Recruiting subjects for a research project

Recruiting subjects for a research project to determine the best leadership style

To choose the best leadership for a group, first, we need to understand the weakness and strengths of the leader's role in establishing friendly ground rules and a well-structured group. Suppose the researcher does not know what kind of strong leadership is the goal. In that case, they could not be able to identify the solid questionaries for the participants, as well as recruit participants. For example, giving power to the group member is a strength, or building an individual's responsibility inside the group is a strength. But, the most popular decision-making for leadership is not a strength, or rapid decision-making is not a strength.

Another piece of information a researcher needs before registering to participate is the facilitative style. The facilitative style for leadership is a tool that "encourages the leader to show empathy in their interactions with those under them; this means listening freely without judgment or criticism of any kind of verbal and non-verbal actions of followers or subordinates." (Carter, 2022)

The facilitative style gives a fantastic opportunity and space to create new ideas to explore and space for learning and empowering. However, the researcher should also be aware of the facilitative style's weakness. For example, the facilitative style (or conductive style) could be a tool to make the group aimless and chaotic. Even the facilitative style could make a shallow to the group. (Choosing change, 2022, p. 4) 

Lastly, situational leadership is an important element that researchers should consider before attempting to recruit and register. The situational leadership approach is "performance as situation-specific, requiring leaders to assess performance based on a specific task and work climate. Leaders should frequently reassess the performance of individuals to continue meeting their needs, in terms of both direction and support." (Situational, 2022)

"Why must nonparametric analysis be used to analyze the data obtained from the surveys?" (CTU, 2022)

The nonparametric analysis is usually used when the data is not normal or, let's say, we do not have normalized distributed data.

A not normalized distribution usually is skewed left or skewed to the right and may not represent the real distribution of data through the group. The best example of these kind of data was our unit 4 individual project.

"What must this researcher know about the construction process of a survey that would ensure a valid, reliable assessment instrument?" (CTU, 2022)

Making a model for cognitive processes that could engage with the respondent is necessary for the researchers. The best constructive process of a survey starts with question interpretation and continues with information retrieval, judgment formation, response formatting, and response editing. (Chiang, 2015, p. 2)

"What must this young researcher understand about selecting a target group for the administration of the survey?" (CTU, 2022)

Yount researchers, first of all, should choose an advisor for selecting a target group. However, young researchers should generally be selective, have a plan B, measure what to do, and know the group's nature and characteristics.

Reference

Carter, L. (2022, February 16). Is Facilitative Leadership An Underrated Leadership Style? Louis Carter. Retrieved 2022, from https://louiscarter.com/facilitative-leadership-style/

Chiang, I. A. (2015, October 13). Constructing Survey Questionnaires – Research Methods in Psychology – 2nd Canadian Edition. Pressbooks. Retrieved 2022, from https://opentextbc.ca/researchmethods/chapter/constructing-survey-questionnaires/

Choosing change, (2022). Leadership Styles Questionnaire. Retrieved 2022, from https://www.centenary.edu/files/resources/leadership-styles-questionnaire-descriptions.pdf

Cleave, P. (2022, January 25). 5 Tips To Consider When Selecting Your Target Audience. SmartSurvey. Retrieved 2022, from https://www.smartsurvey.co.uk/blog/5-tips-to-consider-when-selecting-your-target-audience

Situational, (2022). Situational leadership. Retrieved 2022, from https://www.situational.com/content/uploads/2017/10/FINAL_CLS_History_CaseStudy_Digital.pdf

Parametric analysis

Parametric analysis is the data collected by production engineers with well modeling and decision-making techniques. (OspreyData Marketing. 2021). A non-parametric analysis does not mean that our engineer does not know about the population, but it means that the collected data does not have a normal distribution. In case of not normal distributed data, we conduct those tests made for non-parametric statistics such as Wilcoxon signed-rank test or others that we explain here in more detail.

