Quantitative analysis

Quantitative analysis

Introduction

The quantitative analysis presents an emphasis on more structured data, which preset a better focus on the outcomes based on descriptive and inferential statistics that help improve efficiency and overall results in research. The quantitative analysis aims at understanding the behavior of a given dataset based on a given research problem. The study using this approach focuses on presenting mathematical and statistical modeling to effectively determine effective outcomes that are generated within a given confidence level. It focuses on presenting reality using numerical data (Pandis, 2016). the quantitative analysis utilizes inferential statistical which concentrate on different statistical tests which are conducted based on an understanding of the underlying assumptions as well as the existing research problem. Research questions play a significant role in guiding the use of statistical software that is used. The analysis, in this case, will emphasize two research questions that be assessed and ensure that there is an appropriate statistical test that can help obtain accurate results.

Making conclusions in statistics can only be done through the integration of a better approach that helps promote change adoption of essential changes that define specific statistical issues. Inferential statistics present a highly structured system that can be adequately assessed and improve performance and determination of better research outcomes (Keith, 2019).  the main research questions that form the basis of the analysis are two. Each of the research questions will be analyzed differently, considering that they are assessing different things that help in shaping the research outcomes.

Research question 1: Is there a difference in job satisfaction between people from different geographical locations?

Inferential statistical technique

The appropriate statistical test, in this case, is a one-way analysis of variance. Analysis of variance presents an emphasis on the real difference between means of dependent categorical data. The study offers an understanding of a given research problem that is being assessed. This means that there is a need to ensure that the underlying statistical test is appropriate based on an assessment of the existing assumptions.  Analysis of variance is a statistical test that provides a comparison between the means of groups (Krzywinski & Altman, 2014). On-way analysis of variance incorporates a single dependent variable.  This is a statistical inferential test that provides a greater focus and understanding under which it is vital to create an understanding of whether there exists a statistically significant difference between groups that are being investigated.

The research objective

Research objective provides the basis within which it is easier to determine the different approaches that can be considered in understanding research outcomes. The structure of the research question helps outline the inferential test that is conducted. The goal of the analysis was to determine whether individuals in different geographical locations have different job satisfaction levels.

Hypothesis

Null hypothesis (Ho): There is no statistically significant difference in job satisfaction between people from different geographical locations.

The alternative hypothesis (Ha): There is a statistically significant difference in job satisfaction between people from different geographical locations.

The level of significance is assessed at 0.05.

The choice of inferential statistics

The choice of one-way analysis of variance was the most appropriate statistical test in this context, considering the basis of the research question and the objective of the research. The research aimed at determining whether there is a difference in the level of job satisfaction based on the geographical location of individuals. Conducting an analysis of variance requires that there are underlying assumptions that need to be met (King, 2010). The basic assumption involves the choice of variables and their type. The dependent variable must be continuous and measured on either interval or ratio scale. The independent variable must be categorical variable and measured on either nominal or ordinal scale. Therefore, when assessing the variables that have been included in the analysis, the dependent variable is job satisfaction, which is a continuous variable measured on a ratio scale (Bewick, Cheek, & Ball, 2004). The independent variable is a geographical location, which is a categorical variable measured on a nominal scale. The variable included three categories.

Descriptive statistics

Geographical location

The analysis of variance shows that more than half, 55% of the respondents in the study were from the Greater London region, 24% were from the Rest of South East while 21% were from Central London.

Job satisfaction

The analysis of job satisfaction data across the dataset showed that the results are likely to follow a linear distribution considering that majority of the data values are found in the middle of the normal curve. The analysis shows that the mean job satisfaction level was 4.93 (SD = 1.162).

The analysis focused on determining the average job satisfaction in each of the three geographical regions that were assessed in the study. The mean across the three groups shows that there is a difference. Greater London region had a higher mean of 5.145 (SD = 1.01) followed by Central London with a mean of 4.86 (SD = 1.21) and the Rest of the South East with a mean of 4.52 (SD= 1.33). However, the difference in the means does not statistically mean that we can conclude that there is a statistically significant difference in job satisfaction across the three geographical locations.

The one-way analysis of variance was conducted to determine whether there was a statistically significant difference in job satisfaction between people from different geographical locations. The findings showed that at 95% confidence level, F (2,183) = 4.772, p = 0.010, p<0.05). Thus, we reject the null hypothesis and conclude that there is a statistically significant difference in job satisfaction between people from different geographical locations. Thus, it is essential to conduct a post hoc test to determine the difference between each of the independent groups (Judd et al., 2018).

