Millstone 3: Chapter 4

11

Chapter 3

Amekí Williams

South University

Doctoral Dissertation Preparation

Dr. Widner

1/16/2023

The Role of Leadership Styles on Employee Performance, Motivation, and Job Satisfaction in a Remote Setting

Chapter 3: Methodology

Introduction

The purpose of this quantitative causal-comparative study was for remote workers to identify what leadership style their supervisors or managers are. The chapter included an overview of the research design and rationale, study participants, sampling method and instrumentation, data collection, analysis, and ethical considerations taken in the design.

Chapter 3 contains a descriptive discussion of the conduct of this study, and how it informed the problem. The detailed explanation supports future design replication, data collection, and analysis. The description of the population and sample ensured that the reader could understand the research subjects. The Multivariate Analysis of Variance (MANOVA) data collection tool allowed valid and reliable data collection. As described, data analysis procedures, followed ethical practices. The chapters discussion on limitations and delimitations expands the discussion in chapter 1.

Research Design

Quantitative Causal Comparative Design

Based on the application of this design in establishing the cause-effect connection between variables (independent and dependent) (Bloomfield, & Fisher, 2019), this quantitative casual comparative study is objectified to establish the significance of various leadership styles on employee performance, motivation, and job satisfaction in a remote setting. It is without a doubt that working remotely has been continuously adapted, particularly after the onset of the COVID-19 pandemic. In correlation to this, it is paramount to have an understanding of the aspects of remote working and what it entails in terms of productivity. As Bloomfield, J., & Fisher, M. J. (2019) establish, a quantitative casual comparative study supports the comparison of two variables. As such, this study’s selected design will facilitate the comparison of five essential levels of leadership styles commonly associated with remote working in relation to job satisfaction, motivation, and employee satisfaction.

With the aid of questionnaires, this study will examine a series of questions, including; 1) whether structural leader rewards and punishes team members based on performance insist on clear goals experiment, 2) whether servant leader listens empathy awareness, 3) whether participative (democratic) leader are open-minded and encourage effective communication, 4) whether freedom-thinking leader give employees freedom to perform and stays out of the way as well as comments and helps when needed, and 5) whether transformational leader inspires and empowers strong role models. These questions govern this study’s research.

Given the nature of the study, the independent variables entail the leadership styles, including structural leadership, participative leadership, servant leadership, freedom-thinking leadership, and transformational leadership (Alheet, Adwan, Areiqat, Zamil, & Saleh, 2021). The dependent variables to be discussed in this section are performance, motivation, and satisfaction. With remote working being the mantra in most organizations globally, this study will make significant contributions towards revolutionizing and enhancing productivity in this type of setting. For applicable results, the sample size in this study was 100 remote workers. With the application of Multivariate Analysis of Variance (MANOVA), statistical analysis will be integrated to compute the results acquired from the questionnaires, through which the research questions will be adequately addressed.

5 levels of leadership styles

Structural leader

RQ1: Do structural leaders reward and punish team members based on performance insist on clear goals experiment?

H1o: structural leadership is not a statistical prognosticator for employee performance among remote workers.

H1a: structural leadership is a statistical prognosticator for employee performance among remote workers.

Participative leader

RQ2: Are participative (democratic) leaders open-minded and encourage communication?

H1o: participative leadership is not a statistical prognosticator for motivation, communication, and job satisfaction among remote workers.

H1a: participative leadership is a statistical prognosticator for motivation, communication, and job satisfaction among remote workers.

Servant Leader

RQ3: Do servant leaders listen and practice empathy awareness?

H1o: servant leadership is not a statistical prognosticator for empathy awareness among remote workers.

H1a: servant leadership is a statistical prognosticator for empathy awareness among remote workers.

Freedom- Thinking Leader

RQ4: Do freedom-thinking leaders give employees freedom to perform, stays out of the way, as well as comment and help when needed?

H1o: freedom-thinking leadership is not a statistical prognosticator for enhanced employee performance among remote workers.

H1a: freedom-thinking leadership is a statistical prognosticator for enhanced employee performance among remote workers.

Transformational Leader

RQ5: Do transformational leaders inspire and empower strong role models?

H1o: transformational leadership is not a statistical prognosticator for employee motivation and shaping role models to be emulated among remote workers.

