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irispublishersfashion · 5 years ago

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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”.

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juniperpublishers-agriculture · 5 years ago

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Impact of Technology Adoption on Agricultural Productivity and Income: A case study of Improved Teff Variety Adoption in North Eastern Ethiopia-Juniper Publishers

The study investigates the potential impact of agricultural technology adoption, exemplified here by adoption of improved Teff (Boset variety) on rural household agricultural productivity and income. The research is motivated by two research questions:a. Why and how adoption of improved Teff variety affect the income of adopter farmers in the study area?b. What are the costs and benefits associated with the adoption of improved Teff variety?To answer these questions, the channels of impact are identified based on an extensive literature review and modeled using a household production function. This approach recognizes the interrelation of households’ producer, consumer and labor supply decisions and takes into account potential impact on income from non-farm activities.The study uses a cross-sectional data collected from a randomly selected 163 sample households from North Eastern Ethiopia. The analysis was conducted using a multivariate regression model, which was developed based on the household production function. The estimated result of a linear regression confirmed that adopter farmers have generated, 24% higher farm income from the resulted increase of agricultural output due to adoption. In addition, farm income of households in the survey responds differently to other production factors. The resulted change in farm income due to a unit change in land, capital and other seeds was significant and positive. However, the change in farm income, due to the change in other production factors labour, irrigation water uses, and fertilizers application was negative and insignificant. This could be due to existence of labour surplus, inappropriate application of fertilizer and inefficient utilization of irrigation water.The study also identifies farmers, who adopt improved seeds faced higher input cost (fertilizer and seed) and confirms that adoption had increased the production costs of farmers. Finally, the net impact of adoption calculated by combining the effect of adoption on the farming household’s farm income and its effect on cost of inputs confirms that the total benefit of adoption is far higher than the associated increase in costs.Keywords: Impact assessment; Technology adoption; Teff; Boset; Agricultural productivity; IncomeAbbreviations: ATA: Agricultural Transformation Agency; EIAR: Ethiopian Institute of Agricultural Research; MoA: Ministry of Agriculture; UN-OCHA: United Nations Office for the Coordination of Humanitarian Affairs; DA: Development Agent

Introduction

During the last decades, due to the expansion of agricultural farmland, there has been a rapid growth of agricultural production in Ethiopia. However, the use and contribution of modern agricultural inputs to overall agricultural growth is relatively low [1]. Given the scarcity of suitable arable land it becomes largely difficult to meet the increasing needs of the rapidly growing population through expansion of the area under cultivation [2]. It is, therefore, evident that, comprehensive efforts are required to increase agricultural production through different intensification and productivity enhancement mechanisms.The adoption of modern agricultural technologies is believed to improve the income of the smallholder farmers through enhancing agricultural productivity. And improving the agricultural productivity of farmers requires developing and disseminating cost-effective agricultural technologies. Accordingly, increasing agricultural production, reducing poverty and meeting the demands for food without irreversible degradation of the natural resource base are possible [3]. The theoretical case is in favor of agricultural technology adoption as a panacea for improving the income of smallholder farmers through closing agricultural productivity gaps. Therefore, it is important to study the impact of adoption on the farm households’ agricultural productivity and income empirically.As compared to the availability of literature on the factors influence adoption of improved agricultural technologies, studies assessing the impact of technology adoption are very scarce in Ethiopia. And the existing literature and studies are conducted either at the regional or national level. Furthermore, most of these impact studies are concentrated on Wheat, Cotton, Maize, and Rice varieties. On the other hand, as [4] Teff is relatively unknown somewhere else, [5] research with particular reference to the impact of improved Teff adoption is very rare. According to Agricultural Transformation Agency (ATA), Ministry of Agriculture (MoA) and Ethiopian Institute of Agricultural Research (EIAR), the focus of Teff research has been on breeding yet limited attention given to applied research, such as adoption and impact for many areas. In addition, the report calls for further research on several dimensions, including “Socioeconomics: adoption, impact, and a cost-value ratio of various inputs”. Based on these facts, this study attempts to assess the impact of adopting technology on farm households, agricultural productivity, and income, taking improved Teff (Boset Variety) adoption in Kobo woreda as a case study. Boset is a recently developed and released genetically modified high yielding Teff variety.In Kobo woreda, the north-eastern part of Ethiopia, Teff has extensive coverage of the area planted. But Teff productivity in Kobo is among the lowest in the region. For instance, the average yield of Teff is estimated 1.3 tons per hectare, which falls below the national average (1.47 tons per hectare) and is far from the potential yield of it (3.91 tons per hectare) [6]. And Kobo is also listed as high priority food insecure woreda for the United Nations Office for the Coordination of Humanitarian Affairs (UN-OCHA) [7]. Improved Teff varieties and different agronomic practices have been introduced to resolve the problem of low agricultural productivity, low income, and food insecurity in the area. This makes the woreda interesting to study.All the above reasons make worth to undertake this study. Therefore, it can contribute to the existing limited literature by bridges the gap of information with regard to the impact of improved Teff (Boset variety) on agricultural productivity and income of rural households. Thus, the study can serve as a reference material for policy makers, academicians and researchers. Moreover, this study can give a better insight into the role of modern agricultural technology in the productivity of the agriculture sector, hence, the income of rural households and poverty reduction.

Theoretical and Conceptual Framework

The link between improved teff adoption, productivity and income

Based on the review of the existing literature in the previous chapter the following transmission mechanisms through which improved Teff adoption will affect the agricultural productivity and household income are identified: a. Adoption of the technology (i.e. Boset Teff variety) is expected to have a positive influence on the agricultural production as it (Boset) takes less time to mature than local varieties. In addition, water-logging resistant and weed tolerant nature of the technology (Boset Teff variety), helps to increase yield by protecting the yield that would otherwise be lost due to logging and weeds. Therefore, adoption will lead to increased yields or intensive production practice and diversification of produced crops that may be used for own consumption and/or for being sold at the local markets. Hence, it will lead to an increase in farm income.b. Adoption of improved seeds is labor intensive. Consequently, it will affect the households’ labour time allocation. The increased yield resulted from technology adoption (Boset Teff variety) require the farmers to spend more labour time on their field to collect the harvest. While it is expected to increase the farm income, it will reduce the time allocated for other productive (non/ off-farm) activities there by income from these sources.c. In addition, the technology adoption (i.e., Boset Teff variety) increase expenditures for a group of farm households that formerly relied on local seed varieties. Further, adoption also changes the use of other inputs like fertilizer, pesticides, herbicides etc. Accordingly, the cost of production, including the transaction and transportation costs, will likely to increase.In order to assess the impact of improved Teff adoption on the income of farm households’, this study examines the economic costs and benefits of adoption. It is assumed that improved Teff variety adoption has an impact on agricultural production, reallocation of labour time and household expenditures. Being able to infer a causal connection between a project and an impact indicator depends both on the data that are used for the analysis as well as the empirical methods that are employed. In analysing agricultural production, the relationship between inputs and outputs or profitability is often examined through production or profit functions [8]. The model to estimate empirically all quantitative impact of changes in agricultural practice (i.e, improved Teff adoption) on production and income is modeled on/adopted from the productivity method /income function framework/ by Löwenstein et al. [9].

Productivity method

Productivity method is a revealed preference method which measures the change in productivity and income of the affected people by the given intervention [10]. The farm households in the area earn total cash income (Y) from different sources including farm income (Yfa), labour income from other productive (non/offfarm) activities (Yop), and also income from transfers (remittances and governmental subsidies) (Ytr). The farm household’s total income (Yttl) can therefore be expressed as follows:

Transfer income will not be affected by adoption of improved Teff. Income from other productive activities of the farm house holds in the area is also not affected by the adoption of improved seed (Boset Variety) due to agrarian nature of the area (where farming is seen as the main activity) and limited (none) existence of other productive activities in the area. Moreover, there is a strong cultural bias against non/off-farm activities in the area where farmers prefer to spend their time for leisure or other social activities than participating in other productive activities. Therefore, there is no need for further analysis for these income categories. On the other hand, farm income may be affected through the above described changes in intensive use of production factors and through the increased in productivity of farmland.In simple agriculture production function, the farm output (Xfa) will be produced by combining labor (L), land (Land), and physical capital (K). In the area, farming follows traditional patterns so that farming technology (A) can be assumed to be the same for all households. Farming is not mechanized, characterized by the use of the ard, a primitive ox-drawn plow, and it is more traditional and involves extensive manual work. Farmers in the area have access to irrigation and they use different local seed varieties and chemical fertilizers. Land preparation is done by using animal power. In addition, the use of simple agricultural tools such as picks and hoes is common. All these form the capital stock (K) of the household. More or less the capital stock of the households in the area is assumed to be similar and traditional.There is one part of the capital stock which is improved Teff (Boset Variety) that makes a difference in capital between farm households in the area. This element does vary between households that are adopted improved Teff and those who are using local varieties. Most producers in the area are smallholders, occupying on average less than a hectare of land per household. Having large family size with small plot size, we assume either a declining (zero) or positive marginal productivity of labour.Thus, the households farming production function contains the following explanatory variables.

Farmers generate gross income (PX) by selling their output to the nearest market for constant [11] market price (P). The farm income can be calculated by subtracting the individual household’s total production cost (Ci fa), which is a combination of fixed (Ci f) and variable costs (Ci v) from gross income. Thus, the households’ profits from farming activities can be expressed as follows:

Equation (3) reveals that the adoption of improved Teff varieties affects farm income of the households’ in two channels. One, it is expected that the adoption of improved seeds will have a positive influence on the agricultural production. Hence, the rise in farm yield resulted from the increased in capital stock (i.e. improved Teff varieties) will lead to an increase in the income of the farm household that is generated from the sale of the harvested output. The other channel is the expenditure channel. Farm income falls from increasing the costs of farming as those farmers who adopt improved seeds have to pay for the seeds and cover expenses for related complementary inputs such as fertilizer and also have to bear transportation cost of inputs.Boset Teff has a tall and tender stem which is susceptible to damage by wind and rain. Moreover, the grain holding per straw of Boset Teff is higher than the local varieties, which puts more pressure on the straw. The higher amount of seeds per Boset Teff straw makes it easier to fall to the ground, which causes considerable losses on both the quality and quantity of the harvest. Hence, adopter farmers are expected to apply more fertilizer to strengthen of the straw and control displacement of the steam from its upright position. Consequently, the woreda agriculture office in collaboration with the improved seed distributors of the area insists the farmers to buy the recommended fertilizer quantity while purchasing the seed [12].Inserting equations (3) into equation (1) and considering the assumptions, constant market price for outputs, the total differential of the modified equation (1) can be expressed as follows:

By using equation (4), it is possible to quantify the extent to which changes in agricultural technology (A), the production factors capital (K), labour (L), and land (Land) and the interaction with other income sources (other productive activities and transfer) are systematically affecting the households’ total income.

