistics include probability distribution such as normal and exponential distributions, test of hypothesis such as t-test and analysis of variance (ANOVA), correlation analysis and regression analysis such as multiple and linear regression analysis, (DeSaro,  2011). Also non parametric tests such as rank correlations and Kruskal Wallis test. Inferential statistics are probabilistic in nature thus they are not 100% sure and has margin of error that is significance level, (Tashakkori & Teddlie, 2003).

Variables Description

The study consisted of both independent variables and dependent variables, gender1 which represents the dependent variable while the independent variable was opportunity of a woman to get a promotion or a job opportunity. In SPSS we had gender as nominal variable.

Data analysis and Discussion

Descriptive analysis

The total number of the respondents that participated in the study was one thousand nine hundred and seventy four. Most of the respondents were males who were one thousand and one hundred while there were eight hundred women. In the bar graph below, males are more than women.

Statistical interpretation

The bar graph below shows that most males are in the working force. Those who are working in America that is per study are males since them more in the professional ground as compared to the women. Women are also in the work force but are less as compared to males. Those who are seeking opportunities as well as promotion in the work place is in greater proportion are the males to that of the females.

The frequency of females and females were represented in the table below. 57.3% of the respondents were males while 42.7% were females. Most of the respondents were males as compared as females.

The frequency table below indicates the percentage of the respondents that were if they were in a position to promote a member of staff they would promote a female or a males. Most of the respondents were likely to offer these chance, 14.9% were in a way likely to offer these opportunity to a woman. 67.5% of the respondents tend to think that this is inappropriate since a promotion or opportunity should be due to performance and efficiency and not gender. Opportunity offered due to performance and efficiency then these will increase the output.  

The bar graph below is a graphical representation of woman to get a job or promotion. 

Statistical Interpretation

In the respondents one hundred thirty four which is 6% said that very likely don’t to offer an opportunity, 134 of the respondents think that gender can be equalized in the field. 294 of the respondent gave the opinion that somewhat likely a woman wont be offered an a job or promotion if they were competing for that chance with a male. One hundred and thirty three of the respondents said that somewhat unlikely for a woman getting an opportunity to get the job or a promotion in the place of work. Sixty of the respondents gave very unlikely for a woman to get the offer compared to that of the males.

Most of the respondents in the sample selected which accounted for one thousand and three hundred and fifty three decided that they had no opinion on these since they termed the question as inappropriate.

Statistical interpretation

The frequency table above estimates the gender inequality in the workforce. The table indicates clear depiction of the landscape in the workforce between males and females. 57.3% of the respondents agree with that women have a less opportunity in the workforce as compared to 42.7% don’t agree that women have less opportunity. Most of the employers tend to promote males as compared to the females in the workforce.

The frequency table above shows the relationship what causes the gender inequality in the workforce between the males and females. The factor of consideration at this level was do women and males have the same potentiality in the workforce. What prevents the women the workforce to have a less chance of obtaining good jobs as well as promotion in there places of work. 89% of the respondents believe that females in the work place cannot compete for an opportunity in the workplace compared to the males which is 10% of the respondents.

Inferential analysis

H0: Women are not less likely to get hired for a better paying job or receive a promotion when competing with men

H1: Women are less likely to get hired for a better paying job or receive a promotion when competing with men

A crosstab below shows the likelihood of an occurrence of a female being given a job as well as promotion in the job. Most of the respondents in the table tend to think that the promotion and job creation will favor more males compared to females.

The table below indicates the significance difference between the gender and the opportunity of a woman in the sample selected to be selected for a job or promotion in that task. The p-value which is 0.165 which is less than 0.05 thus we fail to reject the null hypothesis women are not less likely to get hired for a better paying job or receive a promotion when competing with men and thus we conclude that women are less likely to get hired paying job or receive promotion in job market. Despite the test of hypothesis most of the women the job market still do not experience gender inequality.

The analysis above indicates that the gender of the respondents is not in any way related with acquiring of a promotion at work of place as well as getting a job.

Conclusion

  1. The research in the study found that most of the employer or the respondents have the power they would hire male as compared to females since both have equal chances.
  2. Most of the respondents fully disagree the issue of gender inequality in the workforce.

The study recommends that when hiring more members of staff then they should consider females since they are easy to instruct and hardworking in their responsibility. The level of education should be given a priority in the performance as well as hiring job members and promoting them at work place. The education level should highly upraise, since it’s the only way to improve efficiency. The age factor should have a balance in them. 

References

Alderman A. & Salem B. (2010). Survey design. Plastic Reconstruction Surgery, vol 4, pg 9.

Desaro S. (2011). A Students guide to conceptual side of inferential statistics.

Hango D. (2013) Gender differences in science, technology, engineering, mathematics and computer science (STEM) programs at university. Ottawa: Statistics CanadaHttp://www.gss.norc.org (November 17, 2017) 

Kothari, C. (2004). Research Methodology Methods and Techniques New Age International. New Delhi: (P) Limited, Publishers 

Larivière V, Vignola E, Villeneuve C, GeÂlinas P, Gingras Y. (2011) Sex differences in research funding, productivity and impact: an analysis of Quebec university professors. Scientometrics.  87(3): 483±98.

Neuman, W. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th Edition. UK:  Pearson Education Limited

Schuster H, Finkelstein J. (2006) The American Faculty: The Restructuring of Academic Work and Careers. Baltimore: The Johns Hopkins University Press.

Prpi? K. (2002) Gender and productivity differentials in science. Scientometrics. Vol 55(1):  pg 27±58.

Perry, J. & Perry, E. (2014). Contemporary Society: An Introduction to Social Science, 12th Edition. Singapore: Pearson Education, Inc.

Tashakkori & Teddlie C. (2003). Handbook of mixed methods in social & research. Thousands Oaks, CA: Sage.

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