#Multivariate Quantitative Research Methods solution

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A Guide To Dissertation Statistical Analysis Services

Writing a dissertation is a significant academic milestone that requires precision, dedication, and a strong grasp of research methodologies. Among the most challenging aspects of this process is conducting statistical analysis—a critical step to validate your research findings. At Tutors India, we pride ourselves on providing the best dissertation statistical analysis services tailored to meet your unique academic needs.

Why Statistical Analysis Matters in Dissertations

Statistical analysis is the backbone of any research that involves quantitative data. It ensures that your findings are reliable, reproducible, and scientifically sound. By employing the right statistical tools and techniques, you can:

Draw accurate conclusions from your data.

Strengthen the credibility of your research.

Identify patterns and trends that support your hypothesis.

However, the complexity of statistical methods and software often poses a challenge, especially for students unfamiliar with tools like SPSS, R, STATA, or Python. This is where professional services like Tutors India come into play.

What Makes Tutors India Stand Out?

At Tutors India, we combine academic excellence with industry expertise to deliver top-notch statistical analysis services. Here’s why our clients trust us:

Expert Analysts Our team comprises statisticians with advanced degrees and years of experience in academic research. They are adept at handling diverse methodologies and tailoring solutions to meet specific dissertation requirements.

Comprehensive Services We cover the entire spectrum of statistical analysis, including:

Descriptive and inferential statistics.

Hypothesis testing (t-tests, ANOVA, regression, etc.).

Multivariate analysis (factor analysis, cluster analysis, etc.).

Time-series analysis and more.

Customized Solutions We recognize that each dissertation is unique. Our analysts collaborate closely with you to understand your research objectives, ensuring the statistical analysis aligns with your study’s goals.

Cutting-Edge Tools We utilize advanced statistical software, ensuring precision and efficiency. Whether your data is small-scale or complex, we have the tools and expertise to handle it.

Timely Delivery We understand the importance of meeting deadlines. Our team works diligently to provide accurate results within your timeframe, allowing you ample time for review and integration.

Confidentiality Guaranteed Your data is safe with us. We follow strict protocols to ensure complete confidentiality and data security.

How Tutors India Supports Your Dissertation Journey

Our support goes beyond just analyzing your data. We offer:

Data Cleaning and Preparation: Ensuring your dataset is accurate and ready for analysis.

Interpreting Results: Simplifying complex statistical outputs into clear, actionable insights.

Report Writing: Providing detailed explanations and visualizations (charts, graphs, etc.) for inclusion in your dissertation.

Consultations and Revisions: Offering one-on-one consultations to address your queries and accommodate revisions as needed.

Why Choose Professional Help for Statistical Analysis?

Relying on experts for statistical analysis not only saves time but also enhances the quality of your dissertation. Here’s why:

Accuracy: Professionals minimize errors, ensuring your results are credible.

Expert Guidance: Gain insights into advanced techniques that elevate your research.

Peace of Mind: Focus on other aspects of your dissertation, knowing your analysis is in capable hands.

Get Started with Tutors India

If you’re navigating the complexities of dissertation statistical analysis, let Tutors India be your trusted partner. Our commitment to excellence ensures you receive reliable, high-quality support that meets your academic aspirations.

Contact us today to learn more about our dissertation statistical analysis services. With Tutors India by your side, you can confidently tackle your research challenges and achieve academic success.

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SPSS Help for Social Science Students in Analyzing Survey Data

Data analysis is much needed skill to be acquired for any social science student especially for those in research and surveys. One of the most widely used programs for this purpose is SPSS (Statistical Package for the Social Sciences). SPSS is adopted in academies and professions for the reliability of the statistical functionalities and ease of use of the application. This post aims to spss help for students pursuing social science courses and are seeking guidance on the use of SPSS for data analysis. We will discuss the basics of SPSS, how spss has become an important tool for research in social sciences, common difficulties that student face in spss and some helpful solutions to overcome these difficulties.

What is SPSS?

SPSS is an acronym for ‘Statistical Package for the Social Sciences’. It is one of the most popular statistical analysis software, designed for handling quantitative data. The SPSS was developed by Norman H. Nie, C. Hadlai Hull, and Dale H. Bent in the 1960s as a statistical package for the social science disciplines. It has developed into one of the most important and versatile tools of data analysis for use by researchers, academicians, and professionals in areas such as psychology, sociology, political science, public health and marketing. SPSS provides robust statistical operations like descriptive stats, bivariate statistics, linear regression, multivariate regression and many other functions. It gives the capability to import, process, and analyze data, test hypothesis, and generate comprehensive reports with tables and graphs.

Importance of SPSS in Social Science Research

In social science research, studying people’s behavior, societal trends, and social phenomena may involve gathering and evaluating complex collection of data. This is where SPSS is most useful as it makes analysis and generating the results in no time so that the researcher can devote more time towards identifying the patterns and finding insights.

Why SPSS is Preferred in Social Sciences:

Ease of Use: SPSS is easy to use for beginners, as it has a simple graphical user interface especially designed for students and researchers having basic knowledge of statistics. Most of the operations can be performed through the use of menus and dialog boxes, thus minimal programming experience is required for its operation.

Versatility: SPSS can analyse diverse types and formats of data and therefore can be used in various social science research such as experimental research, survey analysis, and observational research.

Comprehensive Statistical Capabilities: SPSS offers almost all advanced statistical procedures that suits the diverse research needs in social sciences. This includes methods for dealing with handling complex survey data, which is common in social science research.

Support for Large Datasets: Social Science research involves big datasets with numerous variables. Due to the ability of SPSS to handle large data sets, it has become the researcher’s preferred data analysis tool.

Integration with Other Software: It can also import and export data using data files from other softwares such as Excel, SAS, Stata and many others, which is viable for researchers who use various tools in their research process.

Using SPSS for Analyzing Survey Data in Social Sciences

Conducting analysis on survey data is one of the common tasks in social sciences research. Surveys are usually conducted to gather information concerning the various areas of life interest, such as the public opinion, consumer behavior to psychological traits and social attitudes. Analyzing survey data with SPSS involves several key steps:

1. Data Import and Preparation: Before analyzing survey data in SPSS, the data is required to be imported into the software. Some of the file formats that can be used includes excel, csv and text files by SPSS. Once the dataset in the raw format is imported, it must be pre-processed before analysis. This involves performing data screening to identify missing values, recoding variables, and creating new variables as needed.

Example: Suppose a student is analyzing survey data on social media usage among college students. The first step would be to import the survey data into SPSS and then check for any missing values or outliers that could affect the analysis.

2. Descriptive Statistics: The next step is descriptive analysis which involves the use of statistical tools to describe the data. This encompasses estimating measures of central tendencies such as mean, median, mode and measures of dispersion such as standard deviation, variance and range. Descriptive statistics are used to understand the data set and to recognize the patterns which may exist.

Example: The student might calculate the average number of hours spent on social media per day by college students and the standard deviation to understand the variability in social media usage.

3. Inferential Statistics: Inferential statistics are used to derive inferences about a population based on a sample. This includes hypothesis testing, in which one is in a position to test theories and assumptions.

Example: If the student wants to test whether there is a significant difference in social media usage between male and female students, they could use a t-test or ANOVA in SPSS.

4. Regression Analysis: Regression analysis is one of the most powerful statistical techniques that is commonly used for studying the relationship between two or more variables. In social sciences, regression is usually applied to explain how various factors affect an outcome. 

Example: The student might use regression analysis to examine how factors like age, gender, and academic performance influence social media usage among college students.

5. Data Visualization: SPSS offers a range of tools for visualizing data, including histograms, bar charts, scatter plots, as well as pie charts. Visualizations are much crucial for communicating the results of an analysis much effectively.

Example: The student could create a bar chart to visually compare the average social media usage between different demographic groups.

6. Interpreting and Reporting Results: The last part of the analysis is the interpretation of the results, and expressing them in a simple and comprehensible language. This includes the presentation of the results to the research questions and the interpretation of the results in the light of the questions. 

Example: The student might interpret the results of their analysis to conclude that female students spend significantly more time on social media than male students and discuss the potential reasons and implications of this finding.

Challenges Faced by Social Science Students in Learning SPSS

SPSS is one of the most powerful tools, and learning to use it effectively can be a challenging work for many students, particularly to those who are new to the field of statistics or data analysis. Some of the common challenges includes:

Understanding the Software Interface: The structure of the SPSS interface is good and easy to use, but due to the availability of multiple sub-menus and steps, the first-time users may get confused.

Choosing the Right Statistical Test: A perennial problem that social science students encounter is the kind of statistical test to apply in a certain analysis because the right test determines the validity of results. 

Interpreting Output: The output generated through SPSS can be confusing, containing numerous tables and figures. Interpreting them and deriving meaningful conclusions might be challenging.

Data Preparation: Data cleaning, transformation and recoding may require careful approach which at times may become challenging.

Application of Results: Connecting the statistical findings to a theory can be challenging, especially for the young statisticians or the students.

SPSS Help for Students: Specific Questions and Research Studies in Social Science

Specific questions that students may expect in their exams include analysing survey data, performing correlation and regression analyses, conducting t-tests and ANOVA, and examining categorical data through chi-square tests. For instance, a student might need to explore the relationship between socioeconomic status and educational attainment, assess the impact of a new policy on public opinion, or evaluate psychological traits using survey responses.