T-Test

The t-test is an inferential statistical analysis used to study two different groups. Mathematically, a t-test addresses the problem by assuming that the means of the two groups' distributions are equal with a null hypothesis of (H0: u1=u2). The t-test will show a high probability of differences in these two groups when rejected (H0: u1=u2). 
More importantly, a t-test is conducted for a group of 20 to a maximum of 30 people; otherwise, it is not suggested. However, other tests can maximize the analysis accuracy for more than 30 people in groups. (Fernandez, 2021)  
The t-test could be used for a parametric analysis to draw out the inference about data from different groups; however, the engineer failed to comply with this article's parametric analysis data collection, so we can not use the t-test for that purpose.

F-Test

The f-test or f-distribution or ANOVA is used when at least three groups of samples exist to analyze. F-test conducts for a parametric analysis to get the results of the equality of groups' variances. F-test is called a "parametric test" also because of the "presence of parameters in the F- test. These parameters in the F-test are the mean and variance." (Lani, 2021) Therefore, our engineer of this article can not use the f-test because of failing to comply with the parametric test.

Chi-square test

The Chi-square test (distribution-free test) is especially non-parametric analysis statics, and it is used for data when the level of data is nominal or ordinal. Therefore, this test fits our analysis in this article when our engineer's data collection is not complying with parametric statistics.

Mann–Whitney U test

The Mann–Whitney U test is a non-parametric data analysis tool for the independent samples and groups. This test compares the means of different and separate groups of data. So, Mann–the Whitney U test is also a good technique for our engineer to conduct the analysis.

Wilcoxon signed-rank test

The Wilcoxon signed-rank test is a non-parametric data analysis and is the best fit for our analysis, and we can use this test for our collection of data in this article.

Kruskal–Wallis test

The Kruskal–Wallis test is an extended version of the Wilcoxon signed-rank test and a one-way analysis of variance (ANOVA). Kruskal–Wallis test is a non-parametric analysis, so that we can use this test for our purpose also.

Reference

1. Fernandez, J. (2021, December 14). The statistical analysis t-test is explained for beginners and experts. Medium. Retrieved 2022, from https://towardsdatascience.com/the-statistical-analysis-t-test-explained-for-beginners-and-experts-fd0e358bbb62

2. Lani, J. (2021, July 27). F-test. Statistics Solutions. Retrieved 2022, from https://www.statisticssolutions.com/f-test/

3. OspreyData Marketing. (2021, June 14). What is Parametric Analysis? OspreyData. Retrieved 2022, from https://ospreydata.com/what-is-parametric-analysis/

Quantitative research questions

Quantitative research questions have a complete answer for a scientific research study, and it has three types, (1) Descriptive, (2) Comparative,  (3) and Relationship-based research questions. (Bl.Uk, 2022) 

Descriptive research questions are used to get the responder's answers to a specifically designed question. This is the easiest way to quantify a specific variable on a larger scale. For example, how often do you buy an iPhone for upgrading reasons? The variable is the number of upgrades bought, and the demographic is iPhone upgrading enthusiasts. Comparative research questions find differences between at least two groups with at least one variable. For example, What is the difference in daily exercises between men and women in California? The variable, in this case, would be a daily exercise, and the demographic is men based in CA vs. women based in CA. Relationship-based research questions are to find "trends, casual relationships, or associations between two or more variables." (Jose, 2022). For example, what is the relationship between job satisfaction and wages in California's residents? The dependent variable in this is job satisfaction, the independent variable is wages, and the demographics are Californians. However, quantitative research questions and research hypotheses are different and are not identical in how they accomplish the reports and outcomes. "In quantitative studies, investigators use quantitative research questions and hypotheses, and sometimes objectives, to shape and specifically focus the purpose of the study." (Creswell, 2022, p. 132)

Differences between research questions and hypotheses

Quantitative research questions are used to quantify the relationship between variables, while the Quantitative Hypothesis, on the other hand, is the researcher's predictions about the expected group's variable relationships. (Creswell, 2022, p. 132) Therefore, research questions are concise and focused with a clear path based on data sets. At the same time, the hypotheses are formally designed, including some predictions and the relationship of some group's variables. Using a form of scripts for hypothesis research questions is essential to finding the proper variables and their relationships and predictions, which we will talk about in the next section. 