Application of the theory and conceptual framework to the research agenda

In assessing the economic impact of improved seed adoption on agricultural productivity and income of the rural households, the study uses the above theoretical and conceptual framework. In this study improved Teff variety used as a factor of production (i.e., Capital) that affect the production and productivity of the farmer. Productivity method that presented in 3.2 is used to estimate the magnitude of the impact (productivity and income) associated with the adoption of improved Teff. As described on Bockstael & McConnell [10], productivity method is used to analyses the economic impact of an input which increases revenue or reduces variable cost.This study focuses on the ex-post economic impact analysis of improved seeds adoption from the perspectives of smallholder farm households that have adopted improved Teff variety. It estimates the economic benefit and costs generated from the adoption of improved Teff varieties in monetary terms. The actual impact accrued by the smallholder farm households is attributed to improved seed adoption. In the analysis of economic costs and benefits, the viewpoint is very important [13]. Hence, this study evaluates the impacts of improved Teff adoption from the perspective of the smallholder farmers that uses it in their production. It compares the magnitude of economic benefit and cost of farm households between the two worlds, with improved Teff and without improved Teff. The differences between the real-world situation, i.e., the world with improved Teff varieties, and the counterfactual, i.e., the world without improved Teff with local varieties, is quantified and fed into equation (4) in section 3.2 to calculate the overall welfare impact of improved Teff adoption on the farm households in the area.

Working hypothesis

Based on the vast literature on the subject and theoretical and conceptual framework outlined in this chapter the following working hypotheses were tested:a. The adoption of improved Teff (Boset) variety increases the output of farm households which results in higher farm income. Hence, there is a strong case that farmers generate more farm income due to adoption of improved seeds.b. Technology adoption (i.e., Boset Teff variety) increase the expenditure of a group of farm households that formerly relied on local seed varieties. Therefore, their cost of production is likely to increase due to adoption.c. The total benefit generated in improved Teff adoption is greater than the total cost of adoption for the farmers in the study area. (the net welfare impact of improved Teff adoption is positive)

Methodology

Description of the area

The study is conducted in Kobo woreda which is located in the North Wollo zone of the Amhara region. It is located at 570km from the capital Addis Abeba and 49km from Woldia which is the zone capital. Agriculture is the main economic activity in the woreda in which about 86% of the population is engaged. The farming system can generally be characterized as mixed and includes the production of arable crops and the raising of livestock. Most of the farmers are engaged in subsistence agriculture with relatively small land holdings; which range from 0.25 to 2.5 hectares, and insufficient application of basic agricultural inputs such as fertilizers and pest control techniques. The main crops grown in the area are Teff, Sorghum, Maize, and other cereals from July through November. Due to the low rainfall amount and high rate of evaporation and transpiration during the Belg rain, there was no crop grown during this period i.e. farmers were producing once a year. But now, with the use of ground water since 2005, farmers are producing twice a year. In addition to the above cereals, cultivation of the most commercial crops in the country such as tomato, onion and pepper is possible during the dry season i.e. from March/April to June/July [14].

Research design

For the purpose of assessing the impact of improved Teff adoption on agricultural productivity and income in Kobo woreda, a cross-sectional research design was adopted to collect data related to the use of improved Teff varieties, production factors, output, total income and income composition from different sources and different socioeconomic and demographic characteristics of farm households in the woreda for the production year 2014/15. According to Bryman & Bell [15] “A cross-sectional design entails the collection of data on more than one case and at a single point in time in order to collect a body of quantifiable or quantitative data in connection with two or more variables, which are then examined to detect patterns of association [15].”The main challenge in assessing the impact of improved Teff adoption is to determine what would have happened to the farmers in the absence of improved Teff adoption. That is, determining the counterfactual will be necessary. For this specific study the “with and without world” scenario is adopted. The counterfactual is a world without the improved Teff, i.e. a world in which the adopter households grow local varieties, and where they use seeds from their last harvest or buy it in lower price. Then the study will use the comparison of the two worlds approach.

Data source and method of data collection

Analysis of this study is principally based on primary data. Primary cross-sectional data is collected for 2014/2015 cropping season using structured household survey questionnaire and to support this information focused group discussion with selected farmers has also been conducted.The data is collected from a group of farming households (having both adopters and non-adopter farmers) using the structured questionnaire prepared to gather information that helps to address the research question and finally to attain the research objectives. The questionnaire elicited information about household demography, household income, expenditure on inputs, crop production and resource endowment, etc. The data is collected from July, 2015 to August, 2015 with the help of 3 Development Agents (DA). The DAs were selected based on their experience and extended knowledge of the existing social settings of study area. One day training was given to the DAs. Before starting the actual data collection, the questionnaire was pre tested on 10 households who were randomly selected from the study area population enabling the modification of some of the questions. Close supervision and follow up was taken place by the researcher to avoid fault and mistakes and to do timely correction as much as possible. Furthermore, the study also used secondary data. Secondary data was collected form, Central Statistics Agency, Zonal and Woreda offices of agriculture, which is used to back up the findings from primary sources.

Sample size and sampling technique

In order to make valid inferences and increase the degree of accuracy of the results, a well-designed sampling frame is a pre-requisite. For this study, initially secondary data from the woreda agriculture office is collected and used to identify the population of the study area that can be possibly categorized as the sampling frame. In this study a two stages sampling technique was adopted for the selection of sample respondents (a group having both improved Teff variety adopters and non-adopters). In the first stage, from the total of 40 kebeles in the woreda, one kebele (kebele 08) is selected purposively based on the distance from woreda capital, relatively rural kebele which has better Teff production potential and high improved Teff (Boset) variety adoption rate (80%). The total farming household-head population size of the selected kebele (kebele 08) is 1,430 (i.e, total poplution for the study) of which 157 are women headed and the rest are male headed.At the second stage, based on the data (registration list of kebele 08 farmers) from the woreda agriculture office, and Ambasel Farmers’ Cooperative Union (distributor of improved seeds in the woreda) the actual improved Teff variety users in 2014/2015 cropping season were identified. Using the same data, a list (with both adopters and non-adopters) was prepared and households were assigned a random number, then a representative sample of 163 farming households (11.4% of the total population) were selected from the list using simple random sampling technique. From the selected 163 households 123 (75.5%) were adopter and 40 (24.5%) were non-adopter households.

Method of data analysis

Descriptive and inferential statistics were used to estimate the impact of improved Teff adoption on the sample households. Descriptive statistics such as tabulation, percentages, and frequencies were used to describe demographics, income and factor endowment of the sample population. In addition, chi-square test and t-test were used to assess if there are possible differences in our sample by differentiating adopter and non-adopter households. Multivariate regression models, based on the theoretical framework elaborated in section 3.2, is also used to analyse the output and income impacts of improved Teff adoption in the study area. STATA version 12 software package is used to analyse and estimate statistical and regression models.

Econometric method:

To analyses the impact of improved Teff adoption a linear multiple regressions analysis was used. As described in the theoretical framework of the paper the farm households in the area earn total cash income (Y_total) from different sources including farm income (Y_Farm), labour income from other productive (non/off-farm) activities (Y_Non_farm), and also income from transfers (remittances and governmental subsidies)(Y_Transfer). The theoretical framework also describes farm income as the total output produced by households multiplied by price minus farming cost (refer equation (3) of chapter three). Based on the assumption of constant price and fixed farming cost the total differential of equation [4] from chapter three gives a working model:

i = 1…n farming households

Definition of variables

a. ‘Y_total’ is the total annual income of households. This is cash income of households from different sources of income. It is the sum of income from farm activities, income from other productive activities and income from different transfers in one year.b. ‘BOSET_SEED’ is the amount of money spent on improved Teff seed for each household per cropping season. It is measured in Ethiopian birr. The improved Teff variety in this study stands for using Boset variety. This variable is used to estimate the impact of Boset variety on the selected outcome variables.c. Adoption of technology is a mental process of applying a given innovation. There is no universal agreed length of time to say households as adopters or non-adopters. In this study adopters are farmers who use improved Teff (Boset variety) in 2014/2015 cropping season while non adopters are farmers who are experienced in growing of local Teff varieties. As many studies verify that adoption influence household wellbeing positively and significantly [16,17] and similar to these findings in this study it is hypothesized that adoption of improved Teff variety is expected to have a positive and significant impacts on productivity and household income.d. ‘LABOUR’ is the total labour days (either family labour or hired labour) spent on planting, weeding and harvesting. It is measured in terms of man days for 2014/15 cropping season.e. ‘LAND’ is the total area cultivated by the farm household for the 2014/2015 cropping season. It is measured in terms of hectares.f. ‘CAPITAL’ is the value of all physical capital (hoes and ploughs used for cultivation) for each household per cropping season. It is measured in Ethiopian birr.g. ‘FERTILIZERS’ is the amount of money spent on chemical fertilizers for a 2014/2015 cropping season. It is measured in Ethiopian birr.h. ‘SEEDS’ is the amount of money spent on other seeds (without Boset) for each household per cropping season. It is measured in Ethiopian birr.i. ‘IRRIGATION’ is the amount of money paid for irrigation water used by the farm households for the production year 2014/15. It is measured in Ethiopian birr.j. ‘Y_Non_farm’ is annual income of households generated through participation in other productive activities. Other productive activities in the survey refers both to self-employment in non-farm sectors such as petty trade, craft work/ carpentry, etc. or off-farm employment such as; daily labour, guard, etc.k. ‘Y_Transfer’ is annual income of households from remittances and government subsidies. This is mainly remittances received form family member abroad and, in the city, and subsidies form government.l. ‘AGE’ is a continuous variable referring to the age of the household head measured in years.m. ‘SEX’ is a nominal variable used as dummy where it equals to 1 if the household head is male and 0 otherwise.n. ‘DEP_ratio’ is household members below the age of 15 and above 65 divided by the total household between the ages of 15 to 65. It shows the burden on the productive part of the population.A counterfactual world is generated by assuming a world without the adoption of improved seed (Boset variety). In this simulated world, the beneficiary households do not adopt improved Teff (Boset variety) and are heavily dependent on local Teff varieties. The differences between real world, i.e. the with-boset world, and the counterfactual, i.e. the without boset world, were analysed using equation [1].