Types of Research Studies Using SPSS

In the social sciences, SPSS is applied both in quantitative and qualitative research projects. Quantitative research include surveys and experiments in which SPSS assists in analysing numerical data to find out whether hypothesis is supported or not, and to find hidden trends and make predictions. SPSS can be used in qualitative analysis to analyse coded data such as open-ended responses in interviews which facilitates pattern recognition. Cross-sectional studies, longitudinal data analysis and experimental study designs are commonly carried out using SPSS in social sciences.

Tips and Tricks for Using SPSS in Social Sciences

Utilize Syntax Commands: SPSS has a point-and-click user interface. But also mastering the syntax commands will save time and make the work reproducible.

Data Cleaning and Preparation: It is crucial always to clean and prepare your data before any analysis can be carried out. It involves examining for cases of missing data, outliers as well as ensuring that the variables are correctly formatted.

Use Descriptive Statistics First: Begin with Descriptive statistics in case you want to have a look at your variables before diving into more complex analyses. This can help identify any anomalies or patterns.

Explore Graphical Options: SPSS help students in providing numerous graphical options for the process of data visualization. Use charts and graphs for better understanding of your data and to effectively communicate your findings.

Why Students should Opt for SPSS Help from Experts?

SPSS assignments are sometimes complicated since they involve core statistical knowledge and the software program. That is where SPSS help for Students can be immensely useful to you. We provide professional expertise to complete your assignments with precision and detail based on the specific requirements and rubric. Our USP lies in the personalized approach we adopt for every student, detailed explanations of the results and inclusion of examples that enable students not just to complete their assignments but to learn how to do them as well. Engaging with a SPSS analysis help expert can minimize errors, improves accuracy and analytical skills, improves grades.

Would you like to experience and avail SPSS help for assignments? Communicate with us for details on how our SPSS help service for students can support your academic journey.

Also Read: 7 Key Steps to Perform Structural Equation Modelling in SPSS Assignments

Helpful Resources and Textbooks for Learning SPSS 

For improving SPSS analytical skills, the following resources provides much needed spss help for students:

"Discovering Statistics Using IBM SPSS Statistics" by Andy Field

"SPSS Survival Manual" by Julie Pallant

IBM SPSS Help and Tutorials

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Challenges that Ph.D. Students Face When Analyzing Quantitative Data

Introduction:

Quantitative Data Analyzing for PhD students often face various challenges while conducting research, including collecting and analyzing data.

Analyzing quantitative data can be a daunting task, especially for students who do not have a strong background in statistics.

Many students struggle with managing large datasets, choosing appropriate statistical methods, and interpreting the results.

In this blog, we will discuss some of the challenges faced by PhD students while analyzing their quantitative data.

Quantitative statistical data analysis’s importance

Regression analysis, multivariate analysis, significance testing, and other statistical methods are frequently used in Statistical data analysis in quantitative research. Expert analysts with strong quantitative skills and broad statistical knowledge are capable of handling these tasks with efficiency. Data can only be inferred statistically once the quantitative data analysis has been completed.

In quantitative data analysis, you must use critical and logical reasoning to transform unstructured data into information that is relevant. It is crucial to use thorough and impartial judgment because the same figure within a dataset may be interpreted in multiple ways. In quantitative research, only experts with the necessary training and expertise should analyze data.

Challenges of Quantitative Research Methods for PhD Students

Dissertation committees frequently criticize the method used to examine a study’s findings harshly. Not to mention how intimidating and challenging statistical data analysis is for PhD candidates in quantitative research.

The four main difficulties that PhD students and researchers have when interpreting quantitative data Analyzing are mentioned below.

1: Hypothesis development

2: Casualty: Cause and Impact

3: Generalizability (External Validity)

4: Reliability (Internal Validity)

1: Hypothesis development

A hypothesis is a statement that a research question has a possible solution. There are two different kinds of hypotheses: the null hypothesis, which states that there has been no effect or change, and the alternative hypothesis (this is usually an experimental hypothesis). We can only obtain evidence that either confirms or contradicts a hypothesis; it can never be proved or refuted. Concepts that need to be measured make up hypotheses. Concepts must be transformed into quantifiable elements and treated as variables.

2: Casualty: Cause and Impact

The process of explaining how things have come to be in their current state involves identifying specific variables in the analysis.

The dependent variable is the variable that is measured to determine the impact of the independent variable.

The independent variable is the variable that the researcher deliberately manipulates to observe its effect on the dependent variable.

The control of the Quantitative Data Analyzing variable is a potential independent variable that is held constant throughout the analysis to eliminate its influence on the dependent variable.

3: Generalizability (External Validity)

This refers to the external validity of a study, which concerns the extent to which the findings can be generalized or applied to other populations or settings beyond the sample studied. It pertains to the degree of extrapolation of results.

4: Reliability (Internal Validity)

In order to validate the research findings, this is concerned with the recurrence of the research. A reliable test must yield consistent results across trials.

The following are some ways that statistical coaches, consultants, or statisticians assist PhD students with statistical data analysis in quantitative research:

consideration of the data

Offering statistics instructions

Creating a plan for analysis

Choosing the study’s software and methodology

Quantitative data analysis implementation

Conclusion:

Analyzing quantitative data is a crucial part of the research process for PhD students. However, it can also be a challenging task that requires a significant amount of time and effort. PhD students need to be aware of the common challenges they may face while analyzing their data, and they should take steps to address these challenges proactively. By seeking help from mentors and peers, using appropriate statistical software, and investing time in learning statistical methods, PhD students can overcome these challenges and produce high-quality research that contributes to their field.

By utilizing statistical support,  PhD students can overcome the difficulties in efficiently assessing their quantitative data with the aid of data analysis in quantitative research. Additionally, these services follow moral standards.

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Multivariate Quantitative Research Methods Assignment Help

https://www.statisticsonlineassignmenthelp.com/Multivariate-Quantitative-Research-Methods-Assignment-Help.php

Multivariate  Quantitative Research Methods is one of the advanced topics in  statistics. Our Statistics  experts and Statistics online tutors being adept in these advanced concepts can  cater to entire array of your needs in Multivariate Quantitative Research  Methods homework help, Multivariate Quantitative Research Methods assignment  help, Multivariate Quantitative Research Methods dissertation help, Multivariate   Quantitative Research Methods quizzes preparation help etc. www.statisticsonlineassignmenthelp provides timely help  at affordable charges with detailed answers to your Nonlinear Dynamics assignments,   homework , research paper writing, research critique, case studies or term  papers so that you get to understand your assignments better apart from having  the answers.  www.statisticsonlineassignmenthelp assures to provide you  with well-structured and well-formatted solutions and our deliveries have  always been on time whether it’s a day’s deadline or long.

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5 research methods to improve web checkout conversion rates Don’t fret a high abandonment rate. Many users just go window shopping, comparing prices, saving items for later, exploring gift options, or just playing around while they check the latest fashion or gadget trends. We can’t avoid it. But you can do something to improve it. These UX research methods can help you with this, and these tips can help you better react to user frustration during the whole process. 1. Quantitative metrics for identifying the problem: metrics like average shopping cart size, effectiveness of recommendation engines, or abandon rate after each phase of the funnel tell much more about customer behaviour. 2. User interviews and usability testing to understand user behavior 3. Gathering insights from customer support 4. Creating a customer journey to draw a line between the dots 5. Testing on lots of users, and finalize it based on the results Once you have a possible new solution for the defined problem, make an A/B or multivariate test and compare the results. You can also combine it with previous methods, like usability testing both variations, and clean up the design and copy before publishing for A/B test. https://uxstudioteam.com/ux-blog/web-checkout/?utm_source=social&utm_medium=referral&utm_campaign=ownsocial

