Chapter 2

Other common instruments used to collect data in research are interviews and observation. Interview is a method of data collection that involves two or more people exchanging information through a series of questions and answers (Cameron, J., 2005). On the other hand, observation is a data collection method where the researcher watches people, events or features of the research environment (Delve, 2022)

Data collection

Information pertaining to the significance of different leadership styles (independent variable) as applied in a remote setting will be collected with the aid of questionnaires. The dependent variables for this study will include job satisfaction, motivation, and employee performance as tabulated above. Responses from the questionnaires will be used adequately 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

Validity is described as the extent to which quantitative research measure or instrument accurately assesses what it is to measure (Heale & Twycross, 2015). In this sense, it ascertains that the results computed are applicable, and accurate. For this study, different types of validity will be considered, including external, internal, criterion-related, construct, and content validity. While external validity refers to the extent to which the research findings can be generalized to other populations or settings, internal validity refers to the extent to which the research can establish cause-and-effect relationships between variables (Heale & Twycross, 2015). Additionally, criterion-related validity refers to the relation between the research instrument and the external criteria. According to Heale and Twycross (2015), construct validity refers to the extent to which the research instrument measures the defined construct and the content validity refers to the extent to which the research instrument or measure covers all aspects of the construct. With the consideration of the documented information, the role of validity in this research revolves around ensuring that the research measure and instrument are accurate and attain the desired objective in relation to assessing what is intended to be measured.

Reliability

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). Heale and Twycross (2015) documented that reliability refers to “the extent to which a research instrument or measure produces consistent and stable results over time.” This study will consider inter-rater reliability which refers to the extent to which different persons produce consistent results as well as internal consistency reliability which refers to the extent to which the questions in the questionnaire are related to each other. Reliability of this study can be attained by testing the validity of the instruments used as well as taking measures to minimize the measurement error.

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.

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. However, the participants will be required to have worked remotely for at least 6 months. Also, 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.

The research procedure for this study will entail sample selection, through which participants from remote working settings will be selected. The next procedure will be collecting data on the styles of leadership of different leaders, with the consideration of employee performance, motivation, and job satisfaction. Significant methods that will facilitate data collection include questionnaires and possibly performance evaluation. The most critical part of this study is defined by data analysis, which will make use of statistical methods, including MANOVA as discussed above. This step will provide insight on job satisfaction, motivation, and employee performance as related to various leadership styles.

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. Taking the Belmont Report into account, the study ought to integrate the ethical principles of beneficence, respect for individuals involved, and non-maleficence (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979). It was also critical 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 conducting quantitative research as documented by Belmont Research (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979), through which it was ensured that the participants were treated with upmost respect and beneficence was integrated as well. 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

Limitations refer to factors that may affect the generalizability or external validity of the study whereas delimitations refer to the specific choices made by the researcher in the design of the study (Theofanidis & Fountouki, 2018). 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 work remotely, 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 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).

A one-way MANOVA answered the five research questions regarding what leadership style characteristics does their supervisors or managers fall under when measuring the dependent variables. The level of significance was p < .05, meaning there was a 5% chance that a difference existed in the 5 leadership styles. Also, the current study determined whether a mean difference exists between those five leadership styles as well. Conducting an F-test could provide an overall comparison of whether the means of the five groups of five leadership styles 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

Assumption 1 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

Assumption 2 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

Assumption 3 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

Assumption 4 requires no univariate or multivariate outliers (Statistics, 2015). This assumption is commonly tested in SPSS by following the Explore 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

Assumption 5 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 100 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

Assumption 6 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

Assumption 7 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

Assumption 8 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 100 participants per independent variable level. Assumption eight, demonstrating adequate sample size, was satisfied upfront by using a priori power analysis.

Assumption 9

Assumption 9 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

Assumption 10 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 use correct data, use an alternative test, or proceed with the analysis despite the violation of assumptions.

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 100 participants. To account for attrition, 50% was added to the minimum sample size of 50, which should reflect a total of 100 in the final sample. The researcher utilized SPSS 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

The relevance of this study is associated with its contributions towards facilitating 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. Additionally, the study will facilitate the development of strong bonds between employees and their leaders with the aim of enhancing employee performance, motivation, and job satisfaction.

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.

Appendix H: SurveyMonkey

Appendix I: Job Satisfaction Survey (JSS)

Appendix J: Permission for Job Satisfaction Survey (JSS)