Results and Discussion

Socio-economic and demographic characteristics of sampled households

In general, the descriptive analysis of shows that there is no statistically significant difference in age, gender, education status, household size and dependency ratio between adopter and non-adopter groups of our sample. The descriptive statistics regarding the input and institutional services utilization by households gives an insight as to whether there is available difference in our sample households with respect to asset endowment, utilization of agricultural inputs and institutional services by comparing the two subgroups of our sample. Based on the analysis, there is no statistically significant difference in the asset ownership, landholding, labour days spent on farming activities, utilization of capital goods, use of irrigation water and fertilizers between the adopter and non-adopter households of our sample. However, even though it is not statistically significant, adopter households had invested higher amount of money on fertilizers than those who relied on local Teff varieties. Moreover, households with improved Teff (Boset) variety were reported to have invest higher amount of money on seeds (Boset and all other seeds) which is statistically significant at 99% confidence interval. Considering the utilization institutional services there is no statistically significant difference in access to credit and agricultural extension services between those two subgroups of our sample.

Hypothesis testing

To estimate the impact of improved Teff (Boset) variety adoption on the income of sampled households, linear multiple regression analysis based on the model presented in section 3.5 is conducted. In the first approach, the influence of the independent variables from equation (4) are used to estimate farm households’ total income (Y_total). The regression uses the stepwise approach starting with a model which contains the full set of independent variables that are then reduced to find the model with the best statistical parameters. In addition to theoretically discussed independent variables different control variables were added to the regression. These are age of the household heads, sex of the household heads and dependency ratio of the households. Table 1 summarizes the results of the regressions based on the working model presented in section 3.5.

The regression results in the above (Table 1) shows the contribution of each factor of production, non-farm income, transfers income and demographic variables towards change in total income of the household. Both models showed variation in total income due to change in different theoretical and controlled variables. The P value of the F statistics shows the overall model is statisti cally significant and the model fits the data very well. The adjusted R-squared value of Model 1 and Model 2 is 0.745 and 0.728 respectively. This means that Model 1 and 2 have relatively the same explanatory power to explain the changes in total income due to the change in independent variables.All significant variables in Table 1 shows the expected signs. In Model 1, household’s factors of production LAND, CAPITAL, SEEDS and the use of improved Teff (BOSET_SEED) plus its income from other productive activities (Y_Non_farm) and transfer income (Y_Transfer) are significantly different from zero and influence the household’s total income. On average, each additional Ethiopian birr investment on improved Teff (BOSET_SEED) and other seeds (SEEDS) increases the total income of the household by 46.268 and 4.654 Ethiopian birr respectively. Moreover, each additional hectare of land cultivated brings 12,702 Ethiopian birr additional income for the household. However, household’s application of chemical fertilizer (FERTILIZRS), its use of irrigation water (IRRIGATION) and the number of days that farmers spent on their farms (LABOUR) are found to be insignificant to change the total income. This means there is no change in total income due to the change in each respective input. Likewise, the control variables dependency ratio, age and sex of the households are statistically insignificant. Result is also similar in Model 2, where only explanatory variables that has systematic and significant influence on household’s total income are considered.So far, the study has examined the possible influence and signs of coefficients of the independent variables included in the above regression analysis. Afterwards the researcher analysed the potential transmission channels between improved Teff (Boset) variety and households’ welfare in a more detail and systematic manner to test the proposed hypotheses in section 2.4 of the paper. The analysis and estimation of the effect of improved seed (Boset) adoption on farm households’ welfare through different possible channels (i.e. Output and Expenditure), is done by applying a simulation approach. The simulation approach uses a counterfactual by assuming a world without the adoption of “Boset” variety. In this simulated world, the adopter households do not have access for “Boset” variety and are heavily dependent on the existing local Teff varieties. The differences between real world, i.e. the with- Boset world and the counterfactual, i.e. the without-Boset world will be simulated and compared.

The output channel: more farm income due to increase in agricultural production

Hypothesis I:

The adoption of improved Teff (Boset) variety increases the output of farm households which results in higher farm income. Hence, there is a strong case that farmers generate more income due to adoption of improved seeds.In order to test the above specified hypothesis and examine the impact of different production factors including Boset variety (BOSET_SEED) on households’ farm income the researcher estimates the farm income (Y_Farm) of the farm households’ based on the theoretical framework presented in section 2.2 of equation (3) and (4). The result of Stata output is summarized in the following table.

The regression results in the above (Table 2) shows the effect of a change in each production factor on the farm income of the households. The P value of the F statistics shows the overall model is statistically significant and fits the data very well. The adjusted R-squared value of the first model is 0.63 and the second model is 0.609. This means that, almost both models have the same explanatory power to explain the changes in farm income resulted from changes of one or more independent variables. The models were diagnosed for possible existence of multi-collinearity using VIF. The STATA output for VIF show that there is no significant collinearity between variables in both models. All the variables have VIF of less than 3 or TOL of greater than 0.1 with mean VIF of 1.42 in Model 1 and mean VIF of 1.39 in Model 2.The coefficients in the model shows the change in the outcome variable (Y_Farm) for a one unit increase in the predictor variable, keeping the remaining predictors constant. The estimated coefficient of the conventional agricultural input variable labour (LABOUR) shows a negative sign, and quite interestingly, chemical fertilizers (FERTILIZERS) and household’s use of irrigation water (IRRIGATION) also show a negative sign. This means that, a unit change in labour days on the field, amount of chemical fertilizers applied and amount the of water use affect the household farm income in the opposite direction with the extent of the respective coefficients. However, all these three inputs are statistically insignificant to affect the farm income of the households. The insignificance of labour is in line with the initially assumed and now confirmed hypothesis that the traditional agriculture practiced in the area might be characterized by labour surplus (cf. section 2.2).Fertilizers are insignificant may be due the currently existing blanket fertilizer amount (100kg per hectare) recommendation in the national and regional level which does not consider location and crop specific aspects. And “Such blanket fertilizer recommendations have negatively influenced chemical fertilizer efficiency and profitability since…fertilizer requirement is affected by soil moisture, soil fertility status, cropping history and cropping systems [18].” The insignificance of irrigation water uses to affect the farm income of households is may be due to ineffective and inefficient utilization of the water from the pressurized irrigation system.The other explanatory variables show the expected sign of directions. Hence, the most important determinant of farm income is area of land cultivated. On average, each additional hectare of land cultivated increases the farm income of the sample households by 13,214.35 Ethiopian birr keeping other variables constant. The use of improved Teff ( BOSET_SEED) variety also has higher impact on farm income. On average, 1 Ethiopian birr spent on ‘Boset’ seed brings about 45.18 Ethiopian birr change in farm income ceteris paribus. Looking in to the p value in the first model, the use of improved Teff (Boset), land physical capital and other seeds are statistically significant at 1% level of significance. However, labour fertilizers and irrigation are statistically insignificant in determining the value of farm income. Likewise, both income from other productive activities and transfer income are statistically insignificant to explain the change in farm income. This confirms our assumption in section 2.2 that there is no link adoption will affect the households’ labour time allocation in the area. As for the transfer income, it may be due to the small representation of households with transfer income in our sample households.The second regression model is also statistically fit except, for a small change in the coefficients of the previously statistically significant variables that are included in the second model. On average each additional Ethiopian birr investment of farmers on ‘Boset’ seed increases their farm income by 44.93 Ethiopian birr. In similar direction, increase in 1 hectare of land cultivated increases farm income by 11,740.21 Ethiopian birr. Furthermore, a one unit increase in capital stock increases farm income by 3.72 Ethiopian birr and each additional money spent on other seeds increases farm income by 4.55 Ethiopian birr. All production factors are statistically significant at 1% level of significance.After predicting the farm income of sample households’ using their real-world data and the coefficients from the above regression models, the result of comparison between the observed and predicted farm income is depicted in Figure 1 below.

According to the regression results presented in Table 2, a unit change in the amount of money paid for ‘Boset’ Teff variety contributes 45.17 Ethiopian birr under Model 1 and 44.93 Ethiopian birr under Model 2 to the households’ average yearly farm income. However, in the counterfactual world, i.e. a world without improved Teff, households do not have access to ‘Boset’ seed and they are entirely dependent on the available local Teff varieties. As local Teff varieties are susceptible to weeds and they take longer time to mature than ‘Boset,’ the sampled households’ would have produced less agricultural outputs in the counterfactual situation which in turn reduced their income generated from farm.In order to estimate the farm income of sample households, without improved seed (Boset), the study used the unstandardized coefficients from the regression estimation in the above models. As the result from the estimation shows, on average households’ farm income would fall from 27,561.31 Ethiopian birr per annum (empirically observed) by 6,590.30 [19] Ethiopian birr based on Model 1 and 6,555.29 [20] Ethiopian birr based on Model 2. This means on average farm income of the households increase by 23.7% - 23.9% from the counterfactual income due to adoption of improved Teff (Boset) variety. Therefore, the finding of study supports the hypothesis that the adoption of “Boset” variety contributed to the households farming income and households earned more income due to increase in agricultural production.The next step is predicting our population income. Since our samples are taken randomly from the total population of 1,430 farming households and it was known that adoption rate of ‘Boset’ seed in the sample kebele is 80% (cf. section 3.4) then we can easily calculate the impact of adoption on the total population. Thus, 80% of the total 1,430 households, i.e, 1,144 households are adopters. Extrapolating the above empirical results from the sample to those 1,144 adopter households, the total income effect of improved seed (Boset) adoption via the output channel is estimated to be from 7,499,251.76 to 7,539,303.20 Ethiopian birr per year.