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Judgment reversed

#Judgment reversed verification

However, if the likelihood of rank reversal is high, then the decision maker should interpret the AHP rankings cautiously, as there is a subtantial probability that these rankings are incorrect. If the rank reversal probability is low, then the rankings are stable and the decision maker can be confident that the AHP ranking is correct. We develop multivariate statistical techniques to obtain both point estimates and confidence intervals of the rank reversal probabilities, and show how simulation experiments can be used as an effective and accurate tool for analyzing the stability of the preference rankings under uncertainty. In the presence of stochastic judgments, the traditional AHP rankings may be stable or unstable, depending on the nature of the uncertainty. If the relative preference statements are represented by judgment intervals, rather than single values, then the rankings resulting from a traditional (deterministic) AHP analysis based on single judgment values may be reversed, and therefore incorrect. This paper presents a methodology for analyzing Analytic Hierarchy Process (AHP) rankings if the pairwise preference judgments are uncertain (stochastic). He is an active collaborator of the International Institute for Applied Systems Analysis in Austria. Professor Duarte Silva has published in the European Journal of Operational Research, is the coauthor of a chapter in a book published by the American Psychological Association and has presented his research in several scientific meetings in Europe and North America. Professor Duarte Silva's major research interests are in the study of mathematical models for multicriteria decision making and in multivariate data analysis, with particular emphasis on discriminant analysis and classification methodologies. in business administration from the University of Georgia. in management sciences from the University of Georgia, and a Ph.D. He holds a Bachelor's degree in economics from the Universidade do Porto, an M.A. But if the judgment (whether it was obtained using shady tactics or not) is part of an overall debt dilemma, a chapter 7 bankruptcy may be the best way to rid yourself of your debts and get a financial fresh start.Ĭontact the law offices of John T.Universidade Católica Portuguesa, Faculdade de Cincias Económicas e Empresariais, Centro Regional do Porto, Rua Diogo Botelho 1327, 4150 Porto, PortugalĪntonio Pedro Duarte Silva is an assistant professor of quantitative methods at Universidade Cat6lica Portuguesa - Centro Regional do Porto. If your only issue is one judgment, you may want to try and settle the debt. If your debts are so significant that you cannot hope to pay them, bankruptcy may be an option for you to consider. You can even file this request on your own for just the cost of a filing fee. If the creditor cannot substantiate that you were served, you stand a reasonable chance of getting the judgment overturned. In this filing, you must explain that you weren’t properly served and ask that the judgment be reversed. If you get a judgment you know was obtained without due process, don’t take it lying down! You can go to the court and file a Motion to Set Aside a Default Judgment. How to Fight Back Against a Judgment That Was Wrongly Granted Unethical collectors will tell the court you were served and when you fail to respond or appear (because you knew nothing about it), the creditor gets a default judgment.īankruptcy can be an effective solution to debt collection But with debt collection suits, the responsibility for serving the suit can be taken on by the creditor or collection agent that filed the claim. This is common with divorce, child custody cases and personal injury lawsuits.

#Judgment reversed verification

How Debt Collectors Get Judgments Illegallyįor some court cases, the sheriff’s department will file the notice so there is verification of service. You can fight back against a judgment obtained through shady tactics, but you have to move faster than the sneaky debt collector! And the bad news is, with a judgment in hand, a creditor can move rapidly to garnish your wages or try and levy your bank account. We frequently have clients that come in for debt and bankruptcy counseling tell us that they received notifications of a judgment against them as a result of a lawsuit they knew nothing about. One of the shady tactics employed by unethical debt collectors is obtaining judgments through sneaky (and illegal) means.ĭid You Get a Judgment on a Lawsuit You Knew Nothing About? But there are some collectors that are unscrupulous and will harass you, lie to you and go beyond the bounds of the law. Sometimes collections agents will be reasonable and obey Fair Debt Collection Practices Act regulations. When your money is tight and you can’t pay your bills on time, the inevitable result is that you’ll be contacted by debt collectors. Judgments can be intimidating, but are reversible

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Understanding the Use of Calculus in Real World

Find Calculus difficult or confusing? Want to master the complicated subject? Attending lectures on calculus is not enough if you really want to learn calculus. There are several time-tested specific methods for learning calculus. And with online calculus tutoring you can learn these methods eventually. However, before you begin to search for calculus tutoring online, you need to understand what exactly calculus is and what is its use in real life scenarios!

Let's have a look at why you should learn calculus and how it can help you in real world:

What Exactly is Calculus?

Calculus is a branch of mathematics that focuses of derivatives, integrals, limits, functions and infinite series. This branch of mathematics is an all-embracing study of how things change. We can say that, calculus provides the fundamental framework that can help in understanding and modelling a system that keeps changing. With the help of calculus, you can construct a quantitative model of change which is simple to understand. It also helps in making or deducing predictions of the models that change continuously.

How Calculus can be used in real life scenarios?

If you do not understand the use of calculus in real life scenarios,you will find calculus confusing and will get de-motivated. Therefore, it is necessary for you to realize the extensive usage of calculus in real life scenarios. Without understanding its actual use, learning the complicated concept is of no use.

Study of calculus bestows its learner with an ability to realize and establish the effects of the changing conditions on the system of study. The wide-ranging research and learning of calculus enable you to even control the system in accordance with you. You can use the system as per your wish. Sounds exciting right? You will be astonished to know that Calculus played a great role in the industrial revolution. It is due to its application in engineering that it played a significant role in industrial development and the expansion of modern science.

Introduction to Learning Calculus

So do you want to learn calculus and are you looking for online calculus tutoring? l To begin with, we can say that calculus describes the old notions of speed, position and acceleration. Then comes single variable calculus concept that is a study of an object that moves along a fixed path. After that, multivariable calculus comes into the picture that deals with the movement of an object on a non-linear surface or space.

The study of calculus then includes Differentiation i.e. finding instant changes of the varied functions, finding solution to certain problems using derivatives and Integration i.e. getting back  the derivative of the function.

And there is so much more that calculus can help you explore!

Conclusion:

Skyline Tutoring provides online calculus tutoring for all levels.

[irispublishersfashion]

Iris Publishers_Journal of Textile Science & Fashion Technology (JTSFT)

Fashion Design Entrepreneurship: Skills and Solutions to Create a Fashion Business

Authored by Clara Eloise Fernandes

Abstract

Purpose: This study proposes a vision of entrepreneurship in fashion design. Higher-education courses have adapted, and fashion design courses have evolved and moved to a more entrepreneurial concept, as a generation of fashion designers has transformed past experiences and professional vision to become entrepreneurs. Authors and reports linked to entrepreneurship observe more than ever the importance and necessity to bring entrepreneurship very early to classrooms. Studies are more divided on that opinion and show that the introduction of such concepts in early stages of education can be harmful for the future of entrepreneurship if those concepts are poorly taught to students.

Methodology: This study employed a mixed-methods approach, as it considered the use of questionnaires to collect data from students and recently graduated students from fashion design schools, in Portugal and abroad; and semi-structured interviews to collect opinions of three main groups of industry professionals: fashion design entrepreneurs, solvers, and specialists. Linear regression & multivariate linear regression was used to analyze the quantitative data obtained, using SPSS. For the qualitative data obtained through interviews, QSR NVivo was used to analyze and encode answers. Finally, a project methodology was used to create a digital platform, proposed as a solution in this study.

Findings: Findings obtained in this study show a lack of support from entities for fashion-related ventures, as well as an evident lack of entrepreneurial thinking in fashion design courses, translated by enormous difficulties for young fashion designers willing to create their own business. Therefore, the need for a solution helping fashion design entrepreneurs was also clearly highlighted by the results obtained. Considering the results obtained through this study, a model for the creation of an entrepreneurship platform will be proposed to create value in the fashion industry.

Research limitations: The main limitation of this study is related to the definition of entrepreneurship itself, as many authors still diverge on this subject. Adding fashion to this topic is also controversial, as the definition of a fashion entrepreneur as yet to be made. Although highereducation courses have made transparency efforts in order to clarify their curricula, this study shows that the specificities presented on the courses and institutions official pages are not very easy to dissect, as many courses present business creation as a potential outcome, without referencing any specific topic on this subject in their curriculum.

Originality / value: This study inserts itself in a multidisciplinary field, mainly composed of two great areas: fashion design and entrepreneurship. The creation of this new subject and the parallelism created between design thinking and entrepreneurial thinking is also crucial. Moreover, the creation of value in the fashion design industry is the main goal here, as it is believed that fashion design SMEs can change the very controversial fashion and textile industry by adding new solutions and value to this billion-dollar market.

Introduction

The Portuguese textile and clothing industry have undoubtedly experienced many changes in the last few years. After the international crisis that stroke hard the economy of many countries, the crisis has been the catalyst for unemployment and austerity as its consequence. However, countries like Portugal are showing a real evolution since those dark times. The textile industry of Portugal has ended the year 2016 with 5063 million euros in exportations, a number that had not been reached since the beginning of the century [1], encouraging and pushing the Portuguese textile and clothing industry further into former previsions made by the director of ATP (Textile and clothing industry association), Paulo Vaz.

Portugal has also experienced a major augmentation regarding higher-education demand from students. Fields like fashion, apparel and textile design have seen the number of entering students increase in their higher-education courses, considering years 2009/2010 in comparison to 2015/2016 [3,4].

Entrepreneurship has also been unquestionably one of the most used words in the past few years, in Portugal and internationally. In Portugal, such affirmation can be confirmed through the number of entrepreneurial models and incentives proposed and created, most of the times linked to regulatory proposals made to emphasize such ventures [5]. In this context, entrepreneurship has become more than something achievable with “luck” and is now considered by public opinion on a global scale as an objective of improvement by many countries, seeing an opportunity and solutions through the growth of entrepreneurship.

More generally, students coming from various fields related to creative arts may benefit considerably from an entrepreneurial mindset, as innovation and multidisciplinary contents are part as these fields as they are part of entrepreneurship itself and can very well lead to a variety of jobs [6] . On the other side, the fashion design field has come to adopt entrepreneurship in another way for the past few years, in the sense that it can be conceded that some individuals have always created their businesses in the field, even if entrepreneurship cannot be reduced to such definition.

In such circumstances, the fashion industry has come to understand the need to innovate in an ever-changing field that comes across crisis on a daily-basis [7], even if on a national level, many are the family SMBs that cannot evolve and grow through innovation, entangled in their traditions, many times associated with the need for family union and only decider of the business's future [7].

As governmental entities have understood the importance of entrepreneurship for the future, many studies are also being made to determine whether or not entrepreneurship education can be the engine for a new generation of entrepreneurs [8-12].

