can you please help me with this assignment

Crete a narratedmultimedia presentation using either Power Point, Screencast-o-matic, or Prezi. Remember,narration with audio (not just ppt notes) is necessary. In essence, your presentations should "play" for us. The presentation should be no more than 5-10 minutes (about 8-10 slides). Be sure to provide some background on the topic, discuss your variables (including frequency tables/charts), include the steps of hypothesis testing, provide figures (crosstabs, measures of association, and tests of significance) and discuss them, and conclude by highlighting how your research fits into the existing body of literature on this topic.

MY WORK ON PRJECT IS AT THE BOTTOM

1. Introduction:Wrte an introduction to your research study in pagraphs. Be sure to include a brief statement of current research on your topic (with an in-text citation of a source), a description of your research topic, why you chose the topic, what you hoped to learn from the topic, your research question, and your broad hypothesis.

Interaction between teachers and students has a substantial impact on classroom dynamics, not only on academic performance, but also on how students interact in the classroom. A growing body of research indicates the importance of good teacher-student interactions for student success (Smith, 2022). The purpose of this study is to analyze the complex interplay between teacher communication techniques, particularly formal communication channels, as well as the dynamics of trust between students as a result. This topic has been chosen due to the growing understanding of the relationship underpinning communication, trust, and overall student well-being. With an awareness of the impact of formal communication on the trust of students, our goal is to provide educators with information to help them improve their teaching techniques in order to foster an inclusive learning community.

Research Question:

What is the relationship between communication between teachers and students and trust between students?

Broad Hypothesis:

We hypothesize that students' trust will be significantly influenced by teachers' formal communication with them. Communicating effectively, including tailored feedback, differentiated instruction techniques, as well as open lines of communication, promotes trust and collaboration within the classroom. In addition to teachers engaging with students on a formal level, it is likely that students will feel empowered, heard, and connected, which will positively influence trust dynamics.

EXPLNATION:

1. Follow formal communication:

In order to examine "formal communication" in relation to teachers' relationships with students, it is suggested taking a multifaceted approach. Three key aspects will be evaluated:

Frequency and Type of Personal Feedback:

• Measurement: Feedback is measured by individual meetings, comments made on assignments, and responses given to students individually.
• Subcategories: There are three types of feedback: additional information (contextualizing), encouragement (congratulations and encouragement for students), and improvement recommendations (feedback aimed at academic enhancement).

Overall, the choice of statistical tests depends on how the data are analyzed, what predictions are possible to make, and whether robustness is required due to possible deviations from the norm or outliers. It is ensured that each questionnaire is customized to the data collected and study objectives, allowing for a robust and accurate study of formal communication and trust.

1. Literature Review:Wrte a 3-5 pagraph lit review (review of studies on your topic) using more than three sources, at least two of them being research studies in peer-reviewed journal articles.

Developing an effective learning environment requires effective communication between educators and students, as well as building confidence in students. The purpose of this study is to examine the relationship among tailored communication and mutual trust. From a variety of perspectives, researchers consistently emphasize the relevance of personalized learning.

According to Smith et al. (Year), an investigation was conducted to determine the influence of continuous and individual remarks on student performance as well as self-confidence. According to the study, there is a positive relationship between tailored responses that increase self-esteem, providing evidence that tailoring interactions are beneficial in terms of learning support.

Furthermore, Johnson and Brown (Year) explored the effectiveness of differentiated instruction for boosting students' self-esteem. Research was conducted in order to identify appropriate methods of teaching that are tailored to individual learning profiles. In this study, results revealed how differentiated instruction positively influenced students' self-esteem, highlighting the importance of adjusting communication in order to accommodate individual students.

1. Methods/Dataset: In one pagraph, describe the GSS dataset.

The General Social Survey (GSS) dataset is utilized in this study to explore the influence of effective communication between teachers and students on student confidence in a learning environment. The GSS dataset is a comprehensive collection of sociological data gathered through surveys conducted in the United States. It covers various topics such as demographics, social attitudes, and educational experiences, making it a suitable resource for investigating the relationship between communication in educational settings and student confidence.

Research Question:

The primary research question seeks to understand the connection between communication among teachers and students and the level of trust among students.

Hypothesis Test:

The null hypothesis (H0) posits that there is no relationship between communication skills and student confidence, while the alternative hypothesis Ha) proposes that tailored communication positively impacts student trust, resulting in increased confidence compared to a control group with less communication. The significance level is set at 0.05. The choice of statistical tests includes the Mann-Whitney U test for two groups and the Kruskal-Wallis test for three or more groups.

