The Use of E-Questionnaires in Organizational Surveys

2013 ◽  
pp. 1-23
Author(s):  
Yael Brender-Ilan ◽  
Gideon Vinitzky
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Human Resource ◽  
Principal Component ◽  
Point Of View ◽  
Future Research ◽  
Advantages And Disadvantages ◽  
Organizational Surveys ◽  
Exploratory Survey ◽  
Hr Managers

In recent years, there has been an increase in academic studies that examine the advantages and disadvantages of using e-questionnaires in organizations, but these studies have tended to ignore the potential differences between human resource (HR) managers and HR consultants with regards to using this tool. This chapter examines the use of e-questionnaires from the point of view of both types of practitioners. The study includes a qualitative exploratory survey, as well as a quantitative survey. T-tests, cluster analysis, and principal component analysis are performed and results support the three propositions that are presented. Specifically, it was found that (a) HR consultants and HR managers differ in the ranking of factors they think are important when deciding whether to use e-questionnaires; (b) preference differences exist between HR managers and HR consultants - managers are more directed by organizational constraints than consultants; and (c) the groupings for e-questionnaire preferences, compared to paper-and-pencil questionnaires, are consistent with Caldwell’s (2003) four roles of HR managers. The chapter concludes with implications and suggestions for future research.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

2021 ◽  
pp. 097215092110135
Author(s):  
Arif Hartono ◽  
Asma'i Ishak ◽  
Agus Abdurrahman ◽  
Budi Astuti ◽  
Endy Gunanto Marsasi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Depth Information ◽  
Factor Scores ◽  
Consumer Spending ◽  
Consumer Responses ◽  
Buying Behaviour ◽  
Manova Analysis

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic.

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

Assessing Teacher Manageability: A Factor Analytic Approach

1988 ◽  
Vol 13 (4) ◽  
pp. 245-252 ◽  
Author(s):  
Stephen P. Safran ◽  
Joan S. Safran ◽  
Robert S. Barcikowski
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Problem Behaviors ◽  
Principal Component ◽  
Analytic Approach ◽  
Future Research ◽  
Maladaptive Behaviors ◽  
Varimax Rotation ◽  
Behaviorally Disordered ◽  
Factor Analytic

An ecologically valid appraisal of students' problem behaviors must include assessment of the teacher's role as perceiver on various levels. This study analyzes the teacher manageability construct, examining educators' beliefs about their ability to manage 39 generally maladaptive behaviors within their own classroom. To address measurement limitations of previous investigations (including nonfactor analytic clustering of behaviors), a principal component analysis followed by a varimax rotation was administered on teacher manageability ratings (N = 182). This statistical analysis yielded nine component subtests (the Teacher Manageability Scale) and demonstrated that by changing the method used to group behaviors, you also modify the structure of a teacher checklist. Lack of Communication was identified as the most difficult behavior to manage. Implications for professionals working with students identified as behaviorally disordered and for future research are discussed.

scholarly journals Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

2012 ◽  
Vol 36 (4) ◽  
pp. 1073-1082 ◽  
Author(s):  
Mariana dos Reis Barrios ◽  
José Marques Junior ◽  
Alan Rodrigo Panosso ◽  
Diego Silva Siqueira ◽  
Newton La Scala Junior
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Magnetic Susceptibility ◽  
Clay Fraction ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Sao Paulo ◽  
São Paulo ◽  
Agricultural Potential

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

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