Years after the most recent economic crisis that stroke the world, it is important to reflect on the current reality in which our society inserts itself, as well as how the powerful fashion industry has seen a new generation of fashion design entrepreneur rise, in order to change a paradigm where only fast-fashion and historical luxury brands were in.

Even with the recent numbers of unemployment keeping at their lowest since 2009 [13], Portugal is still sixth in the ranking of highest unemployment rates in the European Union, and fourth when only considering the Eurozone [14]. More importantly, youth unemployment is still a massive problem for the country, as its rate was 28% in the last trimester of 2016, according to the National Statistics Institute (INE), putting young people between the ages of 15 and 24 years old in a critical place [15].

According to Thomas Friedman, editorialist at The New York Times, paradigms have changed, and generation used to the reality of finding a position after graduation are now in need to create their way into the job market by becoming self-employed, in comparison to the previous generation that “had it easy” [16]. In Portugal, small and medium-sized enterprises (SMEs) lead the numbers, generating low rates of employment at the time [17]. As the socioeconomic frame in which we are inserted has come to create an impulse and evidence the need to develop alternatives to traditional jobs or, when they do not exist, created through new businesses, entrepreneurship can become a solution.

According to the European Commission 2008 report on entrepreneurship education, up to 20% of students who participate in an entrepreneurship education program in secondary school will later start their own company. However, as the primary objective of this investigation aims to understand entrepreneurship as a potential solution for young fashion designers, entrepreneurship education will be approached in the higher education environment. Moreover, this study will also approach the definition of the word entrepreneur [18,19], as many still reduce it to the creation of a business, yet, being an entrepreneur is far more than creating selfemployment [20-24].

Furthermore, by exploring entrepreneurship in the fashion design field, this study has for objectives, firstly, to clarify if fashion design higher education programs are prepared for the new challenges of a society always more directed to entrepreneurship; secondly, to understand what specific skills and attitudes young fashion designers lack when it comes to creating their venture in the industry and finally, and thirdly, an exploration of existing solutions aiming to help fashion design entrepreneurs will be made as well as a search for qualities and functions that could be gamechanging.

This study inserts itself in a research gap, where very few studies address fashion design entrepreneurship as a field. This topic, which is very new regarding scientific research, is approached locally and globally, to contribute to the scientific exploration of fashion design and entrepreneurial activity in the field. Moreover, this study seeks to understand who are these fashion design entrepreneurs in Portugal and abroad, as well as comprehending their stories, the point of view as professionals of the industry, the main difficulties they encountered in their journey, and most importantly, if fashion design higher-education can contribute to the increase of such behavior.

A mixed-method approach is used to cover as much information on both sides of this issue [25]; fashion design students in their senior year will be inquired as well as recently graduated students and on the other side of the fence. On the qualitative analysis side, three groups of distinctive professionals related to the fashion industry will be interviewed to understand the crossroads between entrepreneurship and fashion design.

The results obtained through this analysis aim to contribute to the scientific research in the field by choosing a topic of investigation socially relevant, a problem that belongs to the disciplinary field of design, using a model that can be applied in future investigations, and finally, a process involving users [26]; as the results obtained will directly contribute to the creation of a solution, proposed here as a model, aiming to help fashion design entrepreneurs.

Research Questions

In an interview on the French late show “On n'est pas couché” [27], Olivier Rousteing, creative director at Balmain reflected on his dream as a young child, knowing that he liked to design clothes at a very young age and declared that for him, having a passion was great, but it would be even better to turn it into a job. Rousteing also proclaimed that he senses that this is an actual issue among young people nowadays, as many of them dream to turn that dream into a profession but are never able to. As the scientific field of fashion studies is still very recent [28] the study of multidisciplinary topics involving fashion design is crucial, this study inserts itself in this logic, as it aims to comprehend the relation between fashion design and entrepreneurship.

The challenges and opportunities that come into the path of Fashion Designers is the core of this investigation, considering higher education and its transcription on the job market. The discussion of such thematic develops itself around a set of research lines, considering the education of Fashion Designers: youth unemployment that affects almost every field of activity, the professional skills of these students leaving the educational system, the lack of experience from these young people at the end of their education, as well as the perspective of self-employment.

Considering for that matter fashion design as the nucleus of this research and the particularities of fashion design research [29,30], the following research questions appear: are fashion design higher education courses prepared for the new challenges ahead, in a society that is more entrepreneurial than ever? What specific skills, knowledge and attitudes of young designers lack of to be finally able to launch their venture in this particular field? What are the solutions that are created or can be created to help young designers aspiring to become entrepreneurs? These are the main lines in which this study inserts itself.

The research questions are based on all the previous investigation made to this moment, and it is believed that they reflect what Moreira da Silva interprets as the four conditions essential to produce an investigative work in design: “the problem must belong to the disciplinary field of design, the methods used must construct themselves into a model that can be applied in future investigations or in the profession of design itself; the topic of investigation must be socially relevant, the process must involve the users ”[26]. The four conditions presented by Moreira da Silva were adapted in the context of this study and were used as a guide to elaborate the following figure, demonstrating the importance and articulation of the research questions.

In Figure 1, it can be observed that the first question reflects the necessity to understand if students from higher educational programs developed the necessary skills to face the challenges of an entrepreneurial society. For Frideman T [31], developing skills and being innovative is crucial, as being able to use information that has been taught in the classroom is more important than the information itself. Through the research that was previously conducted, it can be noticed that many factors are contributing to a devaluation of education, as it was the case for the Bologna process. This depreciation that was also the object of study of many researchers of the design field, namely Alexandra Cruchinho, who approached this thematic in her doctoral thesis entitled “Design- The construction of skills continues”.

For More Open Access Journals in Iris Publishers Please click on: https://irispublishers.com/   For More Articles in Journal of Textile Science & Fashion Technology https://irispublishers.com/jtsft/

For More Information:https://irispublishers.com/jtsft/fulltext/fashion-design-entrepreneurship-skills-and-solutions-to-create-a-fashion-business.ID.000553.php

[major-final-project]

Research (Digital Bridge)

Week 1

Whether it’s building a new playground or developing a mobile app for pet groomers, there are multiple ways to satisfy a project brief. However, in order to design a product that successfully delivers business value, it is critical to first clearly define the design problem.

Ask your clients these three key questions at the start of every project:

What is the business objective?

What is the context of product use?

What are the user goals? What is the business goal? This is the most critical question that some design teams still don’t ask stakeholders. Understanding business objectives help your design because it allows you to drill for more specific information. Follow-up questions can unlock a wealth of insights that influence the design approach: How do you know this is an issue? Who is affected by the issue? When and how often does this occur? What benchmarks do you have and what change do you expect? Imagine that your client aims to reduce tech support calls for an e-commerce site. If customers struggle to complete purchases, drilling into root causes might reveal that logging into an account is a major hindrance, or that the website refuses to validate shipping addresses. Interviews with tech support teams can also reveal pain points that customers are experiencing.

Understanding business goals also helps the design team focus and refine work through iterative user testing before full product launch.

For instance, if time on task is expected to decrease by 15% following an interface-lift, that’s a clear target to test against with prototypes.

What is the context of use for this product?

Answers to questions using where, why, when, how often, and so on, describe context of product use and elucidate multiple design decisions.

At a macro level, context informs what technology should convey the design. At a micro level, context places restraints on interactions and the visual treatment of the interface.

Imagine a food manufacturer who wants his quality control technicians to enter production data (such as oil temperature) on a kiosk-based laptop on the factory floor. On the surface, this is a simple problem. But would this be a wise technology choice if the technicians have to enter multiple production values every five to ten minutes? A tablet that the user can carry would be a better choice given the context of use, but if the client doesn’t volunteer such information, how could the design team know to make this recommendation?

What do users expect?  

Business and user goals can be very different. Successful design finds common ground to satisfy them both.

Business stakeholders are often biased or completely naive about their users, making it all the more important to conduct research directly with the intended audience. Understand not only what users need to do, but also what motivates them and what attitudes they have toward their tasks.

When business and user objectives are mapped out, designers should create user flows that support desirable user behavior while satisfying user needs and aligning with their attitudes.

For instance, Amazon prompts shoppers with additional products while at the same time offering hassle-free one-click ordering. Similarly, TurboTax has helped its success by using clean and playful design that supports users during a task they likely find tedious, unpleasant, or even anxiety-inducing.

(14 February 2019)

Summary: Do you need numerical data about your product’s user experience, but you aren’t sure where to start? The first step is choosing the right tool. Check out this list of the most popular types of quantitative methods.

Going into a new office can always be daunting and difficult to adapt however I was introduced warmly into the team, I then was able to stand in a meeting that was a company overview talking through the different company goals and the different teams working on different products and projects. It was quite insightful to see how the company is growing and is needing an organisation and structure to keep the company streamlined and in full open communication. 

I then sat with Ashleigh and Tina to discuss the placements outcomes and goals for my project deliverables.  I then presented them with my time plan and other project deliverables. I will then get this signed off and share the google drive with both Tina and Ashleigh. 