Evaluation Process:

Define Variables:

Independent Variable (IV): Customized communication, measured through the frequency of personalized feedback, use of diverse teaching methods, and open communication channels. Dependent Variable (DV): Student confidence, assessed via self-assessment (e.g., self-efficacy) and performance-based measurements (e.g., test scores, class participation). Research and Null Hypothesis:

Research Hypothesis (H1): Students exposed to effective communication will demonstrate higher confidence than those in the control group with limited communication. Null Hypothesis (H0): No significant difference in student confidence between the communication group and the control group. Alpha Level:

The chosen alpha level is 0.05, indicating that if the p-value is less than 0.05, the null hypothesis will be rejected in favor of the research hypothesis, supporting the positive impact of customized communication on student confidence. Select the Appropriate Test:

For two groups (e.g., communication and control): Mann-Whitney U test. For three or more groups (e.g., different communication variables): Kruskal-Wallis test. Consideration of measurement levels: Appropriate tests for ordinal and interval data, ensuring compatibility with the dataset characteristics. Evaluate the Results:

Interpret the p-value: p < 0.05: Reject the null hypothesis, indicating a significant relationship between communication and student confidence. p >= 0.05: Fail to reject the null hypothesis, suggesting insufficient evidence to conclude that customized communication has a significant impact on student confidence. Beyond Statistics:

Emphasize the importance of interpreting effect sizes (e.g., Cohen's d) rather than relying solely on p-values for a more comprehensive understanding of the relationship between communication and student confidence.

1. Methods/Variables: In one pagraph, discuss why you chose the specific independent and dependent variables for your analyses. Include the names of the variables, the level of measurement, the questions asked in the survey, and the response choices for each.

In this study, the aim is to investigate how customized communication, defined as personalized interactions catering to individual needs, impacts student confidence in a learning environment. The study focuses on various dimensions of customized communication, including the frequency and type of personal feedback, differentiated instruction based on learning styles, and the availability of open communication channels. The dependent variable, student confidence, is measured through self-assessment (e.g., self-efficacy) and performance-based metrics (e.g., improvements in test scores or class participation).

Research Question and Hypotheses: The primary research question revolves around the relationship between communication between teachers and students and trust between students. The null hypothesis (H0) posits that there is no significant relationship between communication skills and student confidence, while the alternative hypothesis (Ha) suggests that students exposed to tailored communication will exhibit higher confidence than those in the control group with less communication. The chosen alpha level is 0.05, indicating a 5% significance level.

Significance Test: For the significance test, the study considers the Mann-Whitney U test for scenarios involving two groups (e.g., customized communication and control) and the Kruskal-Wallis test for situations with three or more groups (e.g., different levels of communication). These non-parametric tests are selected because they are appropriate for ordinal data and are robust against outliers, providing a reliable analysis of the impact of customized communication on student confidence.

Refining the Research Hypothesis: The research hypothesis is refined by specifying the aspects of tailored communication being investigated. For example, if the focus is on one-on-one meetings, the hypothesis could be further refined to state that "students who engage in weekly one-on-one meetings with their teachers will demonstrate improved individual study performance compared to students in the control group without such meetings."

Addressing Potential Variables: Various potential variables that could influence communication and student confidence are identified. These include past learning experiences, class size, and teacher characteristics. To control for these variables, strategies such as random participant assignment, grouping based on relevant characteristics, and inclusion of potential confounding variables as covariates in statistical analyses are employed.

Choice of Statistical Tests: The choice of statistical tests, including the Mann-Whitney U test and Kruskal-Wallis test, is explained based on the nature of the data and the research design.

The tests are selected for their suitability with ordinal data and their robustness against outliers. Additionally, the mention of the independent samples t-test for data over time, such as test scores, highlights the consideration of different test types based on the characteristics of the data.

Interpreting Results: In interpreting p values, the study provides a nuanced understanding of different ranges:

p < 0.05: Reject the null hypothesis, indicating a significant relationship between communication and student confidence. p >= 0.05: Fail to reject the null hypothesis, suggesting insufficient evidence to conclude that tailored communication has a significant impact on student confidence. Beyond Statistics: The conclusion emphasizes the importance of not solely relying on p values and encourages researchers to interpret effect sizes (e.g., Cohen's d) to measure the magnitude of the relationship between communication and student confidence. This broader perspective helps to understand the practical significance of the results beyond statistical significance.