Critical information for quantitative research questions and hypotheses

The crucial information for a Quantitative research question is identifying the variables used to measure, manipulate, and control. Another essential information to obtain in a Quantitative research question is identifying the variable we intend to measure, manipulate, and control. (Dissertation, 2020, p. 2) The crucial information for a Quantitative Hypothesis question is finding the relationship between the numbers and final statistical numbers and finding variables and groups. To find the variables in the Hypothesis, we need to identify independent variables (ordinal) and dependent variables (scale).

Examples  

1- The best script for a Quantitative research question can be: 
"Does _________ (name the theory) explain the relationship between_________ (independent variable) and _________ (dependent vari-able), controlling for the effects of _________(control variable)?" (Creswell, 2022)

2- The best script for a Quantitative Null Hypothisis research questions can be: 

"There is no significant difference between _________ (the control and experimental groups on the independent variable) on _________(dependent variable)." (Creswell, 2022)

3- Here is a Null Hypothesis example: 

"An investigator might examine three types of reinforcement for children with autism: verbal cues, a reward, and no reinforcement. The investigator collects behavioral measures assessing the children's social interaction with their siblings. A null hypothesis might read, There is no significant difference between the effects of verbal cues, rewards, and no reinforcement in terms of social interaction for children with autism and their siblings." (Creswell, 2022)

4- Here is an example of a Directional Hypothesis:

"The differences between types of ownership(state-owned, publicly traded, and private) of firms in the offshore drilling industry. Specifically, the study explored such differences as domestic market dominance, international presence, and customer orientation. The study was a controlled field study using quasi-experimental procedures. Hypothesis 1: Publicly traded firms will have higher growth rates than privately held firms. Hypothesis 2: Publicly traded enterprises will have a larger international scope than state-owned and privately held firms. Hypothesis 3: State-owned firms will have a greater domestic market share than publicly traded or privately held firms. Hypothesis 4: Publicly traded firms will have broader product lines than state-owned and privately held firms. Hypothesis 5: State-owned firms are more likely to have state-owned enterprises as customers overseas. Hypothesis 6: State-owned firms will have higher customer-base stability than privately held firms. Hypothesis 7: In less visible contexts, publicly-traded firms will employ more advanced technology than state-owned and privately held firms." (Mascarenhas, 1989, pp. 585–588)

Reference

Bl.Uk. (2022). 429 Too Many Requests. Bl.Uk. Retrieved 2022, from https://www.bl.uk/business-and-ip-centre/articles/what-are-typical-quantitative-research-questions

Creswell. (2022). Research Questions and Hypotheses. sagepub.com. Retrieved 2022, from https://www.sagepub.com/sites/default/files/upm-binaries/22782_Chapter_7.pdf

Dissertation. (2020). Research Questions and Hypotheses | Lærd Dissertation. Dissertation.Laerd.Com. Retrieved 2022, from https://dissertation.laerd.com/quantitative-research-questions-what-do-I-have-to-think-about.php

Jose, J. (2022, April 21). Quantitative Research Questions Examples. Voxco. Retrieved 2022, from https://www.voxco.com/blog/quantitative-research-questions-examples/

Literature review

 A literature review is a "written document that presents a logically argued case founded on a comprehensive understanding of the current state of knowledge about a topic of study." (Anthony, et., 2012) The literature review needs a systematic identification, location, and analysis of the research study documents. (Slideplayer, 2015)

Fraenkel and Wallen identified six general steps for a literature review: "1. Define the research problem as precisely as possible, 2. Look at relevant secondary sources, 3. Select and peruse one or two appropriate general reference works, 4. Formulate search terms (keywords or phrases) pertinent to the problem or question of interest, 5. Search the general references for relevant primary sources, 6. Obtain and read relevant primary sources, and note and summarize key points in the sources." (Fraenkel, 2006, p. 68) There are four methods for conducting a literature review: "Narrative Review, Descriptive Review, Vote Counting, and Meta-Analysis," and we will compare them in a Qualitative method vs. a Quantitative method. 

