Our consultants examined leadership behavior and organizational learning by using NVivo 10. The common theme was leadership behavior and organizational learning. In addition, three primary subthemes appeared from the analysis: (a) encouragement, (b) helpfulness, and (c) motivation.
Our consultants examined how the organization responds to unexpected circumstances. The organization’s response to unforeseen circumstances was the common theme. In addition, three primary subthemes appeared from the analysis: (a) organizational objectives, (b) improvement, and (c) performance.
Our analysts used a qualitative method and a phenomenological design to explore employees’ lived workplace experiences. We conducted a thematic analysis using NVivo to examine the research question. The common theme was the lived experiences in employees’ workplaces. In addition, we found two primary subthemes: (a) employee engagement and (b) employee job performance.
We investigated leadership behavior and organizational innovation. We performed a thematic analysis using NVivo 10 to examine the research question. The common theme was leadership behavior and follower behavior. In addition, three primary subthemes appeared from the study: (a) motivation, (b) skills, and (c) work.
We examined leadership and business success. The common theme was that leadership leads to the business’s success. In addition, three primary subthemes appeared from the analysis: engagement, self-esteem, and motivation.
Dr. Eun Um’s company has been operational for 18 years, actively interacting with clients from different cultures and identities. AMSTAT Consulting has been successful because of its inherent team of competent and intelligent professionals, most of whom have doctorates from reputable education entities such as Harvard, Stanford, and Columbia. Since its inception, the company has sought to capitalize on its solid background and expand its acquaintance network. Dr. Um knows […]
A simple linear regression analysis is a statistical method that helps to predict the value of a dependent variable based on the value of an independent variable. It assesses the linear relationship between two continuous variables and provides insights into the relationship’s direction, magnitude, and statistical significance.
For instance, you can use simple linear regression to predict the sales of a product based on the advertising spend (i.e., your dependent variable would be “sales” and your independent variable would be “advertising spend”). You could also determine how much of the variation in sales can be explained by advertising spend. Similarly, you could use linear regression to predict the weight of a person based on their height (i.e., your dependent variable would be “weight” and your independent variable would be “height”). You could also determine how much of the variation in weight can be attributed to the person’s height.
Note that simple linear regression is also known as bivariate linear regression, and the dependent variable is also referred to as the outcome, target, or criterion variable. At the same time, the independent variable is also called the predictor, explanatory, or regressor […]
If you aim to investigate whether there are any statistically significant distinctions in the means of two or more distinct groups, you can employ a one-way analysis of variance (ANOVA). For instance, consider a situation where you wish to determine if there are variations in the performance of athletes in a track event based on their preferred running surface (i.e., your dependent variable would be “race performance,” measured in seconds, and your independent variable would be “running surface,” comprising three groups: “grass track,” “cinder track,” and “synthetic track”). Alternatively, a one-way ANOVA could be used to explore whether there are differences in customer satisfaction scores across different service channels (e.g., in-person, phone, online), where your dependent variable would be “satisfaction score,” and your independent variable would be “service channel,” encompassing multiple groups.
It’s important to note that the one-way ANOVA is also known as a between-subjects ANOVA or one-factor ANOVA. While it can technically be applied to an independent variable with only two groups, the independent-samples t-test is preferred in such cases. Hence, the one-way ANOVA is commonly described as a test used when you have three or […]
The independent-samples t-test is used to determine if a difference exists between the means of two independent groups on a continuous dependent variable. More specifically, it will let you determine whether the difference between these two groups is statistically significant. This test is also known by a number of different names, including the independent t-test, independent-measures t-test, between-subjects t-test, unpaired t-test, and Student’s t-test.
For example, you could use the independent-samples t-test to determine whether (mean) salaries, measured in US dollars, differed between males and females (i.e., your dependent variable would be “salary” and your independent variable would be “gender”, which has two groups: “males” and “females”). You could also use an independent-samples t-test to determine whether (mean) reaction time, measured in milliseconds, differed in under 21-year-olds versus those 21 years old and over (i.e., your dependent variable would be “reaction time” and your independent variable would be “age group”, split into two groups: “under 21-year-olds” and “21 years old and over”).
In order to run an independent-samples t-test, there are six assumptions that need to be considered. The first three assumptions relate to your choice of study design and the measurements you […]