The expenditure channel: less income due to additional input costs

Hypothesis II:

Technology adoption (i.e., Boset Teff variety) increase the expenditure of a group of farm households that formerly relied on local seed varieties. Therefore, their cost of production is likely to increase due to adoption.To this point the study discussed the gross benefits generated due to adoption of improved Teff (Boset) by farm households. In this section the study identifies and calculates the costs farming households’ incurred due to adoption of improved Teff (Boset) variety. The data on additional costs of adoption were collected from the sample respondents. They were asked if their production cost has changed in relation with the adoption of “Boset” variety and if yes to specify the type and amount of extra payment they made due to adoption. This helps to calculate the additional economic costs individual households incurred, if they are willing to adopt ‘Boset’ variety.Accordingly, on average all the sampled adopter households in the survey spent extra 83.29 Ethiopian birr on seeds. This may be due to the fact that the selling price of improved seed (Boset) is higher than the available local varieties in the area. Similarly, adopter households had paid 299.67 Ethiopian birr extra cost of fertilizer [21] than if they would have been used available local Teff varieties. The reason of this extra cost of fertilizer is due to the obligatory purchase of additional chemical fertilizers with improved seeds (Boset) from Ambasel Farmers’ Cooperative Union. Furthermore, sampled household farmers reported that there is no any other additional cost than the above stated costs due to adoption. Thus, the sum of the extra payment for seed and fertilizers which is 382.96 Ethiopian birr give us the average annual expenditure of our sample households due to adoption of improved Teff variety. Therefore, this finding is in line with our assumption of the expenditure channel and confirms our hypothesis that adoption of improved Teff (Boset) variety increases the expenditure of a group of farm households that formerly relied on local seed varieties.The next step is extrapolating the above empirical figure from the sample to those 1,144 adopter households, the total effect of improved seed (Boset) adoption on income via the expenditure channel is expected to amount 438,106.24 Ethiopian birr per annum.

Net welfare effect of improved Teff (Boset) adoption

Hypothesis III:

The total benefit generated in improved Teff adoption is greater than the total cost of adoption for the farmers in the study area. (The net welfare impact of improved Teff adoption is positive).

So far, the study estimated the possible benefits and costs of improved seed adoption through the output and expenditure channels. The next step it to calculate the overall effect of adoption on household’s welfare. This is done by subtracting all the total expenses from the total benefits of adoption. As the overall result from Table 3 shows the gross annual benefit of all the 1,144 adopter households in the area for production season 2014/15 is between 7,499,251.76 and 7,539,303.20 Ethiopian birr. On average, adopter households incurred additional cost of 382.96 Ethiopian birr on seeds and fertilizers. Hence, by multiplying the number adopter households (1,144) by the average additional cost of adoption (382.96), the total annual cost of adopting ‘Boset’ Teff is 438,106.24 Ethiopian birr. Therefore, the net annual benefit of improved Teff (Boset) adoption for production year 2014/15 is estimated from 7,061,145.52 to 7,101,196.96 Ethiopian birr. This supports the hypothesis that the total benefit generated in improved Teff adoption is greater than the total cost of adoption for the farmers in the study area (the net welfare impact of adoption is positive). The resulted changed in the average annual household farm income can be attributed to the adoption of improved Teff (Boset) variety adoption. Converting the this net increase in households income to the PCI level shows that, the adoption of ‘Boset’ variety increases the PCI of adopter household members by 1,425.34 [22] -1,433.43 Ethiopian birr (USD 67.34-USD 68.12) from the counterfactual situation.

Summary, Conclusion and Policy Implications

Summary and Conclusion

Agriculture is the main sources of Ethiopia economy and the people at large. Even if it is very important to the people at large and it contributes more to the GDP of the country, the sector has been still dominated by the smallholder and the level of production is very low due to less use of the modern technology and limited use of best agronomic practices. Especially the productivity of Teff crop is very low as compared to other cereal crops whereas the land allocation is the highest one as compared to other crops. To reverse this situation, a continuous emphasis is being placed by the Government on its policies on the viability of intensification of improved agricultural technologies and extension practices as a vital measure for increasing crop production. In order to reflect the impacts of such policy directions, evaluation studies are important. This study applied a theory-based impact assessment approach (i.e., productivity method) to evaluate the impact of technology adoption, exemplified here by adoption of an improved Teff variety, on agricultural productivity and income of farmers.The study used a simulation approach to calculate the impact of improved Teff (Boset) adoption on income of farm households. In this method a counterfactual world, i.e. a world without ‘Boset’ seed, is simulated using real world data. This helps to build a “credible counterfactual” in which the impacts of the adoption can be compared. It also helps to identify different channels that adoption affects the welfare of the adopter households. This approach is different from control/treatment group comparison and allows real world changes. Hence, it shows not only the changes that occur but also why the changes occur. As such this study is different from previous studies which only use treatment/control group comparisons and adds to the existing literature. The study uses a cross-sectional data collected from a randomly selected 163 sample households from keble 08 of kobo woreda. Using quantitative approach, the paper tested three hypotheses in line with the different research questions [23-26].The estimated result of a linear regression confirmed that adoption of improved Teff (Boset) variety has a significant impact on the farm income of adopter households. Adopter farmers have gnereated higher farm income from the resulted increase of agricultural output due to adoption. A simulation result shows that household’s farm income increases on average by 23.7-23.9 percent due to the use of ‘Boset’ variety compared to the counterfactual world where farmers do not have access to ‘Boset’ seed. In addition to ‘Boset’ seed, farm income of households in the survey also responds differently to other production factors. The resulted change in farm income due to a unit change in land, capital and other seeds was significant and positive. However, the change in farm income, due to the change in other production factors labour, irrigation water uses and fertilizers application was negative and insignificant. This could be due to existence of labour surplus, inappropriate application of fertilizer and inefficient utilization of irrigation water.The study also identifies additional costs associated with improved Teff (Boset) variety adoption and confirms that adoption had increased the production costs of farmers who grow ‘Boset’ variety. Adopter farmers spent more money on purchase of seeds and fertilizers than if they would have been used the availablel local Teff varieties. This extra paymet on seeds and fertilizers is due to the relatively higher price of improved seeds than the local varieties and the additional obligatory fertilizer purchase with those seeds from the local seed distributor.In testing the hypothesis that the total benefit generated in improved Teff adoption is greater than the total cost of adoption for the farmers in the study area (the net welfare impact of improved Teff adoption is positive) the researcher calculates the net impact of adoption by combining the effect of adoption on the farming households farm income and its effect on cost of inputs (i.e., additional costs of seed and fertilizers). Accordingly, the study confirms that the total benefit of adoption is far higher than the associated increase in costs. Using the sample result, the extrapolated annual net welfare effect of adoption on the study area was estimated between ETB 7,061,145.52 and ETB 7,101,196.96 which is equivalent to ETB 1,425.34 – ETB 1,433.43 increase over per-capita counterfactual income.Moreover, the empirical findings also show that all the sampled households covered in this study are mainly dependent on agriculture for their livelihood and have lower PCI than the national average. Thus, this increase in annual PCI stemmed from the increase in farm income of adopter households has a significant impact on their lives. In addition to the above findings, the study also tried to investigate whether there are systematic difference and similarities in characteristics of sampled households across adoption status using descriptive and inferential statistics.

Policy implications

The result of this study suggested that improved Teff adoption has provide tangible benefits to the technology adopters in terms of agricultural productivity and net farm income. As a country that has over 6 million farmers growing Teff, the scaling up of this practice to other areas will have a huge impact on the livelihoods of the majority of the poor. So, based on the findings of the study, the following recommendation are forwarded.During the field survey the researcher had learned that, there is late delivery and shortage of improved Teff seed in the area. This is due to the local distributor has lack of capacity in finance, human capital, sufficient and clean warehouse and efficient logistics system. Hence, the government and stakeholders should give technical and financial support for local distributors of improved seed varieties to make the agriculture extension effort more successful in the study area and at large in the region and the country level. Additionally, it is better to create an opportunity that multiplication and distribution of improved seed, to be done through the channels of out growers (farmers) and additional cooperative unions in the area.Due to the aggressive efforts of the government to intensify the use of agricultural technologies, the existing compulsory package of fertilizer with improved seeds is discouraging farmers from adopting improved Teff. Based on the analysis, the application of fertilizer was found not significant to affect the farm income of farm households in the study area. However, farmers are obliged to buy additional fertilizers with improved seeds which costs them additional payment and that reduce the benefit of adoption. It shows the current blanket recommendation of fertilizers is not profitable and the respective regional and woreda agricultural offices should give value for local knowledge and traditional soil fertility preservation mechanisms. And policies related to fertilizer application recommendation should take in to consideration the area and crop specific aspects. The recommended type and amount of fertilizer should be based on soil calibration results of each specific area.It is also important to note that the utilization of the pressurized irrigation scheme in the area needs further attention. The result of our farm income regression analysis shows the use of irrigation water was insignificant to explain the farm income of households. This shows there is inefficient utilization (overuse) of the water. Hence, there should be a mechanism that the woreda agriculture office and the area farmers’ cooperative union to work in cooperation to manage and control the efficient and effective use of irrigation water.