H1a: transformational leadership is a statistical prognosticator for employee motivation and shaping role models to be emulated among remote workers.

Table 1

Variables

Variables	Definition	Operational definition	Measurement Level	Data source/ Instruments
Leadership styles (independent)	The leaders’ methods and approaches when governing others	Structural, participative, servant, freedom-thinking, or transformational	Nominal	Response from the questionnaires
Performance (dependent)	The productivity of the employees	The level employees collaborate to attain the set organizational objectives and goals	Ordinal scale	Graphic rating
Motivation (dependent)	The motivation level exposed on behalf of the employees	The drive promoting enhanced performance	Ordinal scale	Observable responses
Satisfaction (dependent)	The satisfaction of the employees with their jobs	The function of the positive perceived emotion in close relation to contentment of employees.	Ordinal scale	Generic work station scale

Population and Sample

Remote employees

The population will comprise of employees. The target population will be remote workers that work closely with their supervisors, and managers. The study sample will be employees from organizations where strategic management will be studied. The unit of analysis is the impact of leadership styles on management. Selection will be done using the simple sampling technique. 

following sampling formula:

n=(z^(2 ) xρ ̂(1-ρ ̂  ))/∈^2

             

Where:

z = is the Z score

∈= is the margin of error

N = is the population

ρ ̂ = is the population proportion

     The type of sample the researcher is using is the sample size. The sample size used for this study was 100 remote workers. The only factor that disqualified workers from participating in this study was being traditional workers. The selected workers answered the questionnaires and it was established that their leader adopted different leadership styles. For more than half of the population, their supervisors showed relatively high levels of motivation, performance, and satisfaction. Each participant will be informed of the research objectives and fill out consent forms (see Appendix A) before participating in the study. Data collected will be kept confidential by the researcher for 5 years. There will also be an age range of the participants from 18 to 64 years of age .

Instrumentation

Data collection

The information required for this quantitative casual comparative research will be collected with the aid of questionnaires as the primary instrument, which will be structured to garner the significance of different leadership styles (independent variable) as applied in a remote setting. The dependent variables for this study will include job satisfaction, motivation, and employee performance as tabulated above. Additionally, to measure the different levels performance and job satisfaction among the employees, graphic rating and generic work station scale will be used respectively. Observable responses and responses from the questionnaires will also be used as adequate instruments for the collection of data. The validity and reliability of the instruments used for data collection are vital as they will shape the results of the study (Heale & Twycross, 2015). Validity will ascertain that the results computed are applicable, and accurate. On the other hand, reliability is significantly intertwined with how trustworthy the attained results are and its application in the study to eliminate possible errors and threats (Heale & Twycross, 2015).

Research Procedures

Technology will be instigated to facilitate the procedures of the research, particularly in selecting the sample population. Organizations that have adopted remote working will be contacted to provide access to their employees. A representative sample of 100 remote workers will be scheduled to answer the questionnaires. The willingness of the employees to take part in the study was significant as it would ascertain accurate results will be collected. The questionnaires will be disseminated, answered, and submitted online, with strict adherence to a governing set of rules.

Protection of Human Rights

The selected sample population will be required to be willing to provide honest and unbiased information. They are also subjected to have an understanding of what the study entails and what the data collected will be used for. The population will be assured that their information will be protected and used only for the purpose of the study. It was also for the interest of this study that the involved companies remained anonymous to eradicate any form of possible opinion bias and scrutiny. In correlation to this, confidentiality and anonymity will be highly integrated throughout the study.

Ethics

This study adhered to the ethical guidelines for quantitative research, through which it was ensured that the participants were treated with upmost respect. Additionally, the vital parameters of confidentiality, credibility, confirmability, and transferability were used to stipulate an enhanced research process. These parameters ensured that the study valued the relevance of moral principles and ethics. In addition to this, the ethical standards of this study played a central role in the data processing and associated procedures.

Delimitations and Limitations

While the selected research design will facilitate the attainment of applicable results, it is associated with various limitations and delimitations that will be discussed in this section. In consideration of the sample population, the length of remote working experience was a limitation of interest. Without sufficient experience, the study could yield undesirable results. Another limitation is tied down to the problem statement in the sense that only remote workers were considered. It would be of importance if traditional workers would participate in the study as it will facilitate a usable comparison of the various leadership styles utilized.

