Chapter 4

Purpose of the Study

Provide a brief restatement of the research question and hypotheses.

1. Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 1 Null: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 1 Alternant: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

2. Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 2 Null: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 2 Alternant: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

3. Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

Hypothesis 3 Null: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Hypothesis 3 Alternant: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

IThe researcher prepared a close-ended questionnaire using survey monkey to collect data and responses. Among the four key research questions, I created three individual questions around each research question. The respondents were expected to answer whether they agree or disagree with experiencing a leadership trait and explain their job performance during that month. I summed up the responses that agreed with their productivity and the reactions that disagreed with their productivity to get a value that I would use as a response for the main research question. Comment by WIDNER, ROBERT: Avoid use of first person. See APA manual. Comment by Ameki Williams: Done

The questionnaire tested the respondents on a motivation factor and its influence on motivation and productivity. The responses that indicated the participant did not experience a motivation factor (clients who answers ‘no’ to the questions) were not considered in the study since we were interested only in the presence of the motivation factor (clients who answers ‘yes’ to the questions).

To calculate the correlation coefficient in excel, I used the Correlation function.

Research Question 1

Identify the alternative hypothesis.

The null hypothesis: There are no impacts of transformational management on employee motivation and job performance.

The alternative hypothesis: There are impacts of transformational management on employee motivation and job performance.

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

After conducting a correlation analysis in excel, the following details were found.

The Pearson's correlation value is 0.37, which is not 0If the test concludes that the correlation coefficient is significantly different from zero, we say that correlation coefficient is significant. , With that being said, and we accept the alternative hypothesis that there are impacts of transformational management on employee motivation and job performance. Comment by WIDNER, ROBERT: This is not how you determine if a correlation is significant. https://statistics.laerd.com/spss-tutorials/pearsons-product-moment-correlation-using-spss-statistics.php Comment by Ameki Williams: Corrected

Research Question 2

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

After conducting a correlation analysis in excel, the following details were found.

The Pearson's correlation value is 0.39, which is not 0, and we accept the alternative hypothesis that Rewards, and punishment affect employees' performance, motivation, and job satisfaction.

Research Question 3

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

After conducting a correlation analysis in excel, the following details were found.

The Pearson's correlation value is 0.48, which is not 0, and we accept the alternative hypothesis that Delegation motivates employees and leads to job satisfaction and better performance.

Research Question 4

With regards to Pearson’s R;

H0: R = 0

HA: R! = 0

After conducting a correlation analysis in excel, the following details were found.

The Pearson's correlation value is 0.90, which is not 0, and thus we accept the alternative hypothesis that there are impacts of visionary leaders on motivation, employee performance, and job satisfaction.

While descriptive statistics facilitate 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 would be analyzed by integrating MANOVA (Multivariate Analysis of Variance), through which each of the variables will would be analyzed at a given time (Scheiner, 2020). The use of a 5-Likert scale will would 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 would be measured on a 5-point Likert scale, with number 1 being termed as strongly disagree while number 5 will would be assigned to strongly agree. The center of the scale will would read “neither agree nor disagree”. Additionally, SPSS (Version 27) will would be used in analyzing the data. Comment by WIDNER, ROBERT: Please review your entire document for the use of future tense and change to past tense. Comment by Ameki Williams: Done

Technology will would be instigated to facilitate the procedures of the research, particularly in selecting the sample population. Organizations that have adopted remote working will would be contacted to provide access to their employees. A representative sample of 60 remote workers will would be scheduled to answer the questionnaires. However, the participants will would 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 ensure accurate results will would be collected. The questionnaires will would be disseminated, answered, and submitted online, with strict adherence to a governing set of rules.

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

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 would 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.