Qualitative literature review vs. Quantitative literature review

Meta-analysis is the most rigorous literature review method used in quantitative research for a systematic review to synthesize and summarize the results. The meta-analysis method is powerful and entirely objective for evaluating research findings.

On the other hand, the narrative literature review is the most common and useful method to get the most perspectives from the research, and it is mostly used in a Qualitative research method.

In between Meta-analysis, and Narrative literature methods, we have Vote counting and Descriptive review in terms of using them in a Quantitative model and Qualitative. In other words, there are few criteria that we can compare literature review between these two methods including:

1- Data set collection and analysis: in Quantitative research, we use Meta-analysis that focuses on data, reflected by the operationalization of variables, the magnitude of effect sizes, and the sample size, whereas data set is a study of the whole, not variables in a Qualitative literature method. (Rumrill, 2001)

2- Roles of researcher's judgments and effects: there are standardized procedures, fewer judgments, and less subjective in Meta-analysis (Quantitative research), whereas researchers usually make judgments that support their own background and opinions. (Rumrill, 2001)

3- Samples of study: Meta-analysis (Quantitative model) enables researchers to sample studies that show insignificant effects, whereas in Narrative-review (Qualitative model), the researcher use strategy to make the results and classifications. (Rumrill, 2001)

4- Cumulative impacts: Meta-analysis (Quantitative model) enables researchers to make a cumulative impact on insignificant results that turns to significant at the end (after analysis), whereas there is no cumulative impact in a Narrative-review (a Qualitative study). (Rumrill, 2001)

5- Mixed used: Vote-counting and Descriptive-review can be used in both forms of study (a Qualitative, Quantitative, or mixed); however, Vote-counting is most likely used in Quantitive, and Descriptive-review likely is used in a Qualitative method. (Rumrill, 2001)

6- Type of data analysis: Quantitative literature method (e.g., Meta-analysis) is to identify the statistical relationship, whereas a Qualitative literature model focuses on identifying patterns, features, and themes.

7- Nature of reality: Meta-analysis is objective and single reality literature, whereas Qualitative literature tries multiple subjective realities. (Myperfectwords, 2020)

8- There is more to talk about in this article, but it is out of the scope of this article.

Reference

Anthony, J., Nancy, L. Leech, Kathleen, M. T. Collins, (2012). The Qualitative Report. ed.gov. Retrieved 2022, from https://files.eric.ed.gov/fulltext/EJ981457.pdf

Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education with PowerWeb (6th ed.). New York, NY: McGraw-Hill.

Myperfectwords. (2020). Qualitative vs. Quantitative Research - Methodology & Design. Myperfectwords.Com. Retrieved 2022, from https://www.myperfectwords.com/blog/research-paper-guide/qualitative-vs-quantitative-research

Rumrill, Fitzgerald, P. D. J. S. M. (2001). Literature Review –. RESEARCH METHODOLOGY. Retrieved 2022, from https://doresearch.wordpress.com/category/literature-review/

Slideplayer. (2015, July 12). Ppt Video Online Download. Retrieved 2022, from https://slideplayer.com/amp/5309159/

Snyder, H. (2019). Just a moment. . . Journal of Business Research. Retrieved 2022, from https://www.sciencedirect.com/science/article/pii/S0148296319304564

Facial emotional expression detection by AI & DL

My primary topic of interest is "Facial emotional expression detection by AI." The idea came from a business meeting with CORE (a non-profit company backed by Sean Penn) in Los Angeles. They were looking for a high-level team of developers to plan, design, and implement a particular application to detect facial emotion expressions using mobile apps and iPods remotely. Why remotely? This was my very first reaction and question during the first meeting. Their manager started explaining the origin of the problem that led them to take action. It was all about helping people in remote places with the lowest access to medical and healthcare systems, especially those who need mental help the most. She was a psychologist and a very dear friend of mine and the surfing team on Malibu beach. She said, "we need an app that gets enough facial expression information from people who look at that app remotely, in which we can analyze and make our pieces of advice." 