 For the first week, Tina wanted me to start off researching into different research methods and user research techniques. On this blog post, I will be putting in different aspects of research I will find throughout my research time.

https://usabilityhour.com/improving-user-experience/

Intro to User Research: 

It’s well understood that user research is what makes for the best user experiences but what are the right user research techniques for mobile apps? While, there is no doubt that any classic UX researchtechnique may be turned to mobile app user research – there are some techniques which have already been demonstrated to show proven value. Mastering these will help you develop better mobile apps that more closely mirror your users’ expectations.

Mobile is the fastest growing way of accessing the internet in the world. Mobile apps constitute the majority of activity on the smartphone platform. This presents huge opportunities for the mobile app developer but in order to get the user experience right; it also presents a big demand for high-quality user research.

The global app market is now worth more than $100 billion. That’s a significant chunk of change and to secure some of that market will require great user experiences from mobile apps. Mobile user research is the key weapon in the UX designer’s armoury to conquer some of that market.

When to do research

The first thing to know is that there is never a bad time to do research. While there are many models and complicated diagrams to describe how products get built, essentially, you’re always in one of three core phases: conceptualising something brand new, in the middle of designing and/or building something, or assessing something that’s already been built.

There’s plenty to learn in each of those phases. If you’re just starting out, you need to focus on understanding your potential users and their context and needs so that you can understand your best opportunities to serve them. In other words, you’re trying to figure out what problems to solve and for whom. This is often called generative or formative research.

Research goals:

Consider things like:

the stage of the project you’re in

what information you already know about your users, their context, and needs

what your business goals are

what solutions already exist or have been proposed

or where you think there are existing issues.

Where to do research: 

It’s often ideal to be able to perform research in the context of how a person normally would use your product, so you can see how your product fits into their life and observe things that might affect their usage, like interruptions or specific conditions.

For instance, if you’re working on a traffic prediction application, it might be really important to have people test the app while on their commute at rush hour rather than sitting in a lab in the middle of the day. I recently did some work for employees of a cruise line, and there would have been no way to know how the app really behaved until we were out at sea with satellite internet and rolling waves!

After determining your research goal, it’s time to start looking at the kind of information you need to answer your questions.

Quantitative data

Quantitative data measures specific counts collected, like how many times a link was clicked or what percentage of people completed a step. Quantitative data is unambiguous in that you can’t argue what is measured. However, you need to understand the context to interpret the results.

Quantitative data helps us understand questions like: how much, how many and how often?

For instance, you could measure how frequently an item is purchased. The number of sales is unchangeable and unambiguous, but whether 100 sales is good or bad depends on a lot of things. Quantitative research helps us understand what’s happening and questions like: how much, how many, how often. It tends to need a large sample size so that you can feel confident about your results.

Common UX research methods that can provide quantitative data are: - Surveys - a/b testing - multivariate tests - click tests - eye tracking studies - card sorts.

Qualitative data

Qualitative data is basically every other sort of information that you can collect but not necessarily measure. These pieces of information tend to provide descriptions and contexts, and are often used to describe why things are happening.

Qualitative data needs to be interpreted by the researcher and the team and doesn’t have a precise, indisputable outcome. For instance, you might hear people talk about valuing certain traits and note that as a key takeaway, but you can’t numerically measure or compare different participant’s values. You don’t need to include nearly as many sessions or participants in a qualitative study.

Common UX research methods that can provide qualitative data are usability tests, interviews, diary studies, focus groups, and participatory design sessions.

Persona development: 

[juniperpublishers-agriculture]

Ethno-Pharmacognosy and Diversity Encourage Conservation of Wild Ziziphus species Collected from KP, Pakistan-Juniper Publishers

The genus Ziziphus consists of almost 100 species been used in folk and alternative systems of treatment in order to combat different diseases such as; fever, diabetes, skin infections, antipyretic, antinociceptive, antioxidant, antilisterial and larvicidal [1-4]. Due to peculiar geographical region, Pakistan exhibits a great diversity of flora as represented by 7 genera along with 13 species for Ziziphus [5].

Ziziphus oxyphylla Edgew (synonym; Ziziphus acuminata Royle) also written as “Zizyphus oxyphylla Edgew” belongs to the genus Ziziphus and family Rhamnaceae (known as buckthorn family). The plant Z. oxyphylla, popular with common names i.e. Mamyanu, Elanai, Tukbari, Phitni, Amlai and Sezen is a small glabrous tree with short, recurved and unequal spine along with edible fruit (oval in shape), belongs to the genus Ziziphus. Based on folklore use, the plant is in-use for traditional treatment of diseases i.e. jaundices, diabetes, hypertension as well as in gas trou bles [6-9], since long, Z. oxyphylla distributed in different areas of Pakistan, but mostly in warm temperate and subtropical regions throughout the world, especially in Pakistan and India. In Pakistan it may be found in different regions, particularly the rainy and mountain areas as well as the Himalayan series of mountains. The plants is distributed as; Swat Valley, Northern Pakistan, Chagharzai valley, District Buner, KP, Pakistan [9], Buner, Hazara, Swat, Garhi Habibullah, Dir Kohistan valleys, Pakistan [6,10], Kot Malakand agency, Islamabad , subtrop-ical hills of Darazinda, Takht-e-Suleman range Dera Ismail Khan, Pakistan, Palas Valley, Pakistan, Kotli, Azad Jammu Kashmir, Pakistan [11-13].

Ziziphus mauritina Lamk, is most important an well-known species of Ziziphus belonged to buckthorn family Rahmnaceaes. Z. mauritiana genotypes are very important medicine, ethno medicine and traditionally for the control of different diseases cough, sore throat, anti-oxidant, high fevers, jaundice, diabetes and hypertension etc, [14-16s]. well distributed in different region of tropical and sub-tropical areas of the world and mostly found in all continent of the world and native of Afghanistan, Australia, North Africa, North India, Malaysia, and southern China and Pakistan Z. mauritiana is a dominates species/variety of wild vegetation in different arid and as well as desert while in great examples of drought and hard species of Ziziphus [17,18]. In Pakistan, it is widely distributed in three provinces i.e. KPK, (Banuu, Karak and Kohat) while district swat, Buner and Dir is found in only wild from, Punjab (Attock, Chakwal and Mianwali districts) and Sindh province (Karachi, Hyderabad and Nawabshah districts). In Pakistan, jujube is cultivated on an area about 5.425ha with an annual production of 28.000 tones [19].

There is lack of information regarding jujube cultivars in Pakistan. Indigenous cultivars are missing and there is little research work available on their botanical classification [18]. Fluctuation in yield and quality of jujube fruit is greatly affected by the soil properties, climatic factors and cultivar selection [20]. Fruit quality attributes largely depend upon cultivar to be selected. Previously, variations in fruit weight, juice content [21] and seed weight has been recorded in jujube cultivars [22].

Morpho-biochemical methods are used to screened best genotypes in large collected germplasm of different crop species [23- 30]. Among these methods the SDS-PAGE method is effectively used to determinate the taxonomic and evolutionary difficulties of certain plant species [28]. The seed storage protein studies help in documentation and description of variability in crop varieties, cultivars and their wild species but also rich genetic variability and phylogeny association of the accessions. It is considered that variability in protein bands intricate the association among the assortment from various geographical regions [31]. SDS PAGE as a powerful tool that has been used in the solution of problems in the field of taxonomy and explains the origin and evolution of cultivated plants, including the fenugreek [32].

It provides maximum variability among different crop species and the level of polymorphism depends upon the plant species [33]. Haliem and Huqail [34] found 168 different polypeptides bands among diverse Fenugreek genotypes of Saudi Arab and Yemen through SDS-PAGE method. They recorded 26 different polymorphic bands characterized Brassica rapa sub-species brown sarson through this method and recorded 83.33% polymorphic protein bands. They also noted four different cluster groups for the twenty studied genotypes. Jan et al. [28], evaluated three different ecotypes of B. rapa through SDS-PAGE method and high level of variability were noted in protein bands size. Protein electrophoresis is considered a reliable, practical and reproducible method because seed storage proteins are the third hand copy of genomic DNA and largely independent of environmental fluctuations [34,35].

The main objectives of the current research studies on Ziziphus species (1) ethno medicinal uses of Ziziphus species in local peoples (2) to explore different aspects the genetic relationship of wild Ziziphus species (Z. oxyphylla and Z. mauritiana) genotypes based on different morphometric collected from different regions of district Swat KP, Pakistan (2) to study genetic diversity based on store seed protein (SDS-PAGE) of wild Ziziphus species of their genotypes (3) by the using of multivariate analysis, phylogenetic, PCA, for the checking of their relationship of wild Ziziphus species collected from different regions of distract Swat KP, Pakistan.

    Materials and Methods

Samples collection and Ethno-pharmacognosy

Exploratory trips were arranged to different areas of KP, Pakistan years, 2017-2018 and total two Ziziphus species (50 genotypes) were identified and investigated for morphological characterization and SDS PAGE protein profiling. The plants (Ziziphus species) Z. oxyphyla and Z. mauritina specimen were store in the herbarium Department of Botany, Hazara University, Mansehra, KP, Pakistan; the specimens were recognized referring different Floras, viz., Hooker (1872-1897). Ethno medicinal data has been collected through Participatory Rural Appraisal (PRA), which is based on communication with indigenous people and direct communication in the field Martin [36]. The data have been noted through semi-structured interviews with people involved in the plants, organization [37]. A total of 130 residents have been interviewed during the field survey, information on uses of plants to cure various diseases of human being, parts used, of medicine have been collected. Based on the information obtained from the informants in the study area, all the reported diseases have been classified into 12 groups.