By incorporating these detailed explanations, your study is strengthened by providing a comprehensive overview of the research design, variables, hypotheses, statistical tests, and interpretation of results in the context of investigating the impact of tailored communication on student confidence in a learning environment.

1. Methods/Variable information:Copy and paste your frequency tables and descriptive statistics for each variable. Include your charts as well (histogram, bar, or pie). Provide a summary/explanation of what these tables/charts tell us about your variables:

Bar Chart for Customized Communication Customized Communication Levels -------------------------------- | | No Communication | Moderate Communication | High Communication | |---------|-------------------|------------------------|---------------------| | Count | 10 | 20 | 30 | --------------------------------

Variable 1: Customized Communication

Frequency Table:

• Categories: Lack of communication, Moderate communication, High communication
• Each category is counted by the number of participants.

Descriptive Statistics:

• Mean: Communication level on average.
• Standard Deviation: An indicator of communication level variability.

Chart:

• Bar chart illustrating the level of communication.

Variable 2: Confidence among students

Frequency Table:

• Categories: unconfident, moderately confident, and confident
• Each category is counted by the number of participants.

Descriptive Statistics:

• Mean: Student confidence level on average.
• Standard Deviation: Confidence level variability measurement.

Chart:

• Students' confidence levels are shown in a bar chart.

Summary/Explanation

Customized Communication:

• Communications with a higher mean and lower standard deviation are generally more customized.
• An analysis of the bar chart shows if communication levels differ significantly among participants.

Student Confidence:

• A higher mean and a lower standard deviation indicate that students are more confident on average.
• It is easy to compare confidence levels between categories using a bar chart that illustrates the distribution of confidence levels.

Additional Considerations

Correlation Analysis:

• Students' confidence and customized communication are correlated using a correlation coefficient.

Regression Analysis:

• Analyzing regression models to investigate the extent to which the level of customized communication can explain student confidence variability.

1. Findings: Copy & Paste corrected analyses from Weeks 3-6, i.e., Crosstabs, Measures of Association, Tests of Significance. Include the five steps of hypothesis testing.

MY WORK BELOW IS FROM WEEK 3-6

WEEK 3

The question I want to answer is: How does study time impact students' exam scores?

• Independent Variable (IV): Study time
• Dependent Variable (DV): Exam scores

The IV (study time) is what we manipulate or measure to understand its effect on the DV (exam scores).

The data collected is presented in the table below. The independent variable (IV) is study time, and the dependent variable (DV) is exam scores.

The data collected is presented in the table below. The independent variable (IV) is study time, and the dependent variable (DV) is exam scores.

Student	Study Time (hours)	Exam Score
1	3	65
2	5	78
3	2	60
4	6	85
5	4	72
6	1	50
7	7	90
8	5	75
9	4	70
10	3	68

Calculation of Epsilons:

Epsilon is determined by subtracting the mean of a variable from each individual data point. The calculation is as follows:

Calculate the Mean for Study Time and Exam Scores:

• Mean Study Time = (3 + 5 + 2 + 6 + 4 + 1 + 7 + 5 + 4 + 3) / 10 = 4
• Mean Exam Score = (65 + 78 + 60 + 85 + 72 + 50 + 90 + 75 + 70 + 68) / 10 = 69.3

Calculate Epsilons:

• Epsilon for Study Time (for each student) = Study Time of Student - Mean Study Time
• Epsilon for Exam Scores (for each student) = Exam Score of Student - Mean Exam Score

Student	Study Time Epsilon	Exam Score Epsilon
1	-1	-4.3
2	1	8.7
3	-2	-9.3
4	2	15.7
5	0	2.7
6	-3	-19.3
7	3	20.7
8	1	5.7
9	0	0.7
10	-1	-1.3

Discussion of the 10% Rule:

The principle of the 10% rule in statistical analysis states that if the largest absolute epsilon value is smaller than 10% of the range of the independent variable, then multicollinearity is not a significant concern. Put simply, if the individual data points are not excessively extreme compared to the overall spread of the data, multicollinearity is not a major issue.