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statsonlineassignhelp-blog · 7 years ago

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major-final-project · 6 years ago

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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:

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evoldir · 8 years ago

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Graduate position: NicolausCopernicusU.PlantEvolBiology

Ph.D. opportunity in plant evolutionary biology Nicolaus Copernicus University, Torun, Poland The Chair of Ecology and Environment Protection (http://bit.ly/2pF11zj) is recruiting PhD student interested in studying evolution of morphological traits in plants at various time scales. The goal of this project is to contribute towards an understanding of how genetic architecture (the structure of genotype to phenotype mapping) affects the evolution of quantitative traits. According to quantitative genetics theory, the evolution of phenotypic traits depends on the strength of selection and the amount of genetic variation. However, part of this variation maybe constrained by correlations with other traits that are under conflicting selective regimes. In consequence, the ability to respond to selection (evolvability) may be limited, even if a trait has high heritability. However, the extent to which genetic architecture limits phenotypic evolution remains an open question. Likewise, it is unknown whether it affects evolution at the short time scale and is easily overcome by selection, or if the genetic architecture is an important long-term determinant of the direction of evolution. In this project we aim to answer these questions using two plant species from the family Apiaceae, Daucus carota and Ferula communis, as a model system. This project will be carried out in collaboration with Prof. Thomas Hansen (University of Oslo) and dr Krzysztof Bartoszek (Uppsala University). Major tasks: - phylogenetic analysis of various taxa from the family Apiaceae based on RADseq data - establish a database of phenotypic traits (seedlings, fruits, inflorescences and flowers), life history strategies and geographic distributions for species from the family Apiaceae - estimate the rate of morphological trait evolution using phylogenetic comparative methods - help in developing the R package mvSLOUCH dedicated to analysis of multivariate Ornstein-Uhlenbeck models on phylogeny The ideal candidate will have a background in molecular biology/population genetics/phylogenetics, as well as experience working with Linux and modern programming languages such as R. Previous experience in generating and analysis of next-generation sequencing data will be considered positively. A condition of the application is a Master degree (or equivalent) in biology or similar subjects. The stipend for position is 3 000 PLN net monthly (app. 700 EURO) for three years. The living cost in Torun is low. For example, a room in a student house (inc. Wi-Fi, kitchen, heating, hot water) is app. 100-150 EURO monthly, loaf of bread 0.5 EURO, beer 0.5-0.7 EURO, beer in pub 1-2 EURO, dinner in restaurant 5-8 EURO. Nicolaus Copernicus University is located close to the medieval center of Torun (http://bit.ly/2r0zZpV) which is listed among UNESCO World Cultural and Natural Heritage sites. Torun is a dynamic academic city and provides many opportunities for intellectual and cultural stimulation. The Vistula river runs through town and is ideal for the naturalists as many natural protected areas are located in its valley. If you are interested, please send a CV, a short statement of your research interests (max. one page) and the contact details of at least one academic referee to Marcin Piwczynski ([email protected]). Feel free to contact him by email for further information. Review of applicants will start in the first week of August 2017. The positions will stay open until filled. Marcin Piwczynski Chair of Ecology and Biogeography, Faculty of Biology and Environment Protection, Nicolaus Copernicus University Lwowska 1, 87-100 Torun POLAND Marcin Piwczynski, PhD Personal website: http://bit.ly/2pFy8Tz Departmental website: www.keib.umk.pl e-mail: [email protected] Nicolaus Copernicus University Faculty of Biology and Environment Protection Chair of Ecology and Biogeography Lwowska Street 1, PL-87-100 Torun Poland "[email protected]" via Gmail

#EvolDir

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thedatasciencehyderabad · 4 years ago

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College Students This information science course in hyderabad demonstrates your proficiency in complex problem solving with probably the most sophistical know-how available in the market. The Data Science certificate is your passport to an accelerated profession path. 360DigiTMG is the best institute for data science coaching in Hyderabad. The course begins with an introduction to concepts in mathematics, statistics and knowledge science. Students obtain instruction on the earth's most popular languages - Python and R. Understand the time series elements, Level, Trend, Seasonality, Noise and methods to establish them in a time sequence information. In this primary module of forecasting, you'll be taught the applying of Model-based forecasting strategies. Artificial Neural Network model used to resolve the most complicated information where the sample can't be defined utilizing explainable models. Learn about Gradient Descent Algorithm and its optimization strategies to cut back the error to raised match the information. Learn to analyse the unstructured textual information to derive meaningful insights. Understand the language quirks to perform information cleansing, extract options utilizing a bag of phrases and construct the important thing-value pair matrix called DTM. Learn to grasp the sentiment of consumers from their suggestions to take acceptable actions. Advanced ideas of text mining may also be mentioned which help to interpret the context of the raw text information. Topic models utilizing LDA algorithm, emotion mining utilizing lexicons are discussed as part of NLP module. As a part of this module you study further totally different regression techniques used for predicting discrete data. These regression strategies are used to research the numeric data often known as rely data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression fashions try to fit the data to those distributions. The prerequisites for conducting a Hypothesis take a look at, interpretation of the results shall be discussed in this module. Learn about varied statistical calculations used to seize enterprise moments for enabling choice makers to make data pushed decisions. You will study in regards to the distribution of the information and its shape using these calculations. Understand to intercept information by representing information by visuals. Also find out about Univariate evaluation, Bivariate evaluation and Multivariate analysis. The premier modules are dedicated to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. This is probably the most enriching Data Science course in Hyderabad in terms of the array of matters lined. He should possess above average communication abilities and must be adept in speaking the technical ideas to non - technical folks. Data Scientists need a powerful foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and Modeling to construct successful algorithms. They should be proficient in instruments corresponding to Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS. Mostly all the coaching providers teach brief-term courses as well as lengthy-time period programs and could be hours roughly. You can go for Classroom training and Instructor LED online training either weekday morning, weekday evening or weekend course no matter suits you finest. Most institutes listed above provide flexible batch timings to fulfill the needs of working professionals as properly. The fees of institutes in Hyderabad depend on numerous factors like coaching hours, expertise and qualifications of the school. You can get in touch directly with the institute to negotiate the fee as per your finances and requirement for learning Data Science. The high sectors creating essentially the most information science course in hyderabad jobs are BFSI, Energy, Pharmaceutical, HealthCare,

E-commerce, Media, and Retail. Today large firms, medium-sized corporations and even startups are prepared to hire knowledge scientists in India. The 5 most sought after digital skills are Big Data, Software and User Testing, Mobile Development, Cloud Computing, and Software Engineering Management. Below is the Data Science course content in hyderabad utilized by the training institutes as a part of the Data Science course coaching. The Data Science course syllabus covers basic to advanced level course contents which is used by most of Data Science training courses in hyderabad . Training Institutes in Hyderabad – Tiihyd, India is well-known Software Training institute in Ameerpet, Hyderabad India. Listings of 36+ Data Science training institutes positioned near Ameerpet in Hyderabad as on May 12, 2021. More than 7.5 lakh verified Tutors and Institutes are helping millions of students every day and rising their tutoring business on UrbanPro.com. A lot of information science institutes additionally enable the candidates to make the cost in installments. The benefits embrace weekly assignments, mock exams, module clever projects and case studies, specially formulated examine material along with complete career steerage and placement help. In this module, you are also launched to statistical calculations that are used to derive information from information. Learn about insights on how knowledge is assisting organizations to make knowledgeable knowledge-pushed decisions. Data is handled as the brand new oil for all the industries and sectors which hold organizations forward within the competition. Learn the application of Big Data Analytics in real-time, you will perceive the need for analytics with a use case. Also, find out about one of the best project management methodology for Data Mining - CRISP-DM at a excessive degree. A module is dedicated to scripting Machine Learning Algorithms and enabling Deep Learning and Neural Networks with Black Box strategies and SVM. All the levels delineated within the CRISP-DMM framework for a Data Science Project are dealt with in great depth and readability in this course. Complete your software to take the 17-minute on-line eligibility test with eleven inquiries to kick-start the admission course of. The take a look at is designed to assess your quantitative & logical aptitude guaranteeing you are prepared for the programme. The mentorship through trade veterans, BaseCamps, and scholar mentors makes the programme extraordinarily partaking. I would definitely endorse the programme for its wealthy content material and comprehensive approach to Data Science. A gold medallist from IIM Bangalore, an alumnus of IIT Madras and London Business School, Anand is among the many high 10 data scientists in India. Identity is verified based on matching the details uploaded by the Tutor with authorities databases. Python and R are simple to learn and maintain and due to this fact, Godsend to builders in Data Science. Alternatively, when extreme zeros exist within the dependent variable, zero-inflated fashions are preferred, you'll be taught the types of zero-inflated models used to fit excessive zeros data. As part of this module, you will proceed to be taught Regression strategies utilized to predict attribute Data. Learn about the principles of the logistic regression mannequin, perceive the sigmoid curve, the utilization of cutoff worth to interpret the possible end result of the logistic regression model. A Data Scientist have to be a person who loves taking part in with numbers and figures. A sturdy analytical mindset coupled with strong industrial information is the ability set most desired in a Data Scientist.

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ssipune · 5 years ago

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A Detailed Guide About MSC Applied Statistics

MSC applied statistics is a popular post-graduation program among mathematics students. Pursuing this course helps students to get lucrative job opportunities in private as well as the public sector. In this article, we are going to understand in detail about this program.

What is M.Sc Applied Statistics?

M.Sc in Applied Statistics is a full-time mathematics postgraduate degree. The duration of this program is two years. In it, students get in-depth knowledge of subjects-

Mathematics

Social sciences

Finance

Economics

What is the eligibility to pursue M.Sc Applied Statistics?

To pursue M.Sc. Applied Statistics-

You need to complete an undergraduate degree from a recognized university.

You must have maths as a compulsory subject in the undergraduate degree.

You must have at least 50% average marks.

Which subjects are covered in the curriculum of M.Sc Applied Statistics?

The following subjects are covered in the curriculum-

Year One - Semester One

Probability Distributions

Statistical Computing

Linear Algebra

Mathematical Analysis

Sampling Theory

Year One - Semester Two

Statistical Inference

Probability Theory and Applications

Linear Models

Multivariate Statistics-1

Stochastic Processes

Year Two - Semester Three

Design of Experiments

Statistical Learning and Data Mining

Computer Intensive Statistical Methods

Multivariate Statistical Analysis-2

In the third semester, students can select a specialization course between the following-

Bio-Statistics and Data Analysis

Data Science

Industrial Statistics and Operations Research

Year Two - Final Semester

Common subjects in this semester are-

Statistical Machine Learning

Industry Project In Specialization

Scientific and Report Writing

The specialization course subjects are as follows-

Bio-Statistics and Data Analysis

Statistical Methods in Micro-array Data Analysis

Statistical Methods for Bio Computing

Analysis of Clinical Trial Data

Data Science

Big Data Analytics

Statistical Methods for Bio Computing

Statistical Methods for Quality Control

Industrial Statistics and Operations Research

Statistical Methods for Quality Control

Statistical Methods for Reliability

Stochastic Models in Finance

What are career prospects after completing M.Sc Applied Science?