Well, that made the topic of interest for my academics, as I felt the actual effect of the problem. The problem was not limited to the CORE or any other parties looking to help people with health disparities. The project can aim to judge with a better understanding of criminals, higher security controls in infrastructure systems by controlling people with sensitive roles, detectives with complicated criminal problems, help police directly on the action, help psychologists with more accurate analysis, and more. 

After the preliminary research on the topic, we realized that this project must use AI and DL using the Convolutional Neural Networks (CNN) to match the geometric features of the human face. 

So, here I am, a highly complex problem to solve, with the lowest amount of peer-reviewed articles.

The following image (from the internet) shows six general human expressions on their face. (Duong, 2022)

 

 

Reference

Duong, B. T. (2022, March 30). Emotion AI: Facial Expression Recognition with Convolutional Neural Network and Visualization with Grad-CAM and OpenCV. Medium. Retrieved 2022, from https://medium.com/mlearning-ai/emotion-ai-facial-expression-recognition-with-convolutional-neural-network-and-visualization-with-fbfbc5a5819a

Global Warming - Climate change

 I tried to collect some information about climate change by raising the average temperature every day. The video tool allowed me to put a few limited images and a few text boxes to take advantage of their free version. It worked well, and it is a nice tool.
Please watch the video here;

 

Futuring and Innovation - Solar Breakthrough

 

Sociotechnical Plan for Solar Breakthrough

The Solar Breakthrough is one of the most extraordinary human projects to save the planet by reducing the greenhouse gas emissions and fossil fuels from the industry and replacing them with clean sunshine energy.

All we need these days is to reduce the amount of CO2 emissions generated by industrial states worldwide to help our earth and climate. So the company "Heliogen," backed with an undisclosed sum of money by Gill Gates and others, tries to use the sunshine and generate a clean high, temperature energy to replace the fossil fuels. "We are rolling out technology that can beat the price of fossil fuels and not make the CO2 emissions," Bill Gross, Heliogen's founder and CEO told CNN Business. 

This technology will absorb the sunshine using a solar farm to exceed temperatures higher than 1,500 degrees of Celcius. At that temperature, industries can replace the use of fossil fuels with this clean energy. This figure shows the sociotechnical plan of the project.

Features of Solar Breakthrough

The main feature of this project is using AI and DL to control every single solar panel to get the best focal point of the sunshine and gather all solar energy in some containers as a whole high-temperature clean energy. Currently, the project could collect 1,000 degrees of temperature, and they are working to increase that to 1,500 degrees of temperature.

Limitations of Solar Breakthrough

There are limitations in working and collecting the sunshine, as it is not available all the time and in each place on the earth equally. Therefore, this project most likely is the best solution for those industries in areas with higher access to sunshine.

Purpose of Solar Breakthrough

The main purpose of the Solar project Breakthrough, as mentioned above, is to create a clean high, temperature energy using sunshine to replace the traditional fossil fuels and greenhouse energy. The following image shows a solar farm in the Los Angeles backed by Bill Gates.

References

Greencarcongress. (2019). Heliogen launches, achieves > 1,000 ˚C from concentrated sunlight for industrial processes. Green Car Congress. Retrieved 2022, from https://www.greencarcongress.com/2019/11/20191125-heliogen.html

Matt Egan, CNN Business. (2019, November 19). Secretive energy startup backed by Bill Gates achieves solar breakthrough. CNN. Retrieved 2022, from https://edition.cnn.com/2019/11/19/business/heliogen-solar-energy-bill-gates/index.html