For instance, if the range of study time is 7 (ranging from 1 to 7), then 10% of that range would be 0.7. If the largest absolute study time epsilon is less than 0.7, the 10% rule is met. Similarly, if the range of exam scores is 40 (ranging from 50 to 90), then 10% of that range would be 4. If the largest absolute exam score is less than 4, the 10% rule is satisfied.

WEEK 4

State the Research Hypotheses

The hypothesis are:

Null Hypothesis:Ho:1=2

Alternative Hypothesis:H1:1=2

Identify the Level of Significance

It has a significance level of0.01.

Decision and Conclusion

The p-value of 0.105 isgreater thanalpha value of 0.01, thus do not reject the null hypothesis.

The conclusion is:

The null hypothesis should not be rejected at a significance level of 0.01 (p-value > 0.01). Employees who participated in counseling sessions had higher job satisfaction scores than those who did not. However, there is not enough evidence to conclude that the scores differed statistically from those of non-participants. Consequently, we cannot recommend the counselling because it is ineffective.

Explanation:

As indicated in the instruction, we will conduct an Independent Samples T-Test to determine if the job satisfaction scores of employees who participated in counseling sessions differed statistically from those of employees who did not participate.

State the Research Hypotheses

Null hypotheses assert that the two samples are not different. Therefore, the equal symbol is always present in this hypothesis. Alternatives, however, assume that the samples differ. As a hypothesis, this tells us what we need to test or what the claim is. In the problem, it states whether the two scores are statistically different.In other words, difference does not indicate how one is superior or inferior to another. There will always be a not equal symbol next to the alternative when this is the case.

For the test of mean, the population parameter symbol is. We will let1for those who participated in counselling and2for those who did not. In light of these factors, the hypothesis is as follows:

Null Hypothesis:Ho:1=2

Alternative Hypothesis:H1:1=2

WEEK5

Cronbach's coefficient alpha values for the student-professor interaction scale, academic motivation subscales, and academic self concept scale.

How do they describe their research methods?

According the researchers, they gathered 242 undergraduate students out of a class of 950 students enrolled in an introductory psychology course from a mid-size, Midwestern, public university where only first-year students are required to live in residence halls, as respondents for the study. They were selected from about 2000 freshmen students who enrolled that year. The study was conducted in small group sessions of about 10 participants each, across the semester, with the respondents accomplishing surveys and the researchers gathering the statistics including age, gender, major, year level and ethnicity.

Which variables do they analyze and why?

The correlation between alpha values corresponding to each subscale in interaction, academic motivation, self concept and the students' GPA. The researchers believe that these factors are major indicators to the students' academic performance.

What are their hypotheses?

1. The faculty member-student relations strongly affects students' learning performance, as there is a support-seeking dimension in these relationships that when nurtured, helps develop positive outcomes for students.

2. Strong faculty member-student relations increases the satisfaction of students with academic life, decreases the likelihood of dropping out and boosts the students' intellectual drive.

What statistical analyses do they use?

Correlation and regression analyses.

Explain why discussing the data and methods is important to establish the validity and reliability of the research.

Elaborate discussion and accurate interpretation of data and research methods used offers a strong evidence to the researcher's hypothesis, which is the objective of the research itself.

Discuss any critiques you have of the Methods/Data section of the article.

The use of alpha coefficient values is arbitrary and may or may not be applicable to all cases.

Komarraju, M., Musulkin, S., & Bhattacharya, G. (2010). Role of student-faculty interactions in developing college students' academic self-concept, motivation, and achievement. Journal of college student development, 51(3), 332-342.

Reference PDF

https://www.researchgate.net/profile/Meera-Komarraju-2/publication/236712875_Role_of_Student-Faculty_Interactions_in_Developing_College_Students%27_Academic_Self-Concept_Motivation_and_Achievement/links/54aae14d0cf2ce2df668cd7d/Role-of-Student-Faculty-Interactions-in-Developing-College-Students-Academic-Self-Concept-Motivation-and-Achievement.pdf

WEEK 6

This study investigates the impact of effective communication between teachers and students on student confidence in a learning environment. Customized communication refers to personalized interactions that meet the individual's needs, education, and concerns. We believe this communication cat45es a supportive environment, increases students' confidence, and improves their learning.

Research question: What is the relationship between communication between teachers and students and trust between students?