The survey instruments used for the study will would be comprised of the informed consent form (see Appendix A), demographic characteristics questions (see Appendix B), and the Multifactor Leadership Questionnaire (MLQ) (see Appendix E), and individual work performance questionnaire (IWPQ) (see Appendix F), and the Job Satisfaction Survey (JSS) (see Appendix I). Data for the survey will would stem from G*Power (see Appendix G). The researcher is the only individual who can access the file as the computer is password protected. Data will would be kept on the computer for five years after the study is completed (University of Virginia, 2022) The statistical software program (SPSS Version 27) will would be used in the research once responses are gathered. The MLQ, IWPQ, and JSS has been found to be a psychometrically strong measure with a Cronbach’s alpha, indicating good internal consistency (O'Connor & Casey, 2015). Test-retest results for the scale indicate good reliability (r=0.797, p<0.001)’ (O'Connor & Casey, 2015). Additionally, scale developers found there to be adequate assessment in the areas of measurement error, content validity, hypotheses testing, and structural validity’ (O'Connor & Casey, 2015).

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 would 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. The 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.

The reliability of the instruments being used is an essential part of the research study. The reliability concept deals with the assessments' ability to be duplicated while the results are trustworthy across different settings (Rollnick et al., 2019). Threats to reliability exist throughout the entire process of research, and researchers need to be proactive and try to minimize these threats to the research as much as possible (McClelland et al., 2015). The instruments' reliability refers to the level at which the collection tool being used can present stable and consistent results. The study's reliability is always sample dependent, so it may vary from study to study (Scollione & Holdan, 2020). The level of reliability determines the overall accuracy of the results (Mohajan, 2017). When a study has high levels of reliability, another researcher should be able to replicate the study and reassess the outcomes (Rutkowski & Delandshere, 2016). This researcher considered several different types of reliability for this study, and each type is uniquely relevant to the situations where measurements are used (Kamper, 2019).

Table 1:

Reliability Statistics

Mean and standard deviation values are presented in Table 1. Descriptive statistics summarize a given dataset, which can either be presented in the form of a sample or population. Here, descriptive statistics are measured through measures of speed or variation and central tendency, including mean values. The mean score for Freedom-Thinking Leadership (M = 2.77, SD = 1.74) did not significantly differ from Servant Leadership (M = .642, SD = 6.378), Participative (Democratic) Leadership (M = 2.481, SD = .7989). However, turnover’s mean was significantly different than Structural Leadership (M = 2.742, SD = .6690).

Table 2:

Descriptive Statistics

Figure 1.

Histogram of mean for Structural Leadership, Participative (Democratic) Leadership, Servant Leadership, Freedom-Thinking Leadership, and Transformational Leadership with Pareto line.

Assumptions Analysis

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.

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.

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.

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.

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 box plots 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).

Figure 2:

Multivariate Boxplot Comment by WIDNER, ROBERT: You have an outlier. Explain to your reader how you dealt with it.

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 60 participants. There are as many Shapiro-Wilks tests as there are groups of independent variables multiplied by the number of dependent variables.

Table 4:

Shapiro-Wilk Test of Normality

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). The threshold for determining whether multicollinearity was present was r > .90 (Crossley, Subtirelu, & Salsbury, 2013). The five independent variables, r (48) = .51, p < .001, two-tailed. Since the five independent variables, see Table 5) in the MANOVA correlations were below the threshold of .90 (Crossley et al., 2013), the sixth assumption was satisfied. A correlation matrix is presented in Table 5. Comment by WIDNER, ROBERT: Take a look at the example chapter I gave you and follow the procedure for determining multicollinearity. https://www.spsstests.com/2015/03/multicollinearity-test-example-using.html Comment by Ameki Williams: Done

Table 5:

Correlation Matrix

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). Comment by WIDNER, ROBERT: Please review these sites for assessing this assumption. https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php https://real-statistics.com/multivariate-statistics/multivariate-analysis-of-variance-manova/manova-assumptions/

Figure 3:

