This video provides an introduction to factor analysis, and explains why this technique is often used in the social sciences. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 172609 Ben Lambert
Dr. Manishika Jain in this lecture explains factor analysis. Introduction to Factor Analysis: Factor Loading, Factor Scoring & Factor Rotation. NET Psychology postal course - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Psychology-Series.htm NET Psychology MCQs - https://www.doorsteptutor.com/Exams/UGC/Psychology/ IAS Psychology - https://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm IAS Psychology test series - https://www.doorsteptutor.com/Exams/IAS/Mains/Optional/Psychology/ Steps in Research Proposal @0:24 Research Topic @0:43 Review of Literature @0:56 Rationale and Need for the Study @1:18 Definition of Terms @1:24 Assumptions @3:03 Method, Sample and Tools @4:06 Probability Sampling @4:23 Non - Probability Sampling @4:34 Significance of Study @5:13 Technique for Data Analysis @5:18 Bibliography @5:42 Budget @6:28 Chapterisation @6:39 #Expenditure #Tabulate #Significance #Assumption #Literature #Rationale #Constitutive #Phenomena #Elucidate #Literature #Manishika #Examrace Factor Analysis and PCA Reduce large number of variables into fewer number of factors Co-variation is due to latent variable that exert casual influence on observed variables Communalities – each variable’s variance that can be explained by factors Types of Factoring • PCA – maximum variance for 1st factor; removes that and uses maximum for 2nd factor and so on… • Common Factor Analysis – Same as factor analysis (only common variance – used in CFA) • Image Factoring – correlation matrix; uses OLS regression matrix • Maximum Likelihood Method – on correlation matrix • Alpha Factoring • Weight Square Estimate communalities - each variable’s variance that can be explained by factor. See factors are retained Factor rotation - Procedure in which the eigenvectors (factors) are rotated in an attempt to achieve simple structure. Factor loading - Relation of each variable to the underlying factor. Output of a simple factor analysis looking at indicators of wealth, with just six variables and two resulting factors 6 variables: Income, education, occupation, house value, public parks and crimes 2 factors: individual socioeconomic status and neighborhood socioeconomic status Factor Score – if value of variables are given then factor values can be predicted Interpretation
Views: 6286 Examrace
Determining the efficiency of a number of variables in their ability to measure a single construct. Link to Monte Carlo calculator: http://www.allenandunwin.com/spss4/further_resources.html Download the file titled MonteCarloPA.zip.
Views: 276096 TheRMUoHP Biostatistics Resource Channel
This webinar discusses the use of regression models that include both quantitative and categorical predictive factors. It begins with a discussion of the use of indicator variables in models with a single X and then moves on to multiple regression models. The inclusion of categorical factors in nonlinear regression, logistic regression, and life data regression is also considered. The webinar will be of interest to anyone who deals with data that is divided into groups and wishes to test whether regression relationships differ significantly between the groups. It will also be of interest to analysts who are developing predictive models that involve both quantitative and non-quantitative factors.
Views: 926 Statgraphics
Principal Component Analysis and Factor Analysis in SAS https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis
Views: 23023 econometricsacademy
Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 7926 nptelhrd
Principal component regression involves two steps. In the step 1 , predictor variables are combined through PCA algorithms (to create composite variables). Then these composite variables are used in the regression analysis instead of the original predictor variables. Contact :[email protected] ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 5242 Analytics University
For more information, please visit http://web.ics.purdue.edu/~jinsuh/analyticspractice-factor.php.
Views: 1043 Jinsuh Lee
Principal Component Analysis and Factor Analysis in R https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis
Views: 99691 econometricsacademy
The IBM SPSS Statistics Premium Edition helps data analysts, planners, forecasters, survey researchers, program evaluators and database marketers – among others – to easily accomplish tasks at every phase of the analytical process. It includes a broad array of fully integrated Statistics capabilities and related products for specialized analytical tasks across the enterprise. The software will improve productivity significantly and help achieve superior results for specific projects and business goals.
Views: 768 GeoEngineerings School
Learn how to include a categorical variable (a factor or qualitative variable) in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a brief overview of the topics addressed in this video:
Views: 66817 MarinStatsLectures-R Programming & Statistics
This video demonstrates conducting a factor analysis (principal components analysis) with varimax rotation in SPSS.
Views: 81463 Dr. Todd Grande
Download materials for exercise: https://github.com/jeromyanglim/r-vandenberghe-exercise https://github.com/jeromyanglim/r-vandenberghe-exercise/archive/master.zip Tutorial demonstrates how to perform several common analyses in psychology using R (i.e., exploratory factor analysis, confirmatory factor analysis using lavaan, correlations, and regression analysis).
Views: 5914 Jeromy Anglim
In this video, I demonstrate how to calculate internal reliability scores, means and standard deviations for test subscales based on FA, and also demonstrate how to put this information into an APA format table. If you are familiar with creating APA tables, please only view the first 12 minutes of the presentation.
Views: 4262 Peter O'Connor
Introduction to factor analysis/ principal components analysis including interpretation. Do I need to run a factor analysis (FA)? Questionnaires with inter-related questions, summarising content of lots of questions (items) by a few factors, creating scores for attributes, validity of a scale, checking a scale is unidimensional for Cronbach Alpha Types of FA: exploratory and confirmatory Steps in perfoming EFA Example: EFA on personaility data NOTE: somewhere in the video I say you can compute mean and standard deviations of the estimated factor scores. Well, you can, but it;s not meaningful. To see how people scored on a factor, a histogram or QQ plot would do.