The different level of the similarity among information delivered by various informants was calculated by the Informants’ Consensus Factor, ICF Trotter and Logan [38] by applying the following formula:

Where, Nur = number of use reports from informants for a specific plant-use category; Nt = number of taxa or species that are used for that plant use category for all informants.

ICF Values range between 0 and 1, where ‘1’ indicates the highest level of informant agreement. The fidelity level (FL), the percentage of informants claiming the use of a certain plant species for the same major purpose, was calculated for the most frequently reported diseases or ailments as:

Where, Np = number of informants that claim a use of a plant species to treat a particular disease; N = number of informants that use the plants as a medicine to treat any given disease [37].

Morphological analysis

In the current work qualitative and quantitative characterizations were carried out of the collected samples, Qualitative traits were recorded on the general visualization (phenotypic observations). Ten qualitative traits i.e. Tree vigor, Leaf type, Leaf shape, Leaf colure, Tomentose, stem color, Spines, fruit color, leaf margin, fruit shape and quantitative characters which were measured with the help of vernier caliper for the measurement of plant height(feet), Branching, Leaf length(mm), Leaf width(mm), Leaf thickness(mm), Petiole length(mm), Inter node length(cm), Stem diameter(inches), fruit weight, fruit diameter (mm) and fruit length (mm).

Protein extraction and their preparation

Total seed protein (SDS-PAGE), a single mature and uncontaminated seed of Z. oxyphylla and Z. muritiana was selected for the analyses of total protein form each genotype collected from different regions of Swat KP, Pakistan. The seed was ground through the pestle and mortar and for the extraction of total protein and were add 400μl protein extraction buffer composition as (0.5M Tris- HCL pH 8.0, 0.2% SDS, 5M Urea, 1% B-mercaptoethanol) to 0.1g of seed powder into 1.5mL eppendr of tube and then vertex for three (3), mints thoroughly to homogenize the powder and solution.

Bromo-Phenol Blue (BPB) solution was added to the protein extraction buffer as tracking dye to monitor the movement of protein in the gel, then samples were centrifuge for 40 mints at 14,000rpm at 10 0C and follow the standard electrophoretic method [4,39]. After centrifugation samples the curd protein were recovered as a clear supernatant on the top of the tube. Then were transferred into a new tube and were store at 120 0C until electrophoresis. After that for the checking of make 12% polyacrylamide gel the separation gel were making form the mixing of (3.0M Tris- HCl pH9.0, 0.4% SDS) and 4.5% stacking gel (0.4M Tris-HCl pH8.0, 0.4% SDS), Electrode buffer (0.025 M Tris, 129 M Glycine, 0.125% SDS) was add into the top of the gel plate and select total volume as 8μl of the protein extraction mixture were loaded into each well of the gel with the help of micropipette and add molecular markers and finally run 100V until the blue color were passed through the bottom of the gel plates, the gel were stained by the staining solution containing 0.2% BPB dissolved in 10% glacial acetic acid, 40% methanol and water in the ratio of 10:40:50. Gels were destained in a solution consisting 5% acetic acid and 20% methanol for 15 minutes [40].

Data analysis

The current data was recorded from the design gel (destined) on the basis of absences and presences of total seed protein gel bands, 1 is denoted for the presence and 0, for the absence of the loci were arranged in Microsoft excel 2010, and this 0, 1 data were analyzed for cluster analysis and PCA (Principles Component Analysis) was performed by PCord 5.0, SSPS and Statistics.

    Results

Ethno-pharmacognosy

The current research work on two wild Ziziphus species Z. oxyphylla and Z. mauritina collected from different regions of KP, Pakistan, we have been in listed for control of diseases categories as12 and for each Ziziphus species , scientific name, family , local name, illnesses to be treated, and parts used for the different diseases were noted in (Table 1). Consumption of plant parts as medicine among the informants shows disparities. Fruits are mostly used part for majority, followed by roots, leaves and bark, this was the first time of the study area threat to the species is marginal as seeds are the leading plant part used for medicinal purposes. It was supposed that the collection of part of plant as medicinal part from the wild were not manageable. According to residents, this type of activity is done by the collectors related to illegal activity of medicinal plants. Ziziphus species mostly Z. oxyphylla is vulnerable to this type of activity in the study region. ICF values were established to know the settlement among the informants of Swat valley for usage of plants to cure certain illness groups. The ICF values ranges from 0.992 to 0.124 with an average value of 0.440. Jaundice has the highest ICF value 0.992 with 130 use-reports for 2 plant species.

The specie liable for this high consensus was Z. oxyphylla with 130 of the defined events, linked by Blood purification values for ICF 0.8140, 107 use reports and 2 species respectively, for the use of Digestive infection ICF values 0.682, 90 and for 2 species etc (Table 2). Medicinal plants thought to be effective in treating specific illness have high ICF values. The high ICF value for Urinary infection possibly unveiled that this ailment is common in the study area High ICF values also designate that the specie predictably used to treat these illnesses are worth searching for bioactive compounds. The least agreement (ICF=0.124) between the informers was detected for plants used to cure Lever protection. The low ICF value as noted in our study could be due to a lack of communication among people in various areas. To discover conventionally significant medicinal species in the society, Fidelity Level (FL) of plants has been predicted based on use reports which have been cited by 50 or more informants for being used against a given disorder and the examination demonstrated that the highest FL value found in Z. oxyphylla followed by Z. mauritina respectively. The least FL value was found in the case of Z. oxyphylla. FIC and FL studies presented that the most commonly used species in the study area are Z. oxyphylla (ICF = 0.992) with 130 use-reports and FL value (100%). When choosing the most ideal plant species for each ailment category, we took the high-fidelity Level (%) in each category of ailment due their high biological compounds which were used for the control of various numbers of diseases (Table 2).

Morphological characterization

In the current work qualitative and quantitative characterizations were carried out of the collected samples, Qualitative traits were noted on the general visualization (phenotypic observations). qualitative traits i.e. Leaf pubescent, Leaf shape, Leaf color, fruit color, Seed shape and quantitative characters which were measured with the help of vernier caliper for the measurement of plant height (feet), Branching, Leaf length (mm), Leaf width (mm), Leaf thickness (mm), Petiole length (mm), Inter node length (cm), Stem diameter (inches), fruit weight, fruit diameter (mm) and fruit length (mm).

By using the Pearson correlation coefficient, the result for the association coefficient among the various traits for the two species of Ziziphus (Z. oxyphylla and Z. muritiana) was performed (Table 3 and 4). In correlation study the petiole length in the Z. oxyphylla, is negatively correlated with leaf length in the while positively correlated with leaf length in Z. muritiana. Leaf width is negatively correlated with the leaf length in Z. oxyphylla while positively correlated in the Z. muritiana. The flower length in Z. muritiana and Z. oxyphylla is negatively correlated with the leaf length and leaf width and so on.

The double data matrix of 50 genotypes based on morphology was analyzed for the construction of phylogenetic tree to represents the similarity of various species or genera and the two species of the Ziziphus were investigated for similarities and the phylogenetic tree was constructed. The phylogenetic tree divided the two species in three groups R1. RII and RII (Figure 1). R1 and RII consisted of total genotypes of Z. oxyphylla. While the RII was into composed of all genotypes of Z. muritiana. The similarity indexes were performed for all the genotype of 2 species that was 23.529% for Z. muritiana and Z. oxyphylla (Table 5).

SDS- PAGE analysis

Total 12 bands were observed in the both of species, the phylogenetic relationship among the 2 species through phylogenetic tree has been shown in the (Figure 2). The phylogenetic tree was divided into two regions. R-I comprised of only genotype of Z. oxyphylla collected from (KP, Pakistan), while Regon II consists of genotypes of Z. muritiana collected from (KP, Pakistan) respectively.

Locus variation

Table 6 show interspecific variation among 50 genotypes of the Ziziphus (Z. oxyphylla and Z. muritiana) species. Among all the genotypes, 12 loci (L1-L12) were noted out of these L1, 6, 7 and 8 was monomorphic and were marked as generic specific which is used to discriminate the Ziziphus species. Moreover, the loci L-2, L-4, L-5, L-9, L-10, L-11 and L-12 marked as polymorphic with 34, 70, 68, 70, 50, 50, 50 and 50 percent genetic diversity, respectively. The inter species comparative locus contribution toward genetic disagreement (CLCTGD) was 66.66% in the two species of 50 Ziziphus genotypes (Table 6 and Figure 2). Intraspecific locus variation among 25 genotypes of Z. oxyphylla is represented in Table 7, Notably, L-9, 10, 11, 12 were absent in Z. oxyphylla. L-1, L-6, L-8 was monomorphic in Z. oxyphylla. While L-2, 3, 4 and L-5 was polymorphic and the locus contribution toward genetic disagreement (LCTGD) of Z. oxyphylla was 41.66% (Table 7).