Assumptions, Risk, and Biases

For starters, it was hoped that the participants will provide accurate results that would not contaminate the collected information. Despite involving the organizations that have adopted remote working, there is a risk that some participants contacted don’t have relative experience as remote workers. It is also notable that a significant population use remote working as a way of earning extra income, and as such, could pose a threat to the results of the study. The only bias associated with this study is attributed to the limit of only using remote workers as the sample population of choice.

Data Analysis: Multivariate Analysis of Variance (MANOVA) 

While descriptive statistics facilitates the completion of different variables of a study, inferential analysis supports the investigation of the relationship between dependent and independent variables. In correlation to this, it is without a doubt that these analysis tools are of significance to this study. With the aid of these tools, the data collected will be analyzed by integrating MANOVA (Multivariate Analysis of Variance), through which each of the variables will be analyzed at a given time (Scheiner, 2020). The use of a 5-Likert scale will play a critical role in the collection of data since it supports the assignment of numeric values to the leadership questions in the questionnaire. In this same context, the dependent variables will be measured on a 5-point Likert scale, with number 1 being termed as strongly disagree while number 5 will be assigned to strongly agree. The center of the scale will read “neither agree nor disagree”. Additionally, SPSS was used in analyzing the data.

Data Assumptions

Once the data were cleaned, data screening was conducted to assess the underlying assumptions. SPSS was used to evaluate the assumptions of normality, homogeneity of variance-covariance matrices, linearity, and multicollinearity (Tabachnick & Fidell, 2013).

Descriptive and inferential statistics were used to analyze each research question to determine if assumptions were met. The research questions address potential differences between multiples dependent variables; therefore, a one-way multivariate analysis of variance (MANOVA) will be utilized to analyze (French et al., 2008). Descriptive statistics are used to summarize the data and inferential statistics are used to test the hypotheses. MANOVA also allows for a more accurate and comprehensive picture of the phenomena being studied by the researcher (Allen, 2017). Finally, measuring the multiple response variables together will provide more chances at discovering the factor that is central to the investigation (Allen, 2017). The one-way MANOVA, a parametric statistical test, will be run to perform inferential statistical analyses and indicate how likely the current study results could be replicated for an entire population (Fraenkel & Wallen, 2009).

This current study examined the independent variable, 5 levels of leadership styles, and the dependent variables, if and to what extent does a difference exist in knowledge of professional help available between African American clergy who completed at least a four-year bachelor’s degree and those who have not completed a least a four-year bachelor’s degree. If and to what extent does a difference exist in knowledge of risk factors and causes between African American clergy who completed at least a four-year bachelor’s degree and those who have not completed a least a four-year bachelor’s degree. If and to what extent does a difference exist in knowledge of professional help available between African American clergy who completed at least a four-year bachelor’s degree and those who have not completed a least a four-year bachelor’s degree? Since this current study involves three dependent variables, the researcher determined that a one-way MANOVA would be the most appropriate statistical test to use since it can determine whether there were any differences between independent groups on more than one continuous dependent variable (French et al., 2008).

A one-way MANOVA answered the three research questions regarding whether there was a statistically significant difference between the two comparison groups of clergies when measuring the dependent variables simultaneously. The level of significance was p < .05, meaning there was a 5% chance that a difference existed in the two groups of clergies when there was not an actual difference. Also, the current study determined whether a mean difference exists between the two groups of clergies. Conducting an F-test could provide an overall comparison of whether the means of the two groups of clergies differ. If the obtained F is larger than the critical F, the null hypotheses is rejected (Gravetter & Larry, 2016). The one-way MANOVA creates a linear combination of the three dependent variables to generate a grand mean and determine if there were group differences in the dependent variables.

Parametric tests, like one-way MANOVA, are appropriate when the data reveal a normal distribution. A one-way MANOVA test shows whether equal variances and normal score distributions are present (Field, 2013). Therefore, an essential requirement to use a one-way MANOVA test is that the assumptions of normality be met. Normality ensures scores are typically distributed, which would be indicated by a bell-shaped curve (Field, 2013). If normality assumptions are not met, a Mann-Whitney test may be used. The Mann-Whitney U is a non-parametric inferential test that can be used to analyze ranked data when the data are not normally distributed (Fraenkel & Wallen, 2009).