A Tukey HSD post hoc analysis was conducted to determine the groups that were different. The findings showed that there was a significant difference in mean job satisfaction between Greater London people and the Rest of southeast (p =0.007). There was no significant difference between Central London and Greater London, as well as no significant difference in job satisfaction between central London and Greater London.

Research question 2: Which is a stronger predictor of psychological ill-health: job satisfaction or neuroticism?

Inferential statistical technique

Determining predictors in research is based on model development, which helps present an understanding of better measures that can help promote improved outcomes. Thus, based on the study research question, the inferential statistical test would be multiple regression analysis. Regression analysis presents a predictive relationship between independent and dependent variables. Multiple regression analysis provides an understanding of a linear relationship where there are more than two independent variables that predict a given dependent variable (Angelini, 2018).

Research objective

The study sought to determine the predictors of psychological ill-health with emphasis on Job satisfaction and neuroticism.

Hypothesis

Null hypothesis

Ho1: Neuroticism does not significantly predict psychological ill-health.

Ho2: Job satisfaction does not predict substantially psychological ill-health.

Alternative hypothesis

Ha1: Neuroticism significantly predicts psychological ill-health.

Ha2: Job satisfaction significantly predicts psychological ill-health.

The level of significance that provides critical information regarding the research is 0.05. This is essential in improving efficiency in helping improve research outcomes.

Choosing the appropriate test

Understanding whether the statistical test that was conducted requires an assessment of specific factors that present a structured system that emphasizes on the successful application of multiple regression analysis. Multiple regression analysis aims at understanding the determinants or factors that predict a dependent variable that is being assessed in the study (López, Fabrizio, & Plencovich, 2016). Thus, multiple regression evaluates different variables in order to determine whether all of them or specific variables have an influence on the dependent variable. Multiple regression is the most useful test based on the variables that have been included in the analysis, as well as the research question (Afifi et al., 2019). The research question is particular in asking about which of the variables, neuroticism, and job satisfaction significantly predict psychological ill-health. Thus the independent variables that are assessed are two variables that are measured on a ratio scale. The dependent variable is psychological ill-health.

Descriptive statistical analysis

Job satisfaction

The analysis shows that the data follows a normal distribution. Most of the data occur within the middle level. The average job satisfaction was 4.93 (SD = 1.162), which shows that there is a better development of the findings while also taking into different focus systems that present a better knowledge of the findings.

Neuroticism assessment shows that the data is normally distributed since the data is well spread out, with the majority of the data occurring in the middle of the normal curve. The average neuroticism was 3.85 (SD = 1.2).

Psychological ill-health

The data analysis, as shown in the histogram, the data is normally distributed. The average in psychological ill-health was 11.15 (SD = 5). Effective identification of the findings presents a greater emphasis on better approaches that help in shaping an understanding of the fact that conducting multiple regression analysis requires that the dependent variable assessed in the study must be normally distributed.

Inferential analysis

Determining predictive elements within a research process is based on a clear and compelling understanding where it would be possible to achieve a higher level of knowledge on the existing relationship between the variables that are being investigated (Wang, Rosner, & Goodman, 2016). This is because it is the most appropriate statistical test that would provide evident and effective results that can be based upon in making conclusions regarding psychological ill-health.

The model summary that has been developed in this case focused on understanding the explanation of the existing relationship between variables. The correlation coefficient (r) is 0.227, which shows that there is a weak positive relationship between the variables included in the study and the dependent variable. The coefficient of determination r² = 0.052. This shows that 5.2% of psychological ill-health is explained by job satisfaction and neuroticism.  This means that 94.8% of the psychological ill-health is explained by other factors that have not been included in the study.

The analysis of variance shows significant results, which means that the model is a significant predictor of the dependent variable. Psychological ill health can be effectively predicted by job satisfaction and neuroticism; hence it is vital to assess the influence of each of the variables included in the analysis based on coefficients of the outcomes.

The analysis of the coefficients which provide an understanding of the regression analysis showed that only neuroticism (p = 0.017) was a statistically significant predictor of psychological ill-health. Job satisfaction based on the study does not predict psychological ill-health.

Therefore the regression model that best describes the relationship between the variables is

Where x is the neuroticism level while y is the psychological ill-health. This means that an increase in one unit in neuroticism will lead to a 0.725 increase in psychological ill-health, which is essential in main policy development regarding better approaches that can be considered in effectively attaining improved outcomes.