The Table 7 represents the intraspecific variation among the 25 genotypes of Z. muritiana, exhibited high intra-specific locus variation. Among 12 loci, out of which L-1, 7, 8, 9, 10, 11 and 12 was monomorphic, while L-2, L-4, L-5and L-6 were polymorphic. The locus contribution toward genetic disagreement (LCTGD) of Z. muritaiana was 25% (Table 7).

    Discussion

Many of the local communities in the regions depends on the consumption of medicinal plants to use for the control/use against different number of diseases, However, the vanishing of these plant species is steadily reported chiefly due to fluctuations in the environment, land degradation and unsustainable use of these plants; moreover, the expansion of invasive species has donated a lot to their disappearance [41]. Conservation of medicinal plant genetic diversity has freshly created a lot of attention in the tropics as a result of many years of misconduct, adverse environment as well as socio-economic changes. Population genetic theory expects that the reduction in the genetic diversity limits a species ability to keep pace with the changing selection pressure [42]. Plant species mainly the medicinal plants rely on the existing genetic diversity for constancy and survival under the ever-fluctuating environments [43]. Understanding medicinal plants species population genetic structure is vital for their conservation, planning and justifiable organization [44].

Therefore, a common goal line of conservation is to preserve genetic diversity in “red listed” species, which is crucial for longterm survival and evolutionary response to the altering environment [45]. One main implication of this method, from the viewpoint of conservation genetics, is that it could help us set sampling intervals of areas within populations to optimize the genetic diversity in collections from local populations of rare, endangered, or endemic plant species [46]. For the purpose of conservation of plant species, most of the investigations deal only with determination of genetic diversity in individual populations [40].

The purpose of this study was to recognize the phylogenetic relationship, genetic diversity, genetic structure, and a core collection of Ziziphus species. Now, we explain our consequences with respect to genetic diversity and the causes of the genetic idleness. The current position of genetic structure is briefly debated. Moreover, we further clarify the competence of the plan used to build the core collection. Genetic redundancy is a significant issue in plant genetic resource management. The identification of duplicates is important in germplasm repositories, particularly when considering the construction of core collections [40].

Various apparatuses are now presented for documentation of required differences in the genotypes, including morphological / phenotypic, biochemical and molecular markers [40]. Though morphological description is the principal step in the description and alliance of crops genotypes, but these are highly subjective by the environment [40]. The double data matrix of 50 genotypes based on morphology was analyzed for the construction of phylogenetic tree to represents the similarity of various species or genera and the two species of the Ziziphus were investigated for similarities and the phylogenetic tree was constructed (Figure 1). The phylogenetic tree divided the two species in three groups R1, RII and RII (Figure 2). R1 and RII consisted of total genotypes of Z. oxyphylla. While the RII was into composed of all genotypes of Z. muritiana. The similarity indexes for all the genotype of 2 species was 23.529% for Z. muritiana and Z. oxyphylla [47].

The two plant species under the genus Ziziphus study exposed that no two plants have similar protein banding patterns which demonstrates the presence of genetic diversity between these species. The presence of common bands/locus (L-1, 6, 7, 8) among these two Ziziphus species suggests their close genetic similarity and common ancestry [40]. Also, [40] accredited the appearance of a common locus/band in all individual in a population to the fact that the gene coding for the enzyme or protein does not differ. Due to High inter-species locus contribution toward genetic disagreement SDS-PAGE could be a reliable technique for identification of these two species, while intra-specie locus contribution toward genetic diversity was high in genotypes of Ziziphus oxyphylla (41.66%) as compare to Z. muritiana (25%).

Conclusion

Best of our current study was first time to investigated that the seed protein and ethno medicinal uses of wild Ziziphus species collected from different regions of KP, Pakistan, the genetic pool of dissimilarity within genotypes and Z. oxyphylla and Z. mauritiana as well, is due to selection as well as for the crops/plants species improvement and their conservation for the better studies of genetic diversity and its distribution in the wild Ziziphus species of the studies needed duet to their conservation and will be help greatly to labeling what to conserve as well as where to conserve and will be and will enhance our information and understanding of the taxonomy, origin and evolution of wild Ziziphus species (Z. oxyphylla and Z. mauritiana) respectively.

To know more about Journal of Agriculture Research- https://juniperpublishers.com/artoaj/index.php

To know more about open access journal publishers click on Juniper publishers

[ux-studio]

The A/B test benefits hugely by showing which solution works the best in live environment, during real use. Use it for an existing website like Facebook, for new design or feature ideas. Run two versions simultaneously in an A/B test, or run many more in multivariate testing. Make sure to have only one difference between them if running a simple A/B test with two variants. More differences will prevent defining the cause of the winner’s better results. A/B tests should objectively decide which solution works better. They just won’t show why; qualitative research will, which makes it a must. Read more about both quantitative and qualitative research methods here: https://uxstudioteam.com/ux-blog/ux-research-methods/?utm_source=Social&utm_medium=referral&utm_campaign=ownsocial

[emarawork2015]

Discover More About The Framework Component In Clutch

By Jason Ellis

Clutch framework should be an imperative hardware of higher framework packages, in whose capacity might be the exchange in mechanical powers between motors with different parts. They are applied in structural designing alongside avionics. It is a basic piece of technological capacity in automobiles like in VC clutch. Without this particular component, the vehicle does not shift easily. Most likely neither obviously engage how important it would be to have all the components within good, generating satisfactory condition. In short, it truly is designed to have all the energy produced by the actual engine towards the front, back or each axles, for needs regarding propulsion. The particular materials utilized for generating clutch system components ought to be long lasting together with high quality. Simply because coupling will be one portion of the whole gear rental which may be loaded along with major applications. Therefore, it is crucial address this unique important advancement component fittings particular components from which it will be produced. A great way in restoring feeling pocket component is really its repair. But, it may be easier in avoiding this method including saving this type of element, finding much better quality in addition longer lasting material. Verification of current condition of company economics may be conducted through security review. Results of the particular verification tend to be recorded through different techniques. Manufacturing procedure and methods can be evaluated not only based on the study, but also simply by indicators connected with process capacity, variance with process and also the position for process inside tolerance area. The production process will be considered as competent if the recommended values about capability indices are abided. Graphical procedures are regular in information. This is on the grounds that they might be straightforward and basically interpretable. Visual showcase is especially vital for clear information assessment and knowing relationship amongst individual element. A graph is actually a visual approach to showing multivariate info by means of two dimensional graph of quantitative variables showed starting from the identical point. The specific relative location and place of the reaction is typically uninformative. The aim of which study is going to be comparison advantages of supplied material that may then be used in often the manufacture through its ideal properties. Accordingly, specialists pick the best decision material to apply in the generation, hence bringing about a decrease the measure of contradictions of extreme item, pushing machine ineffectiveness and gadget wear. Using worn decreasing apparatuses from the assembling procedure prompts higher surface territory harshness of machined part. This specific outcomes in diminishing generation exactness. Surface high quality likewise affects not only additional production technology and but additionally an attaching profile of anticorrosion covering. With the suggested solution, scientists increase their own efficiency and even effectiveness whenever pressed. This chart had been applied to component which can be currently made of materials with assorted parameters specific. Component is usually manufactured in the business. In research amount of parameters could be used, nevertheless the radar graphs are effective when utilizing a limited quantity. The difference in between values noticeable of person materials together with required beliefs on each beam is a difference between the best and the real. Parameter using the largest space, deviation, generally requires an instantaneous attention. While reading charts is required to avoid screen that can possibly distort visible perception. Easy charts might be best. These are often the execution associated with brainstorming, all of us selected and place intervals regarding physical parameters.

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Choosing the Right UX Research Method

As more and more organisations become focused on creating great experiences, more teams are being tasked with conducting research to inform and validate user experience objectives.

UX research can be extremely helpful in crafting a product strategy and ensuring that the solutions built fit users’ needs, but it can be hard to know how to get started.  This article will show you how to set your research objectives and choose the method so that you can uncover the information you need.

When to do research

The first thing to know is that there is never a bad time to do research. While there are many models and complicated diagrams to describe how products get built, essentially, you’re always in one of three core phases: conceptualising something brand new, in the middle of designing and/or building something, or assessing something that’s already been built.

There’s plenty to learn in each of those phases. If you’re just starting out, you need to focus on understanding your potential users and their context and needs so that you can understand your best opportunities to serve them. In other words, you’re trying to figure out what problems to solve and for whom.  This is often called generative or formative research.

Once you’re actively building something, you’ll shift your focus to analysing the solutions that you’re coming up with, and making sure that they address the needs of your users. You’ll want to assess both conceptual fit and specific interactions quality.  We usually call this evaluative research.

When you have a live product or service, you’ll want to continue to assess how well you’re serving people’s needs, but you’ll also want to use research to discover how people change and how you can continue to provide value. At this point, you’ll be doing a mix of the generative type of work that is generally in the conceptual phase and evaluative work.

There is no cut-and-dried guide of exactly what methods to employ when, but there should never be a time that you can’t find an open question to investigate.

Determine your specific research objectives

At any given time, your team might have dozens of open questions that you could explore. I recommend keeping a master list of outstanding open questions to keep track of possible research activities, but focusing on answering just one open question at a time. The core goal of a study will determine which method you ultimately use.

If you need help coming up with research goals, consider things like:

the stage of the project you’re in

what information you already know about your users, their context, and needs

what your business goals are

what solutions already exist or have been proposed

or where you think there are existing issues.