After completing M.Sc. Applied Science, numerous opportunities open for you some of them are mentioned below-

Statistician

Statistician helps in solving problems that businesses, academia, industries and the government faces with the help of formulae and data. They do their duty by analyzing the data and applying various techniques of mathematics, as well as statistics.

Quantitative Developer

A quantitative developer is someone who develops a trading infrastructure for investment banks. They do this with the help of computer programming.

Quantitative Risk Analyst

A quantitative risk analyst applies the mathematical and statistical method to solve financial as well as risk management problems. Additionally, they develop and implement complex financial models that are used to make decisions on investment, pricing, etc. by financial institutions.

Conclusion

These are just some of the opportunities, pursuing masters in statistics in India would open a wide range of opportunities for you. These options would provide you with job satisfaction and lucrative salary option.

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t-baba · 7 years ago

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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

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

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

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

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

#UX Books #UX Conferences #UX Courses #UX Degrees #UX Games #UX Glossary #UX Podcast #UX Process #UX

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econometricians-blog · 7 years ago

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As a member of Data Science Central (DSC), American Economic Association (AES), Royal Economic Society (RES), International Health Economics Association (iHEA) and The Econometrics Society, I have been working closely with top academics in Economics, Econometrics, Statistics and Research Methods. Also, I am providing supervision in Applied Econometrics and Statistics to PhD candidates in Project Management, Business Management, Finance, Corporate Governance and Social Sciences.

I am professionally trained Econometrician and Data Sciences expert providing online courses in Stata, Eviews, SPSS, Nvivo10/11, WinRATS, SAS, GAUSS, Gretl, Minitab, C++, JavaScript and Python. I helped more than 1200 clients from around the world in applied econometrics, data sciences solutions and statistical analysis of data for research in corporate governance, financial performance, economics of international trade, business evaluation, Value at Risk, Options Pricing, Stock Evaluation, Pairs Trading and Backtesting using various econometrics packages. I completed my MS in Economics with specialization in Applied Econometrics Research from The University of Sheffield, UK.

I have a teaching and academic research experience of more than 11 years at a QS Ranked University. I teach modules in Economics, Statistics, Econometrics and Quantitative Analysis. Key themes and topics of my teaching are Qualitative Data Analysis, Factor Analysis, Principle Component Analysis, Power and Sample Size determination for Survival Studies, Analysis of Open ended surveys and interviews, Multivariate Time Series techniques in VAR/VECM, VARX, SVAR, Multivariate GARCH, ARDL and Bayesian Multivariate Time Series Methods. So far, more than 70 PhD and MS/MRes candidates completed their courses in Applied Econometrics and Applied Statistics under my supervision.

I welcome you to explore the above factors of my skills and experience from Econometricians Club and AnEconomist. I would also like a free demo in any course in the list, a proposed solution for your project using your own data and I will ensure you get the best outcome for your course or project on freelance terms and conditions.

Regards Muhammad Anees Assistant Professor Founder Econometricians Club Founder AnEconomist Founder Stata Pro Help

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matt41john · 8 years ago

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20 Best Master’s in Assessment & Measurement

As our world continues to digitize, the need to collect, analyze, interpret, store, and use data increases. In the education world, for better or worse, testing and data are driving decisions more and more as schools seek to be evidence-driven and governing bodies seek accountability. Who creates those tests? Who collects the data and what do they do with it? How is the data used and how do schools implement research into school policy? Or student learning? The answer is that a great many people do. Their job titles vary and include: Educational psychologists, statisticians, research analysts, or psychometricians. These data-interested, quantitatively-motivated souls design testing instruments and systems, gather and assess data, manage databases and other programs to evaluate data and/or prepare reports,conduct studies and surveys, and advise educational policy boards.

To qualify for work in assessment and evaluation requires graduate work. Historically the Master’s of Assessment, Measurement, and Evaluation was a psychology degree, however, with data now driving decisions, the assessment degree has expanded into other fields. The Master’s (MEd, MA, or MS) in Educational Assessment, Measurement, and Evaluation focuses on the application of data-analysis, assessment tools, qualitative and quantitative research, psychometrics, statistics, and evaluation in educational settings for the purpose of improving student learning and institutional effectiveness. Each of the nation’s 16,000 school districts and each of the 50 state education departments collects large quantities of data to assess the impact of schooling in its community; in addition to these there are many state and federal data-collection programs that are driving demand for highly-skilled analysts and directors.

What goes into a Master’s in Educational Assessment, Measurement, and Evaluation (EAME)?

A master’s (MEd, MA, or MS) in EAME is focused on the foundational knowledge and skills in assessing learning, both individually and institutionally, measuring and analyzing data, and evaluating data through testing, surveys, and other techniques of collecting it. The purpose of practitioners is to improve student learning by improving institutional effectiveness. Since the degree is interdisciplinary it draws from social science, research design, and psychology. Historically the EAME degree had a social science and psychology focus, however, now the demand for highly-skilled analysts has spilled into many areas from K-12 schools, institutions of higher learning, government agencies, testing companies, and the corporate world. Coursework includes classes in:

psychometrics

qualitative/quantitative measurement or research

statistics

multivariate analysis

classical test theory

designing evaluation instruments or technology

educational courses in ethics, assessment, or learning environments

A master’s degree in EAMA is 30-45 credits and usually involves a capstone research project or thesis where the knowledge learned is applied in a real-world scenario. Some degrees under the EAME umbrella are Educational Diagnostics, a specifically EAME degree for special education settings, mostly focused on individuals, and Curriculum and Instruction or Educational Leadership with an assessment focus.

What is the job outlook for an EAME degree?

Organizations such as the American Statistical Association and the National Science Foundation report jobs are plentiful and satisfying. According to the Bureau of Labor Statistics the field is projected to grow 27 percent from 2012 to 2022, much faster than the average for all occupations. The BLS states: ” Growth is expected to result from more widespread use of statistical analysis to make informed business, healthcare, and policy decisions.”

Graduate work in EAME can prepare you for a job as a researcher and analyst in several fields, including: academic institutions, state and federal agencies, school districts, the testing and evaluation industry, marketing research, and large-scale test management.

Possible jobs and median salaries (according to Payscale) include:

College and university professors – $84,632

Directors of institutional research – $79,480

Directors of assessment – $73,099

Research Analyst, staff members in research and evaluation divisions in public schools, government agencies, and private corporations and foundations.- $51,754

Psychometricians – $78,603

Educational Diagnostician – $57,857

What are best degrees in EAME?

The answer to this depends on what you’re looking for. The best degrees fit your needs and goals. Most people seeking an online EAME program are seeking career advancement, though the degree is accessible to those with little to no experience. If you’re seeking to move from a master’s to a PhD, be sure the degree is not terminal and lean towards MS degrees. If you want to move up in your job, a graduate certificate may work. If your skills are geared toward leadership, look for EAME programs that include coursework in leadership or educational administration. Those more inclined to dealing with hard-data, designing systems of data, be sure to check that coursework includes statistics, multivariate analysis, classic test theory, and psychometrics.

What is your methodology for ranking the schools on the list?

Our methodology relies on statistics collected by the National Center for Education Statistics found in the IPEDS database such as graduation rate, tenure, endowment per student, etc. In addition to IPEDS we utilize reputable ranking organizations such as U.S. News and World Report, the NCTQ, and Payscale.com to determine average salaries and job outlook, and for this ranking, the American Evaluation Association. Program descriptions are written based on the school’s website for the degree. Our researchers gather the most relevant information to communicate the most effective help to those seeking to find the best school

For the 20 Best Master’s in Assessment, Measurement, and Evaluation we looked at the school’s reputation, financials, and faculty for our overall score. The specifics of our criteria breakdown are as follows: 15 percent for each of two U.S. News and World Report rankings (Overall Score and Best Education Schools), 30 percent for tuition per credit, 30 percent for average mid-career salary, and lastly, as the faculty is one of the most important data-points in considering degrees,, the percentage of tenured professors from IPEDS.

We wish you the best of luck in your voyage of life and hope our ranking is a valuable map to your destination. Godspeed!

1.University of California – Berkeley

Serving approximately 37,500 total students and offering hundreds of majors, the University of California – Berkeley has something for everyone. Founded in 1868, with the vision to “contribute even more than California’s gold to the glory and happiness of advancing generations,” UC Berkeley is doing just that.

With faculty experts in data science, evaluation, measurement, and research methods, UC Berkeley’s MA in Social Research Methodology is academically excellent. The degree in Social Research Methodology leads to careers such as as an educational scholar and researcher in schools, colleges, and universities; non-profits, and corporations. From design-based research to ethnography and interviewing, to measurement, assessment and evaluation, theory, applied statistics, and the manipulation of large data sets, the program covers all the aspects of the field. Graduates will be prepared to shape educational policy, school curriculum, and teacher practices to increase student learning. Admission to the program includes a minimum GPA, a statement of purpose, personal history essay, three letters of recommendation, and GRE taken no later than a month before the application deadline.

Although UC Berkeley’s tuition is not cheap at $1196 per credit, it ranks #18 by U.S. News and World Report for Best Education Schools, has impressive 83 percent of faculty with tenure or on track for tenure, and the top mid-career salary is $174,000. The positives far outweigh the negatives and make UC Berkeley our #1 overall.

2. University of Illinois – Urbana-Champaign

Located in the twin cities Champaign-Urbana, University of Illinois enrolls over 40,000 students. Their mission reads: “With our land-grant heritage as a foundation, we pioneer innovative research that tackles global problems and expands the human experience.” In step with their mission, University of Illinois does just that.