Hypothesis test: Null hypothesis (H0): There is no relationship between communication skills and student confidence. Alternative hypothesis (Ha): There is a relationship between. Tailored communication and student trust means that students exposed to it will have more confidence than students in the control group with less communication. Alpha Level: 0.05 Significance Test: It may be tentative or at least provisional depending on the difference (communication and trust between students), the appropriate test would be: < br>Mann -Whitney U test: if there are only two groups (e.g. customized communication and control). Kruskal-Wallis Test: If there are three or more groups (e.g. different levels of communication).

1. Follow formal communication: Defining "formal communication" as an individual distinction is too broad. We need to decide how to evaluate its location and subject matter difference. Here are some practical uses: Frequency and type of personal feedback: Increase the value of feedback, such as one-on-one meetings, written comments on tasks, or individual responses to questions. Feedback is further divided by content (clarification, support, improvement suggestions). Differential Instruction: Identifies specific strategies to be used based on student learning (e.g. visual aids, activities, differentiation). Open communication channels: Evaluate the availability and use of channels such as office hours, email, online forums, or time for student issues. 2. Refining the Research Hypothesis: Improving your research hypothesis to make it more specific to your chosen use. For example, if you focus on one-on-one meetings, your hypothesis might be: "Students who meet weekly with their teachers will demonstrate better individual study performance than students in the control group." No. Meeting. 3. Addressing potential variables: Identify variables that could affect communication and student confidence that could affect your results. For example: Learning from the past: Successful students will try to communicate more and their current self-confidence will influence your actions in a positive way. Class Size: Smaller classes can encourage more personal interaction. Teacher Self: Although learning is structured, some teachers will communicate better. Control these variables as follows: Assign participants randomly to groups (experimental and control groups). Start grouping by relevant characteristics such as past performance. Inclusion of confounding variables for covariates in statistical analyses. 4. Exam Selection: More than the appropriate exam. Explain the reason behind choosing them: Mann-Whitney U-test: Non-parametric if there are two groups, suitable for normal data and robust for outliers. Kruskal-Wallis Test: If there are three or more groups, it is non-parametric, has normal data control and is suitable for comparison of different levels of communication. Independent samples t-test: If data over time (such as test scores) meet the assumption of normality, parametric tests will be more powerful than nonparametric tests. 5. Interpret p values: Explore the nuances of different p value ranges: p <0.001: Very strong evidence for a significant effect. 0.001 < 0.05: Strong evidence of significant effect. 0.05 < 0.10: Weak evidence of a significant effect requires replication or further study. p >= 0.10: No significant result, but it goes without saying that customized communication is ineffective. 6. Beyond Statistics: Do not publish P values. Interpret effect sizes (e.g., Cohen's d) to measure the magnitude of the relationship between communication and student confidence.

Explanation:

Detailed Description of the Evaluation Process to Customize Communication and Student Confidence Building on my previous response, here is a more detailed description of the evaluation process measures for your work: 1. Define variables: Arguments (IV): customized communication. This can be used in a variety of ways, such as: The frequency with which teachers provide personalized feedback. Use different teaching methods that suit students' learning styles. Provide students with an open means of communication to solve problems. Use different teaching methods that suit students' learning styles. Provide students with an open means of communication to solve problems. Dependent variable (DV): student confidence. This can be measured by: Self-Assessment (e.g. self-efficacy, self-efficacy). Performance-based measurement (e.g., improvement in test scores, participation in class discussions). 2. Research and Null Hypothesis: Research Hypothesis (H1): Compared to students in the control group who received less communication, students who received communication skills will be more confident. Null Hypothesis (H0): There is no significant difference in terms of student confidence between the communication group and the control group. 3. Alpha Level: The alpha level you choose is 0.05; This means that if the p-value of the test is less than 0.05, you will reject the null hypothesis and conclude that the customized communication is effective. positive effects: student confidence. 4. Select the appropriate test: The appropriate test depends on the characteristics of the data: Multiple groups: Two groups (e.g., communication and management): Mann-Whitney Test. Three or more groups (e.g. different communication variables): Kruskal-Wallis test. Measurement Levels: Ordinal data (such as Likert scale scores): These tests are appropriate to the data set. Interval data (such as test scores): These tests can also be used with interval data, although more powerful tests (such as the independent samples t-test) may be appropriate even if the data meet the assumption of normality.

5. Evaluate the results: After performing the selection test you will receive the p value. Here's how to interpret it: p < 0.05: You reject the null hypothesis. There is a significant relationship between communication and student confidence, supporting your research hypothesis. p >= 0.05: You reject the null hypothesis. These data do not provide sufficient evidence to conclude that therapeutic communication has an impact on student confidence.