In order to run a one-way MANOVA, ten assumptions needed to be addressed one at a time to ensure the sample could be analyzed using this test, which consisted of (1) two or more dependent variables on a continuous level, (2) one independent variable has two or more categorical, independent groups, (3) independence of observation, (4) no univariate or multivariate outliers, (5) multivariate normality, (6) no multicollinearity, (7) linear relationship between dependent variable for each independent group, (8) adequate sample size, (9) homogeneity of variance-covariance matrices, and (10) homogeneity of variances (Statistics, 2015).

Assumption 1

The first assumption requires two or more dependent variables measured at the continuous level (Statistics, 2015). Assumption one was satisfied for the study, as there are three dependent variables measured on a Likert-type scale, which is commonly accepted to be continuous in the field of the social sciences.

Assumption 2

The second assumption requires one independent variable with two or more categorical, independent levels (Statistics, 2015). The term level is typically reserved for groups that have an order (Statistics, 2015). The study has one independent variable (Leadership Styles) with five levels (structural leader, participative leader, servant leader, freedom-thinking, leader, and transformational leader). The second assumption was satisfied.

Assumption 3

The third assumption requires independence of observation where there is no relationship between the participants in any of the groups. Having different participants in each group is a way to address this assumption (Statistics, 2015). Assumption three was met as the data had different participants in each of the three groups.

Assumption 4

The fourth assumption requires no univariate or multivariate outliers (Statistics, 2015). This assumption is commonly tested in SPSS by following the Explore AI procedures then visually analyzing boxplots to detect outliers. Any data that are more than 1.5 box-lengths from the edge of their box are classified by SPSS as outliers and are noted by circular icons, and data more than three box-lengths away are noted by an asterisk. Although this is not a foolproof method, it is the more straightforward approach (Statistics, 2015).

Assumption 5

The fifth assumption requires multivariate normality, which means normally distributed data for each of the groups in the independent variable is expected (Statistics, 2015). This assumption is commonly tested by utilizing the Shapiro-Wilks test for normality in SPSS by following the seven-step Explore procedure. This test is commonly utilized if the sample size is less than 50 participants. There are as many Shapiro-Wilks tests as there are groups of the independent variable multiplied by the number of dependent variables.

Assumption 6

The sixth assumption requires that there be no multicollinearity, which means that the dependent variables should be reasonably correlated with each other (Statistics, 2015). If the correlations are too high (greater than 0.9), there is risk for multicollinearity, which is problematic for a MANOVA (Statistics, 2015). Utilizing the Bivariate procedure in SPSS, Pearson correlations between the dependent variables are analyzed to determine correlation between the variables (Statistics, 2015).

Assumption 7

The seventh assumption requires a linear association between the dependent variables for each group of independent variables (Statistics, 2015). A scatterplot matrix for each group of the independent variables identifies if there is linear relationship (a straight line) or not (a curved line). If the variables are not linearly related, then there is a loss of ability to identify differences (Statistics, 2015). In SPSS, after splitting the data file to separate out the independent levels, the Chart Builder procedure was utilized to assess linearity through scatterplot (Statistics, 2015).

Assumption 8

The eighth assumption requires a sufficient sample size. Laerd (2018) stated that the larger the sample size the better, but at a minimum, there need to be as many participants in each group of the independent variable as there are number of dependent variables. GCU required a minimum of 50 participants per independent variable level. Assumption eight, demonstrating adequate sample size, was satisfied upfront by using a priori power analysis.

Assumption 9

The ninth assumption requires homogeneity (similar or comparable) of variance-covariance matrices (matrix of all possible pairs of variables) (Statistics, 2015). After un-splitting the file, the assumption could be tested by utilizing Box’s M test of equality of covariance in SPSS. The important row is the significance level (p-value) of the Box’s M test. If the test is not statistically significant (i.e., p > .001), there is homogeneity of variance-covariance matrices and no assumptions are violated (Statistics, 2015).