The University of Illinois – Urbana-Champaign offers various degrees in Interpretive, Statistical, Measurement, and Evaluative Methodologies for Education, referred to as “Queries.” The Queries program focuses on these four specializations; faculty and students are concerned with developing and applying new methodologies and becoming involved with multiple research activities, both within the Department and the College, as well as across the larger campus. Upon graduation, students find positions in the public and private sector including measurement analysts, testing and evaluation directors, statistical specialists, and University faculty. Queries offers both a Master of Sciences or a Doctor of Philosophy through the Department of Educational Psychology.

The University of Illinois – Urbana-Champaign is ranked #24 in Best Education Schools by U.S. News and World Report and scored 63/100 in their overall score. The cost per credit is a low $452 and with $102,000 as an average mid-career salary, there is no doubt that the University of Illinois – Urbana-Champaign belongs at #2.

3. University of Iowa

With just over 31,000 students, the University of Iowa is one of the nation’s top public research universities, a member of the Big Ten conference since 1899, and an Association of American Universities member since 1909.

The MA in Educational Measurement and Statistics will qualify you to teach at the university level, analyze and interpret quantitative data, independently conduct research and communicate findings to various audiences, design assessments, and apply complex quantitative information in making policy decisions. The U of I degree is 32 hours and is offered with or without a thesis; a thesis is recommended for those intending to move into a PhD program in the field. The non-thesis version is offered completely online or on campus. Both thesis and non-thesis versions require a six-hour comprehensive exam. Admission requirements includes a bachelor’s degree, a minimum GPA of 3.00, a statement of purpose, three letters of recommendation, and GRE scores.

Ranked #43 for Best Education Schools by U.S. News and World Report and with the best ROI on our list, U of I may be the perfect fit for you to advance in your career or move into a different position fitting for your goals. The tuition is $504 and the mid-career salary is $120,000.

4. Ohio State University

As a Public Ivy, Ohio State University is bursting with academic options, service opportunities, extracurricular groups, and accolades. Research remains a focus with expenditures in the top 10 in the United States. OSU’s main campus is in Columbus, with others in Lima, Mansfield, Marion, Newark, and Wooster.

The College of Education and Human Ecology at OSU offers the MA in Educational Studies, Quantitative Research, Evaluation and Measurement (QREM). The program is suited for those with analytical backgrounds, though anyone with a bachelor’s can apply. There are two options for the QREM program: The 34-credit thesis option and the 31-credit non-thesis option. Courses such as Qualitative Research for Educators, Behavioral Research Methods in Applied Settings, Examining Knowledge, Truth, and Objectivity: Philosophy of Science, and Introduction to Measurement will prepare you to design, assess, implement, and improve schools’ data collection and use. Also notable is the active student body, alumni network, and graduate teaching opportunities at OSU.

When considering the $132,000 mid-career salary, an Ohio State degree becomes worth the investment. Ranked #18 by U.S. News and World Report for Best Education Schools and with a tuition of $1,104 tuition per credit propels the program to #3.

5. George Mason University

Located on three suburban campuses near the District of Columbia, George Mason University serves 33,900 students on 817 acres. It’s prime location just outside of Washington D. C. makes this school a hub for top-notch research and cutting edge educational techniques. The University also has a sizable commuter population.

The Department of Educational Psychology offers a 30-credit master’s degree program designed for professionals to apply the principles of learning, cognition, and motivation to problems in the area of education. They will use research, assessment, and evaluation methodologies to design and implement effective educational programs in a broad range of contexts. Upon completion of the program, students will have a Master of Sciences in Educational Psychology with a concentration in Assessment, Evaluation, and Testing. Classes include: Theories of Learning and Cognition, Education Research, Educational and Psychological Measurement, and Program Evaluation, just to name a few. There are several degree options with this focus including a Graduate Certificate, a bachelor’s with an accelerated master’s, and a PhD option.

George Mason University is ranked #62 in Best Education Schools by U.S. News and World Report and scored 39/100 in their overall score. The cost per credit is $763 and the average mid-career salary is $130,000, making the return on investment worth considering.

6. University of Washington

“Let there be light” is a fitting motto for the University of Washington. The UW is a multi-campus university in Seattle, Tacoma and Bothell, as well as a world-class academic medical center with an enrollment of 45,000 students. The University has 16 colleges and schools and offers 1,800 undergraduate courses each quarter.

The MEd in Measurement and Statistics prepares you to work in school districts, state governments, universities, non-profit agencies, and private companies. The MEd at UW is designed for individuals from a variety of backgrounds from social science to psychology who are interested in applying statistics, developing assessments, developing tests, and using psychometrics to improve schools and institutions. The program features 45 credits in Educational Foundations, Learning Science & Human Development, Measurement & Statistics, with nine credits for thesis/project.

UW’s impeccable reputation and marvelous #9 ranking for Best Education Schools from U.S. News and World Report are further bolstered by a good ROI; the tuition per credit $1,486 and the mid-career salary is $150,000.

7. University of South Florida

The University of South Florida, or USF, is a research institution with a main campus in Tampa, and regional campuses in Sarasota and St. Petersburg. Founded in 1956, today there are a total of 42,000 students.

The College of Education at USF offers M.Ed., Ed.S., and Ph.D. programs in Measurement and Evaluation as well as graduate certificates in Evaluation, Qualitative Research in Education, and Quantitative Research in Education. The program is designed to prepare students for leadership in the fields of educational policy and practice at the local, state, national, and international levels. Graduates go on to positions such as Directors of Accountability and Assessment, Supervisors of Testing, Evaluation and Research in school districts; faculty positions in public and private colleges, universities, and community colleges, research positions in state departments of education and other governmental agencies, and measurement positions in state and national testing organizations. Because of the variety of degrees offered, there are many paths for students to advance their education in this growing field.

The University of South Florida is ranked #93 in Best Education Schools by U.S. News and World Report and scored 36/100 in their overall score. The cost per credit is the lowest on the list at only $431 and the average mid-career salary is $114,000, making the return on investment one of the best.

8. Boston College

The first institution of higher learning in Boston was Boston College. The main campus is designated as a historic district and adorned with green spaces and paths surrounded by Collegiate Gothic architecture. The private Jesuit College is respected for academic excellence and research and enrolls 14,000 total students.

At Boston College, the Department of Educational Research, Measurement, and Evaluation (ERME) offers the M.Ed. in Educational Research, Measurement and Evaluation and the MS in Applied Statistics and Psychometrics; both degrees are 30 credits and take two to three years to complete. A distinct advantage to a BC education in ERME is the proximity to Greater Boston and the Northeast region and research centers on campus. The degrees both feature hands-on research, often with faculty. The MEd courses include Research Design, Statistics, Large Scale Data Collection, and Program Evaluation. The MS includes courses in: multiple regression and multivariate models, hierarchical linear models, and psychometric models. Admission to the program includes transcripts, a statement of purpose, resume, two letters of recommendation, and a GRE score.

One of the more costly degrees in terms of tuition at $1,420 per credit, BC makes up for it in average mid-careers salary at $105,000. Add to the ROI a #23 ranking by U.S. News and World Report for Best Education Schools, and a 70 percent faculty tenure and it’s clear BC deserves to be in the top five.

9. Western Michigan University

Home to 23,500 students, Western Michigan University is a public university that was founded in 1903. It is located in the city of Kalamazoo and offers more than 140 undergraduate programs, as well as many graduate degrees through the Hawarth College of Business, the College of Engineering and Applied Sciences, and the College of Education and Human Development.

The Master of Arts in evaluation, measurement and research in the Department of Educational Leadership, Research and Technology at Western Michigan University is designed to prepare you for staff positions in evaluation, testing or research units in schools or non-school organizations.

Students will take basic courses in applied statistics, evaluation, measurement, and qualitative and quantitative research methods. Completion of the program requires 27 credits, and a capstone portfolio experience. The portfolio will demonstrate the student’s ability to apply the principles and techniques learned from the core courses.

Ranked #120 for Best Education Schools by U.S. News and World Report and an overall score of 29/100, the University isn’t the highest ranked on the list, but the ROI makes it worth considering. The tuition is $554 and the mid-career salary is $98,500.

10. George Washington University

Founded in 1821, George Washington University educates 15,000 total students only four blocks from the White House. The School is particularly known for having over 12,000 internship opportunities in places like the White House, Bloomingdale’s, National Institutes of Health, and the World Bank.

The Graduate School of Education and Human Development’s MA in Assessment, Testing, and Measurement in Education is the “nation’s premier graduate training in psychometric and statistical methods taught by faculty who are engaged in world-class research.” The 30-credit program includes courses in foundational assessment, design of ATM models, and educational testing as well as a culminating capstone project and cumulative exam. Due to the fact that statistical measurement is now integral to corporate, educational and non-profit operations, having the MA in ATM will allow you to work in school districts, institutions of higher education, research-oriented organizations, federal research agencies, or nonprofit organizations. The ATM program is 15-months and is designed for individuals who are entering or advancing in positions.

An investment in GWU makes sense. The tuition is on the high end on our list at $1,655, however considering the average mid-career salary of $130,000 makes the ROI worth it. Ranked #38 by U.S. News and World Report for Best Education Schools you can certainly count on quality.

11. Florida State University

Florida State University football has a rich tradition reaching back to 1899; but education has an even richer one. Founded in 1851, FSU is the oldest continuous site of higher education in Florida. With an enrollment of 41,500 students, FSU is diverse and offers a vast array of studies. In fact, the School offers and incredible 341 degree programs.

At FSU the MS in Measurement and Statistics is offered in a thesis or non-thesis form.

This degree focuses on foundational knowledge and skills in measurement theory, statistical analysis, and evaluation. This degree can also complement a higher degree in a related discipline, such as Educational Leadership and Policy Studies, Educational Psychology, or Instructional Systems and Learning Technologies. Curriculum includes courses such as Descriptive/Inferential Statistics Applications, Multivariate Analysis Applications, and Measurement Theory. Students may choose a Thesis or Comprehensive Exam. Admission requirements include a bachelor’s degree with at least a 3.0 GPA, GRE scores, three letters of recommendation, and a personal statement.

