see attachment 2 assignments
Discussion board # 1 Quantitative Data
Please note that there are three posts needed to successfully complete the discussion board assignment. An initial post addressing the discussion board topic is due by end of day Saturday. Two response posts to at least one other student is due by end of day Tuesday.
Initial post: #1
Includes one substantive initial post using at least two scholarly or professional references with accompanying in-text citations to support any paraphrased, summarized, or quoted material.
your initial post should be at least 350 words.
Includes an open-ended, thought-provoking question posed to classmates.
Part I: Provide an example of how t-tests could be used in your potential field of study for your dissertation. Please make sure you address both paired samples and independent samples. Also please discuss the assumptions that need to be met to use this type of analysis. Your EOSA modules discuss this.
Part II: Provide an example of how correlation could be used within your potential field of study for your dissertation. Please make sure you address the purpose of correlation and the type of results you would obtain. Also please discuss the assumptions that need to be met to use this type of analysis. Your EOSA modules discuss this. Clearly identify the variables you are considering.
If no initial posts exist to allow for a response to be made, you may submit an additional initial post addressing another aspect of the unit topic.
Respond to post # 1 (JasL)
Response posts:
Includes at least two substantive responses that each include at least 1 scholarly, professional, or textbook reference with accompanying in-text-citation to support any paraphrased, summarized, or quoted material.
responses should be at least 200 words.
Part I:
When assessing the effectiveness of implementing LEAN principles within the same organization we could collect data on a specific metric, such as production cycle time, from a sample of production lines before the introduction of LEAN principles and then collect data on the same production lines after the implementation of LEAN practices.
Null Hypothesis (H0): There is no significant difference in production cycle time before and after the implementation of LEAN principles.
Alternative Hypothesis (H1): There is a significant difference in production cycle time before and after the implementation of LEAN principles.
Assumptions for the paired samples t-test:
The differences between pairs of observations are independent.
The differences between pairs of observations are normally distributed.
The differences between pairs of observations have a constant variance.
When considering a scenario where we want to compare the production cycle time between two different organizations, one that has fully implemented LEAN principles and another that has not implemented LEAN principles yet.
Null Hypothesis (H0): There is no significant difference in production cycle time between the organization using LEAN principles and the organization not using LEAN principles.
Alternative Hypothesis (H1): There is a significant difference in production cycle time between the organization using LEAN principles and the organization not using LEAN principles.
Assumptions for the independent samples t-test:
The two samples are independent of each other.
Both samples are normally distributed.
Both samples have equal variances (homogeneity of variances assumption), although this assumption can be relaxed if sample sizes are sufficiently large.
In both cases, if the assumptions are met, we can use the appropriate t-test to determine whether there is a statistically significant difference in production cycle time associated with the implementation of LEAN principles
Part II:
Considering an example where correlation analysis is used to study the relationship between the implementation of LEAN principles and the efficiency of production processes in manufacturing organizations.
Variables:
Variable 1 (X): Level of implementation of LEAN principles in manufacturing processes. This could be measured using a scale indicating the extent to which various LEAN tools and techniques (e.g., 5S, Kanban, Value Stream Mapping) are employed within the organization. Higher scores indicate greater implementation of LEAN principles.
Variable 2 (Y): Efficiency of production processes. This could be measured using metrics such as cycle time, lead time, or defects per unit produced. Lower values indicate higher efficiency.
Purpose of Correlation Analysis: The purpose of correlation analysis in this context is to examine whether there is a relationship between the level of implementation of LEAN principles and the efficiency of production processes. Specifically, we want to determine whether higher levels of LEAN implementation are associated with greater efficiency in manufacturing operations.
Results: After collecting data on the level of LEAN implementation and efficiency of production processes from a sample of manufacturing organizations, we can compute the correlation coefficient (typically Pearson's correlation coefficient, denoted by "r") to quantify the strength and direction of the relationship between these variables. The correlation coefficient ranges from -1 to 1, where:
A value close to 1 indicates a strong positive correlation (as X increases, Y increases).
A value close to -1 indicates a strong negative correlation (as X increases, Y decreases).
A value close to 0 indicates little to no linear relationship between the variables.