Scatter Plot for Correlation Analysis

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. Assumption eight, demonstrating adequate sample size, was satisfied upfront by using a priori power analysis. When conducting quantitative research, the sample size calculation is based on the researcher's needed effect size, which is the difference between the mean responses of the two groups, the alpha error or false positive error, and the statistical power (Gogtay, 2010). A priori analysis was conducted utilizing G*Power 3.1.9.4 (see Appendix G) software to determine the minimum necessary sample size for this study to achieve significance. Comment by WIDNER, ROBERT: Provide the results form your G*Power. Comment by Ameki Williams: Done

The estimated sample size used for this study was 45 remote workers. 15 percent would be added for possible attrition, and another 15% would be added for possible use of nonparametric tests. Thus 30% totaled would be added to the sample size of 45 to get a sample size of 60. The effect size would be .15, the alpha level would be .5, the power would be .8, the number of groups would be 5, and the number of response dependent variables would be 3.

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).

Table 4:

Box’s Test of Equality of Covariance Matrices Comment by WIDNER, ROBERT: https://www.researchgate.net/post/How-do-I-continue-with-my-analysis-if-the-Boxs-M-and-Levenes-tests-are-significant-in-MANOVA Take a look at some of the answers for the posed question and apply to your paper.

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). If the test is not statistically significant ( p > .05), there is similar variances and the expectation of homogeneity of variances has not been broken (Laerd Statistics, 2015). For Structural leadership, the significance was less than .05 ( p = .015), for the Participative leadership, the significance was less than .05 ( p = .005), for Servant leadership, the significance was less than .05 ( p = .009), for Freedom-thinking leadership, the significance was less than .05 ( p = .001), and for Transformational leadership, the significance was also less than .05 ( p = .091). for the variables of interest as indicated in Table 5, which means that this assumption was violated. Comment by WIDNER, ROBERT: Please review the example paper I sent to you for this. Comment by Ameki Williams: Done Comment by WIDNER, ROBERT: https://www.introspective-mode.org/data-assumption-homogeneity-of-variance-covariance/ Review this to help you with this assumption.

Table 5: Comment by WIDNER, ROBERT: This is out of place. Comment by Ameki Williams: Chart has been moved to correct place.

Levene’s Test: Independent Sample Test

Table 6: Assumption Strategies for One-Way MANOVA

Table 6: Comment by WIDNER, ROBERT: This is out of place. Comment by Ameki Williams: Chart has been moved to correct place.

Levene’s Test: Independent Sample Test

The following research questions guide this quantitative study:

RQ1: Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

There were three research questions for this study and the corresponding hypotheses that were addressed included:

RQ1: Is there a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job performance between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

RQ2: Is there a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in motivation between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.?

RQ3: Is there a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles?

H1o: There is not a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

H1a: There is a statistically significant difference in job satisfaction between remote workers with Structural Leader, Participative Leader, Servant Leader, Freedom-Thinking Leader, and Transformational Leadership styles.

Limitations. The analysis addressed if there was a relationship between the dependent and independent variables, but it did not offer a reason why a relationship is present. Potential limitations are also laid in self-reporting. Ostroff and Kozlowski (1992) noted although self-reports in data are appropriate when interest was in newcomer perceptions, a comparison of newcomers’ perceptions to those of other sources of information may have been fruitful for further understanding the process. When stating newcomer, this means a person that has recently arrived in a place or joined a group (Theofanidis & Fountouki, 2018).

During the data analysis process, there were two identified limitations: (1) assumptions not met and (2) outliers. It is essential to discuss the limitations that emerged during the data analysis and how these limitations may affect the interpretation of the results. The first limitation were assumptions that were not met for the one-way MANOVA, and two-tailed Independent Samples t-test. All three tests had assumption violations in multivariate normality, no univariate or multivariate outliers, homogeneity of variance-covariance matrices, and homogeneity of variances.

Chapter 4 described the data collected including descriptive statistics and data specific to each research question. After the data were analyzed, both the first and second research questions resulted in statistically significant results. Chapter 5 provided a summary of the study, findings, implications, and recommendations for future research.

References