The questions might be large and very open, like “who are our users?” or more targeted things like “who uses feature x most?” or “what colour should this button be?” Those are all valid things to explore, but require totally different research methods, so it’s good to be explicit.

Once you’ve identified open questions, you and the team can prioritise which things would be riskiest to get wrong, and therefore, what you should investigate first. This might be impacted by what project phase you’re in or what is currently going on in the team. For instance, if you’re in the conceptual phase of a new app and don’t have a clear understanding of your potential user’s daily workflows yet, you’d want to prioritize that before assessing any particular solutions.

From your general list of open questions, specify individual objectives to investigate. For instance, rather than saying that you want to assess the usability of an entire onboarding workflow, you might break down the open questions into individual items, like, “Can visitors find the pricing page?” and “Do potential customers understand the pricing tiers?”

You can usually combine multiple goals into a single round of research, but only if the methods align. For instance, you could explore many different hypotheses about a proposed solution in a single usability test session. Know that you’ll need to do several rounds of different types of research to get everything answered and that is totally OK.

Looking at data types

After determining your research goal, it’s time to start looking at the kind of information you need to answer your questions.

There are two main types of data: quantitative and qualitative.

Quantitative data

Quantitative data measures specific counts collected, like how many times a link was clicked or what percentage of people completed a step. Quantitative data is unambiguous in that you can’t argue what is measured. However, you need to understand the context to interpret the results.

For instance, you could measure how frequently an item is purchased. The number of sales is unchangeable and unambiguous, but whether 100 sales is good or bad depends on a lot of things. Quantitative research helps us understand what’s happening and questions like: how much, how many, how often. It tends to need a large sample size so that you can feel confident about your results.

Common UX research methods that can provide quantitative data are surveys, a/b or multivariate tests, click tests, eye tracking studies, and card sorts.

Qualitative data

Qualitative data is basically every other sort of information that you can collect but not necessarily measure. These pieces of information tend to provide descriptions and contexts, and are often used to describe why things are happening.

Qualitative data needs to be interpreted by the researcher and the team and doesn’t have a precise, indisputable outcome. For instance, you might hear people talk about valuing certain traits and note that as a key takeaway, but you can’t numerically measure or compare different participant’s values. You don’t need to include nearly as many sessions or participants in a qualitative study.

Common UX research methods that can provide qualitative data are usability tests, interviews, diary studies, focus groups, and participatory design sessions.

Some methods can produce multiple types of data. For instance, in a usability study, you might measure things like how long it took someone to complete a task, which is quantitative data, but also make observations about what frustrated them, which is qualitative data. In general, quantitative data will help you understand what is going on, and qualitative data will give you more context about why things are happening and how to move forward or serve better.

Behavioural vs attitudinal data

There is also a distinction between the types of research where you observe people directly to see what they do, and the type where you ask for people’s opinions.

Any direct-observation method is known as behavioural research. Ethnographic studies, usability tests, a/b tests, and eye tracking are all examples of methods that measure actions. Behavioral research is often thought of as the holy grail in UX research, because we know that people are exceptionally bad at predicting and accurately representing their own behaviour. Direct observation can give you the most authentic sense of what people really do and where they get stuck.

By contrast, attitudinal research like surveys, interviews, and focus groups asks for self-reported information from participants. These methods can be helpful to understand stated beliefs, expectations, and perceptions. For instance, you might interview users and find that they all wish they could integrate your tool with another tool they use, which isn’t necessarily an insight you’d glean from observing them to perform tasks in your tool.

It’s also common to both observe behaviour and ask for self-reported feedback within a single session, meaning that you can get both sorts of data, which is likely to be useful regardless of your open question.

Other considerations

Even after you’ve chosen a specific research method, there are a few more things you may need to consider when planning your research methods.

Where to conduct

It’s often ideal to be able to perform research in the context of how a person normally would use your product, so you can see how your product fits into their life and observe things that might affect their usage, like interruptions or specific conditions.

For instance, if you’re working on a traffic prediction application, it might be really important to have people test the app while on their commute at rush hour rather than sitting in a lab in the middle of the day. I recently did some work for employees of a cruise line, and there would have been no way to know how the app really behaved until we were out at sea with satellite internet and rolling waves!

You might have the opportunity to bring someone to a lab setting, meet them in a neutral location, or even intercept them in a public setting, like a coffee shop.

You may also decide to conduct sessions remotely, meaning that you and the participant are not in the same location. This can be especially useful if you need to reach a broad set of users and don’t have travel budget or have an especially quick turnaround time.

There is no absolute right or wrong answer about where the sessions should occur, but it’s important to think through how the location might affect the quality of your research and adjust as much as you can.

Moderation

Regardless of where the session takes place, many methods are traditionally moderated, meaning that a researcher is present during the session to lead the conversation, set tasks, and dig deeper into interesting conversation points. You can tend to get the richest, deepest data with moderated studies. But these can be time-consuming and require a good deal of practice to do effectively.

You can also collect data when you aren’t present, which is known as unmoderated research. There are traditional unmoderated methods like surveys, and variations of traditional methods, like usability tests, where you set tasks for users to perform on their own and ask them to record their screen and voice.

Unmoderated research takes a bit more careful planning because you need to be especially clear and conscious of asking neutral questions, but you can often conduct them faster, cheaper, and with a broader audience traditionally moderated methods. Whenever you do unmoderated research, I strongly suggest doing a pilot round and getting feedback from teammates to ensure that instructions are clear.

Research methods

Once you’ve thought through what stage of the product you’re in, what your key research goals are, what kind of data you need to collect to answer your questions, and other considerations, you can pinpoint a method that will serve your needs. I’ll go through a list of common research methods and their most common usages.

Usability tests: consist of asking a participant to conduct common tasks within a system or prototype and share their thoughts as they do so. A researcher often observes and asks follow up questions.

Common usages: Evaluating how well a solution works and identifying areas to improve.

UX interview: a conversation between a researcher and a participant, where the researcher usually looking to dig deep into a particular topic. The participant can be a potential end user, a business stakeholder or teammate.

Common usages: Learning basics of people’s needs, wants, areas of concern, pain points, motivations, and initial reactions.

Focus groups: similar to interviews, but occur with multiple participants and one researcher. Moderators need to be aware of potential group dynamics dominating the conversation, and these sessions tend to include more divergent and convergent activities to draw out each individual’s viewpoints.

Common usages: Similar to interviews in learning basics of people’s needs, wants, areas of concern, pain points, motivations, and initial reactions. May also be used to understand social dynamics of a group.

Surveys: lists of questions that can be used to gather any type of attitudinal behaviour.

Common usages: Attempting to define or verify scale of outlook among larger group

Diary study: a longitudinal method that asks participants to document their activities, interactions or attitudes over a set period of time. For instance, you might ask someone to answer three questions about the apps they use while they commute every day.

Common usages: Understanding the details of how people use something in the context of their real life.

Card sorts: a way to help you see how people group and categorise information. You can either provide existing categories and have users sort the elements into those groupings or participants can create their own.

Common usages: Help inform information architecture and navigation structures.

Tree tests: the opposite of card sorts, wherein you provide participants with a proposed structure and ask them to find individual elements within the structure.

Common usages: Help assess a proposed navigation and information architecture structure.

A/B testing: Providing different solutions to audiences and measuring their actions to see which better hits your goals.

Common usages: Assess which of two solutions performs better.

Christian Rohrer and Susan Farrell also have great cheat sheets of best times to employ different UX research methods.

Wrapping up

To get the most out of UX research, you need to consider your project stage, objectives, the type of data that will answer your questions, and where you want to conduct your research.

As with most things in UX, there is no one right answer for every situation, but after reading this article you’re well on your way to successfully conducting UX research.

The post Choosing the Right UX Research Method appeared first on UX Mastery.

by Amanda Stockwell via UX Mastery http://ift.tt/2neeoqd

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Solution Manual for Statistics and Data Analysis for Nursing Research 2nd edition by Polit

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The second edition of Statistics and Data Analysis for Nursing , uses a conversational style to teach students how to use statistical methods and procedures to analyze research findings. Readers are guided through the complete analysis process from performing a statistical analysis to the rationale behind doing so. Special focus is given to quantitative methods. Other features include management of data, how to “clean” data, and how to work around missing data. New to this edition are

updated research examples utilizinging examples from an international mix of studies published by nurse researchers in 2006-2009.

Table of Contents

1 Introduction to Data Analysis in an Evidence-Based Practice Environment

2 Frequency Distribution: Tabulating and Displaying Data

3 Central Tendency, Variability, and Location

4 Correlation, Crosstabulation, and Risk Indexes: Describing Relationships:

5 Statistical Inference

6 t Tests

7 Analysis of Variance

8 Chi Square and Other Nonparametric Tests

9 Correlation and Simple Regression

10 Multiple Regression

11 Analysis of Covariance, MANOVA, and Other Related Multivariate Techniques

12 Using Logistic Regression

13 Factor Analysis and Internal Consistency Reliability Analysis

14 Missing Values

Appendix A: Theoretical Probability Tables

Appendix B: Power Analysis/Effect Size Tables

Appendix C: Tips on Handling Missing Data

Appendix D: Answers for Selected Exercises

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