Assumptions for Correlation Analysis:
Linearity: The relationship between the variables is linear. In other words, when plotted on a scatterplot, the points roughly form a straight line pattern.
Independence: Each data point represents an independent observation.
Normality: The variables X and Y are approximately normally distributed.
Interpretation: If the correlation coefficient is statistically significant (typically assessed using a significance level, such as p < 0.05), we can conclude that there is a significant linear relationship between the level of LEAN implementation and the efficiency of production processes. Additionally, the sign of the correlation coefficient indicates the direction of the relationship (positive or negative), while the magnitude indicates the strength of the relationship.
References:
Fox, J. (2016). Using the R Commander. Taylor & Francis. https://online.vitalsource.com/books/9781498741934
Respond to post # 2 (TchaW)
Response posts:
Includes at least two substantive responses that each include at least 1 scholarly, professional, or textbook reference with accompanying in-text-citation to support any paraphrased, summarized, or quoted material.
responses should be at least 200 words.
Part I: T-tests in Education Research
In the field of education research, t-tests can be valuable for analyzing various aspects of student performance, program effectiveness, or intervention outcomes. Here's an example of how t-tests could be used in education research for a dissertation:
Paired Samples T-test Example:
Let's say we are conducting a study to evaluate the effectiveness of a new teaching method in improving students' math scores. We want to compare the math test scores of students before and after implementing the new teaching method. In this case, we would use a paired samples t-test.
Paired Samples T-test:
Research Question: Is there a significant difference in math test scores before and after implementing the new teaching method?
Variables: The independent variable is the teaching method (before vs. after), and the dependent variable is the math test scores.
Assumptions: The assumptions for conducting a paired samples t-test include:
Normality: The differences between paired observations should be normally distributed.
Independence: The paired observations should be independent of each other.
Interval or Ratio Scale: The dependent variable (math test scores) should be measured on an interval or ratio scale.
Independent Samples T-test Example:
Now, let's consider another scenario where we want to compare the math test scores of students who received the new teaching method with those who received the traditional teaching method. In this case, we would use an independent samples t-test.
Independent-samples T-test:
Research Question: Is there a significant difference in math test scores between students who received the new teaching method and those who received the traditional teaching method?
Variables: The independent variable is the teaching method (new vs. traditional), and the dependent variable is the math test scores.
Assumptions: The assumptions for conducting an independent samples t-test include:
Normality: The math test scores in each group should be normally distributed.
Independence: The two groups should be independent of each other.
Homogeneity of Variances: The variances of the math test scores should be equal across the two groups.
Part II: Correlation Analysis in Education Research
Correlation analysis can be used in education research to examine the relationships between variables such as study habits, student engagement, and academic performance. Here's an example of how correlation analysis could be applied in education research:
Example:
Suppose we're interested in understanding the relationship between students' study habits and their academic performance in a particular subject, let's say mathematics.
CorrelationAnalysis:
Research Question: Is there a significant correlation between students' study hours and their math test scores?
Variables: The variables of interest are:
Independent Variable: Study Hours (measured in hours per week)
Dependent Variable: Math Test Scores
Purpose: The purpose of correlation analysis is to determine the strength and direction of the relationship between two continuous variables.
Assumptions: The assumptions for correlation analysis include:
Linearity: The relationship between the two variables should be linear.
Normality: Both variables should be approximately normally distributed.
Homoscedasticity: The variances of the variables should be equal across all levels of the independent variable.
Independence: The observations should be independent of each other.
Results: After conducting the correlation analysis, we might find a moderate positive correlation between study hours and math test scores (e.g., r = 0.50, p < 0.05). This result would indicate that students who study more tend to achieve higher scores on math tests.
Thought-provoking question for classmates:
How might the results of a correlation study exploring the relationship between social support and resilience influence intervention strategies for individuals coping with chronic illness?
References:
Fox, J. (2016). Using the R Commander. Taylor & Francis.
https://online.vitalsource.com/books/9781498741934
Schoonenboom, J. (2023). The Fundamental Difference Between Qualitative and Quantitative
Data in Mixed Methods Research. Forum: Qualitative Social Research / Qualitative
Sozialforschung, 24(1), 1–24. https://doi-
org.libraryresources.columbiasouthern.edu/10.17169/fqs-24.1.3986