Affordability and quality intersect at FSU. The cost per credit is $1,110 with a $107,000 average mid-career salary and the school ranked #52 in the country for Best Education Schools by U.S. News and World Report.

12. University of Connecticut

At the University of Connecticut, “Knowledge exploration throughout the University’s network of campuses is united by a culture of innovation.” This spirit has been driving the 30,000 student public, research university since 1881.

The Department of Educational Psychology and Neag School of Education offer the MA in Measurement, Evaluation, and Assessment (MEA). The MA in MEA is designed for specifically for educators and practitioners who wish to gain more knowledge and skill in the emerging field of measurement, evaluation and assessment, however, anyone with a bachelor’s is encouraged to apply. The degree moves through coursework focusing on fundamentals of MEA: Instrument development, classical and modern measurement theory and applications, item response theory, causal inference, multivariate statistical techniques, multilevel modeling, sampling methodology, and educational assessment. Generally, three semesters of full time study is required to complete the degree though duration varies depending on pace.

The strength of UConn’s program is their reputation for excellent academics. U.S. News and World Report ranked the school #27 for Best Education Schools and the program is recognized by other ranking organizations as well. The percentage of tenured faculty is 69, one of the best on our list. The tuition is $1,932 per credit.

13. University of Maryland – College Park

Founded in 1856, the University of Maryland the flagship and a land-grant institution of the UM System. The School’s 37,000 students have amazing academic choices with 250 academic programs and the tremendous advantage of having the Library of Congress, National Archives and the Smithsonian Institution just five miles away.

The MA in Measurement, Statistics and Evaluation (EDMS) program at UM will prepare you to serve as research associates in academic, government, and business settings. Since educational research relies on data collection instruments, students in the MA program will learn foundational and advanced skills in developing models and methods, sampling frameworks, and analyzing data. Something to note about UM is that full-time assistantships are available and renewable for those who qualify. Perhaps the most attractive feature of UM is their incredible location with DC just five miles away. Students have conducted projects or been employed by American Institutes for Research, The Census Bureau, State Departments of Education, National Education Association, U.S. Department of Education, Educational Testing Service, and Maryland Assessment Research Center for Education Success to name a few.

UM has the the third highest tuition on our list at $1,831 per credit, but the cost is offset by the long-term return; the mid-career salary is also the third highest at $144,000. With proven quality and a good reputation, UM will not disappoint.

14. Rutgers University

“Sol iustitiae et occidentem illustra – Sun of righteousness, shine upon the West also.” A fitting motto indeed for Rutgers University in New Brunswick, New Jersey. A member of the Association of American Universities, Rutgers is known for research and outstanding academics. Their rich history and tradition go back to 1766. RU has 31 schools and colleges, more than 300 programs and degrees, nearly 300 research centers and institutes and 69,000 total students.

Rutgers Graduate School of Education offers an Ed.M. in Educational Statistics, Measurement, and Evaluation (ESME). The degree trains you with the knowledge and skills for employment in a variety of fields; for example, researchers or data analysts in the fields of social science research, educational testing, marketing research, or pharmaceutical research. A sampling of coursework includes Statistical Methods, Psychometric Theory, Applied Multivariate Analysis, and Causal Modeling. The ESME program is 33 credits and does not require a thesis or comprehensive exam. Admission includes a bachelor’s degree, GRE scores, three letters of recommendation, and a personal statement.

Affordable tuition at $1,172, a $95,500 mid-career salary and a #52 ranking for Best Education School by U.S. News and World Report are attractive and nicely complement Rutgers respected reputation as an excellent institution.

15. Pennsylvania State University

Pennsylvania State University is a leader in teaching, research, and public service. The University’s influence is huge with 24 campuses, 100,000 students, a teaching hospital, the online World Campus, and “the largest student-run philanthropic organization on the planet.” Penn State is a Public Ivy and offers an world-class education across many fields.

The MS in Educational Psychology, Measurement and Evaluation is offered through the Department of Educational Psychology, Counseling, and Special Education. There are two tracks, thesis and non-thesis; the non-thesis is intended for those not seeking to move into a doctoral program. The 30-credit program includes coursework and a paper/thesis. Courses include Applied Statistical Inference for the Behavioral Sciences, Learning Processes in Relation to Educational Practices, Introduction to Educational Research, and Principles of Measurement. There are teaching and research assistantships available, though priority is given to those who apply for the MS/PhD program; hence, it would be advisable to apply and decide later whether moving into the PhD program is best.

In your search for the perfect fit, Penn State is worth a look because we are all looking for the highest quality for the best price. At Penn State the tuition is not cheap, but is affordable at $1,428 and the mid-career salary is $91,900 making the ROI favorable. As for quality, Penn State lives up to its Public Ivy reputation.

16. University of North Carolina – Greensboro

Founded in 1891, the University of North Carolina “defines excellence not only by the people we attract, but by the meaningful contributions they make.” The 19,000 students are offered 86 undergraduate, 74 master’s, and 32 doctoral programs. UNCG is recognized as for research, community engagement, and Weatherspoon Art Museum, one of the largest collections of American art in the country.

The MS in Educational Research, Measurement, and Evaluation (ERM) at UNCG is excellent. UNCG’s ERM program has two concentrations as well: Measurement and Quantitative Methods and Program Evaluation. The 33-credit program teaches foundational knowledge and skills amounting to a comprehensive education in educational research, measurement, and evaluation through coursework, practicums, hands-on field experiences, and a comprehensive exam. The program for those seeking career advancement or ambitions to move into doctoral work in fields that employ quantitative research methods. Classes are held during the evenings making this degree convenient, and with the two concentrations and multiple electives (six of the total 33 credits) there are good choices within the program to tailor it to your interests.

If you choose to earn your MS in ERM at UNCG, you will be pleased with the quality of program and the long-term investment. The School is ranked #52 by U.S. News and World Report, the tuition is $1,172 per credit, and the mid-career salary is $95,500.

17. Oklahoma State University

Oklahoma State University was established on Christmas Eve in 1890. Originally Oklahoma Agricultural and Mechanical College, OSU is a land-grant, sun-grant, coeducational public research university that has grown greatly since the first graduating class of six. OSU has more than 35,000 students across its five-campus system

The School of Educational Studies offers the MS in Educational Psychology, Educational Research and Evaluation (ERE) at the OSU Stillwater and Tulsa campuses. At OSU the focus for the ERE program is research. Students receive work very closely with faculty as instructors, mentors, and research partners. One particular example of this is that students in the program typically attend conference and present their research alongside faculty mentors. This real-world research experience is a definite strength of the program. Curriculum specifics of the 36-hour program include an 18-hour foundation in educational psychology, research inquiry, and a thesis, report, or creative component and an 18-hour focus on research, evaluation, measurement and statistics in educational settings.

Seventy one percent of OSU’s faculty are tenured leading to an assurance of high-caliber teaching. Tuition is one of the most affordable at $825 and the mid-career salary is $82,800.

18. Columbia University Teachers College

Columbia University Teachers College is the oldest and largest graduate school of education in the United States. Founded in 1887, it has been a Faculty of Columbia University since its affiliation in 1898.

Columbia University Teachers College offers a Master of Science in Applied Statistics and a Master of Education in Measurement and Evaluation. Both of these degrees offer classes like probability and statistical inference, applied regression analysis, linear models and experimental design, multilevel and longitudinal data analysis, and multivariate analysis, just to name a few. The degree culminates in a project that is determined by the student in consultation with their advisor. The M.S. in Applied Statistics requires at least one year of study and provides training for positions in applied research settings, testing organizations, and business organizations. The Ed.M. in Measurement and Evaluation is a two-year master’s degree and provides training for a positions in educational research bureaus and testing organizations.

Ranked #7 in the Best Education Schools by U.S. News and World Report, the $1512 cost per credit is worth it for those who desire the world-class education and renown that Columbia is known for. Graduates average mid-career salary is $73,400.

19. University of Toledo

Situated in Toledo, Ohio is the picturesque campus of the University of Toledo. Serving 23,000 students with a vibrant campus life of over 300 student organizations, the University offers over 300 degrees from undergraduate to graduate and professional degrees. UT is has nationally ranked programs in business, engineering, law, and occupational therapy.

The Department of Educational Foundations and Leadership at the Judith Herb College of Education (JHCOE) offers an MA in Educational Research and Measurement with specializations in measurement, program evaluation, qualitative research methods, and statistics. The MA will prepare you for employment in university teaching and research, educational institutions, business, and government, all of which are demanding highly-trained individuals in research, measurement, and statistics. The MA program is 36 hours and includes a thesis. Program outcomes include expertise in the design, execution, and interpretation of applied research and a deep understanding of the theoretical foundations of research and measurement.

UT’s faculty are experts and 73 percent are tenured. With confidence in quality, you can add and affordable tuition of $979 cost per credit and a mid-career salary is $83,100.

20. University of Wisconsin – Milwaukee

The University of Wisconsin–Milwaukee is the largest university in the Milwaukee metropolitan area and a member of the University of Wisconsin System. It is also the second largest university in Wisconsin.

UWM’s Department of Educational Psychology offers a Masters degree for students who are interested in specializing in educational statistics and measurement: Students may choose either a non-thesis or a thesis track. The non-thesis program provides basic research methodology coursework for students who are planning to go straight to work in positions that require educational measurement and research methodology. The thesis program is primarily intended for students planning to go on to doctoral studies. Students will complete 30 credits, including 12 credits of core courses, six credits in Learning and Development, and 12 elective credits, six of which must be in Educational Statistics and Measurement. Each student’s program of study is developed in consultation with an assigned advisor. A full-time student can typically complete the program in two years. In addition to course requirements, students must either complete a thesis or pass a final comprehensive examination.

The University is ranked by U.S. News and World Report Best Education Schools as #77, recognizing it as one of the top 100 in the nation. The cost per credit is the highest on the list at $2052 per credit hour, but students will be assured of a top-quality education as 70% of the full-time faculty is tenured or on tenure-track.

from Top Masters in Education http://ift.tt/2qRDvCE

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