Assumption 10

The tenth assumption requires homogeneity (same) of variances. Assuming the assumption of homogeneity of variance-covariance matrices were not violated, a Levene’s test of equality of variances procedure in SPSS is run (Statistics, 2015). The one-way MANOVA assumes that there are equal variances between the groups of the independent variable. The important column is the Sig. which represents the significance level (p-value) of the test. If the test is not statistically significant (greater than .05), there are equal variances and the assumption of homogeneity of variances has not been violated (Statistics, 2015).

Meeting assumptions is a requirement for obtaining accurate results when using a one-way MANOVA as seen below in Table 2; however, it is common for data to violate one or more of these assumptions. When data violate assumptions, the researcher must either correct data, use an alternative test, or proceed with the analysis despite the violation of assumptions.

Table 2. Assumption Strategies for One-Way MANOVA

Assumption	Test	Alternate Fail Procedure
1. Two or more continuous DVs	Design feature	Change design or analysis
2. Two or more categorical IVs	Design feature	Change design or analysis
3. Independence of observations	Design feature	Change design or analysis
4. No univariate or multivariate outliers	Review SPSS box plots; Mahalanobis distance test	Verify data entry or measurement errors; keep and transform or evaluate effect by running one- way MANOVA with and without outliers, or remove
5. Normality of DV distribution or multivariate normality	Shapiro-Wilk test	Transform DVs, run one-way MANOVA; or keep as one-way MANOVA is somewhat robust to normality deviations
6. DVs moderately correlated	Pearson correlation coefficient test between DVs	If low correlation, use multiple one-way ANOVAs. If high correlation, remove DV with high correlation or combine scores for new DV
7. A linear relationship between each pair of DVs for each IV group	Scatterplot matrix	Transform one or more DVs; remove non-linear DV, or keep and accept a loss of power
8. Adequate sample size	Minimum in each IV group as the number of DVs	Increase sample size
9. Homogeneity of variances	Box’s test of Equality of Covariance Matrices	Proceed if equal samples of IVs. If unequal sample sizes, transform or keep and use Pillai’s Trace instead of Wilk’s Lambda
10. Homogeneity of variance- covariance matrices	Levene’s Test of Equality of Error Variances test	Transform to equalize variances or continue and accept lower statistical significance and run different post-hoc tests

To determine the appropriate sample sizes, the researcher used G*Power software. MANOVA is used by the researcher to deduce a relationship between independent and dependent variables using a smaller sample, which can be generalized to a larger population (Allen, 2017). Based upon the number of variables, the minimum sample size for the one-way MANOVA was 28 participants. To account for attrition, 30% was added to the minimum sample size, which should reflect a total of 36 in the final sample. The researcher utilized SPSS version 26 to clean the data by identifying any missing values. Missing data in quantitative research can lead to loss of important information, increase for standard errors, weaken generalization of findings, and reduce statistical power (Dong & Peng, 2013).

Significance of the Study

It is without a doubt that this study is of critical importance as it will facilitate an understanding of the different leadership styles and the variables of job satisfaction, motivation, and employee performance in a remote setting. The study will explore the impact of structural, servant, freedom-thinking, participative, and transformational leadership styles on the productivity and performance of employees. Taking the attained results into account, the study will provide evidenced results establishing the most productive leadership style.

Summary

This quantitative casual comparative study was purposed to determine the relationship between independent and dependent variables by establishing role of leadership styles on employee performance, motivation, and job satisfaction in a remote setting. With the aid of questionnaires, significant information will be collected from a sample size of 100 remote workers. A quantitative methodology will be integrated to scrutinize and analyze the data collected, forming the basis of this third chapter. In correlation with the limitation attributed to the sample population, various challenges were associated with the study. Regardless, the ethical standards in association facilitated the attainment of dependable results. With an understanding of the methodology to be incorporated, the subsequent chapter will cover the vital aspects of data collection and analysis.

References

Alheet, A., Adwan, A., Areiqat, A., Zamil, A., & Saleh, M. (2021). The effect of leadership styles on employees’ innovative work behavior.  Management Science Letters,  11(1), 239-246.

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design.  Journal of the Australasian Rehabilitation Nurses Association,  22(2), 27-30.

Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies.  Evidence-based nursing,  18(3), 66-67.

Scheiner, S. M. (2020). MANOVA: multiple response variables and multispecies interactions. In  Design and analysis of ecological experiments (pp. 94-112). Chapman and Hall/CRC.