1. Findings: Explain what the analyses displayed above tell us about the relationships between the variables in the study.

Cronbach's coefficient alpha values: These values indicate the accuracy or reliability of the scales measuring student-faculty interaction, motivation for learning, and self-perception. A higher value indicates a higher level of reliability, with values ranging from 0 to 1. According to the question, all of the values are above 0.7, which suggests acceptable reliability for the scales.

In correlation analyses, the strength and direction of the linear relationship between two variables are measured. There is a range of -1 to 1, with 0 indicating no relationship and positive or negative values indicating a positive or negative relationship, respectively. Students' academic self-concept, academic motivation, and academic achievement are positively correlated with student-faculty interaction, as reported in the question. As a result, students with more frequent and positive interactions with their faculty tend to have a higher self-concept, motivation, and achievement level.

Analyses of regression: These measure the extent to which one variable (the dependent variable) can be predicted by another variable (the independent variable) while controlling for other variables (the covariates). Even after controlling for gender, ethnicity, and prior GPA, student-faculty interaction is a significant predictor of academic self-concept, academic motivation, and academic achievement. In this way, student-faculty interaction affects students' academic outcomes independently and positively, regardless of other demographic and academic factors.

1. Conclusions: Explain how the findings contribute to the study of your topic. What did you learn? What other variables need to be explored? What further research on your topic could be pursued in the future?

Week 3:

Contribution to Study:The epsilon calculations and application of the 10% rule contribute to understanding multicollinearity in statistical analysis. The focus on study time and exam scores provides a foundational understanding of the variables' relationship.

Learnings:Through this study, you've gained insights into the importance of considering multicollinearity when analyzing data. The epsilon calculations highlight the impact of individual data points on statistical outcomes.

Future Exploration:

• Study Techniques:Investigate different study techniques and their impact on exam scores. This could include qualitative data on the methods students use to study.
• External Factors:Explore external factors like stress, personal commitments, or health that may influence both study time and exam performance.

Week 4:

Contribution to Study:The exploration of job satisfaction scores and counseling participation contributes to understanding the effectiveness of counseling sessions in enhancing job satisfaction.

Learnings:While the null hypothesis was not rejected, indicating no statistical difference, the study highlights the need to consider other variables influencing job satisfaction.

Future Exploration:

• Counseling Intensity:Investigate the impact of the frequency and duration of counseling sessions on job satisfaction.
• Employee Characteristics:Explore how individual characteristics (e.g., personality, prior experiences) may influence the effectiveness of counseling.

Week 5:

Contribution to Study:The study on student-faculty interactions provides insights into the correlation between these interactions and academic outcomes.

Learnings:The study suggests that positive faculty-student relations have a significant impact on academic performance, satisfaction, and student engagement.

Future Exploration:

• Different Interaction Types:Investigate how various types of interactions (mentorship, collaborative projects) influence academic outcomes.
• Faculty Characteristics:Explore how the characteristics of faculty members impact student perceptions and academic success.

Week 6:

Contribution to Study:The proposed study on effective communication and student confidence contributes to understanding the potential link between tailored communication and student outcomes.

Learnings:The hypothesis sets the stage for exploring the relationship between teacher-student communication and student confidence.

Future Exploration:

• Communication Channels:Investigate the impact of different communication channels (e.g., online platforms, face-to-face interactions) on student confidence.
• Cultural Factors:Explore how cultural backgrounds may influence the perception of effective communication.

References:Include full APA citations of all sources used. List them in alphabetical order by the author's last name. Use hanging indentation (Line spacing options - Indention - Special - Hanging). Don't forget to cite the GSS dataset (citation is listed in Week 1 Lessons).

Barker, A. (2019, August 22). The Importance of Communication Skills [Top 10 Studies].Retrieved January 22, 2024, from https://www.goconqr.com/en-US/p/16607332-The-Importance-of-Communication-Skills-Top-10-Studies-mind_maps

Kumar, S. (2018). The Importance of Effective Communication: Some Food for Thought.Journal of Applied Laboratory Medicine, 2(6), 891-892. doi: 10.1373/jalm.2017.025627

McCabe, C., & Timmins, F. (2013). Communication Skills: A Literature Review and Conceptual Framework for Practice.Journal of Psychiatric and Mental Health Nursing, 20(6), 469-477. doi: 10.1111/jpm.12039