Views: 66604 Phil Chan
What it is? The purpose of exploratory factor analysis is to reduce a larger set of questions into a number of factors of sub-dimensions. Imagine me getting all the students grades to my class for all their assessments, and then using an EFA to break them down into different factors, which may be “excellent students”, “moderate achievers”, “borderline passers” and “apathetic failers”. Why do we do it? We do it as usually we have sub-dimensions nested within an over-arching dimension or construct. We don’t usually know what these dimensions are yet though, so let the computer guide us, along with our judgement. For my PhD a component of this involved discerning the types of emotions consumer’s experience when they are exposed to an innovation. I started with about 60, and use EFA to reduce it down to a preliminary amount of about 30, spread across three factors or dimensions of emotions that related to eachother, but were distinct from the emotions of the other factors. We can then use these factor scores in regression analysis or other things such as grouping variables or mean analysis. How do we do it? To do this, we select ANALYZE, DIMENSION REDUCTION, then FACTOR. We move over into the “variables” window the items (variables) we want to assess. We then select rotation, and in this case select Direct Oblimin. This is an Oblique rotation used because it is likely the items we are using are related to each other. If we used a bunch of items that are not related to each other, for example age, income, purchase history, etc. than an Orthogonalized rotation such as Varimax is advised. We can then hit Ok and run the analysis. The output we want to view is the Eigenvalues table. Eigenvalues purely indicate how many factors SPSS has extracted. We want to explain as much variance in the data as possible, but only if our factors make sense and we don’t have too many. If an eigenvalue is above 1, then good to keep. In this case Explaining 71.04% of variance with 3 factors is good. We then move to the Pattern Matrix (as we are using Direct oblimin). I believe the Structure Matrix is viewed most commonly for Varimax rotation. Looking at this table shows our key results with the number of factors extracted expressed through the number of columns. And the items that belong to that factor on the left. However, this is a mess, and virtually illegible. We need to make the output easier to read. Going back to our analysis we select Options, and select Sorted by size, and then Suppress small coefficients below .300. Now the output will be much cleaner. From here we can see distinct factors emerging, with the items that belong within each of those factors. The numbers represent associations with other items, think of them like correlations. We can see that the items WITHIN a factor are strongly associated with other items within the factor, but show no association with items of other factors. This is because we have supressed any below .300. You could turn this off and would find a bunch, but they are negligible. The general rule of thumb is that you want an item to have an association of at least twice its largest association with any item of another factor, and be at least .600. This means that any association outside of the factor of .300 or above is problematic. By supressing those smaller than .300, we can be confident here that we have a clean EFA. The items of the factor reveal three sub-dimensions. It is up to the researcher to interpret these by closely reading the items, and naming them with a name that explains the underlying dimension. Factor 1 may be “Spare Resources for their Pet”, whilst factor 3 may be “Crazy Pet Person”. With such analysis, we always need to let good theory and conceptual insight decide our analysis. If we have a bunch of items in a factor that don’t seem to relate to eachother and are contradictory, perhaps statistically they make sense but not conceptually. This is not good. We may need to try removing items and seeing how the results come out again. Similarly if we have an item that crossloads strongly with other items, remove it, run again and review. EFA is an iterative an exploratory process that may require several rounds of analysis, reducing the number of items or potential dimensions as we go. There is no objectively correct answer for how the items should load.
Views: 599 Luke Butcher
In this video I demonstrate how to do a factor analysis in SmartPLS for either a formative or reflective measurement model. I now have an article published that cites this video. Paul Benjamin Lowry and James Gaskin (2014). "Partial least squares (PLS)structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it," IEEE Transactions on Professional Communication (57:2), pp. 123-146. http://www.kolobkreations.com/PLSIEEETPC2014.pdf
Views: 80497 James Gaskin
I describe what multicolinearity is, why it is a problem, how it can be measured, and what one can do about it. I also give guidelines for interpreting levels of tolerance and the variance inflation factor. For more information and references, check out: http://how2stats.blogspot.com/2011/09/collinearity.html
Views: 133909 how2stats
Topic 2. Introductory - advanced regression analysis, IRT, factor analysis and structural equation modeling with categorical, censored, and count outcomes. Recorded presentation at Johns Hopkins University, August 21, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic2Handout.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 580 Mplus
The factor analysis video series is available for FREE as an iTune book for download on the iPad. The ISBN is 978-1-62847-041-3. The title is "Factor Analysis". Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. The book can be accessed at: https://itunes.apple.com/us/book/factor-analysis/id656956844?ls=1 This video demonstrates the methodology for conducting factor analysis using SPSS.
Views: 17848 Lee Rusty Waller
This video demonstrates how to interpret multiple regression output in SPSS. This example includes two predictor variables and one outcome variable. Unstandardized and standardized coefficients are reviewed.
Views: 131996 Dr. Todd Grande
In this video I show how to fix regression weights greater than 1.00 in AMOS during the CFA. These are also sometimes called Heywood Cases.
Views: 20561 James Gaskin
Discover factor variables and a basic introduction to using them in regression models. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 48852 StataCorp LLC
In this video, we look at how to run an exploratory factor analysis (principal components analysis) in SPSS (Part 1 of 6). Youtube SPSS factor analysis Principal Component Analysis YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at how to run a factor analysis or more specifically we'll be running a principal components analysis in SPSS. And as we begin here it's important to note, because it can get confusing in the field, that factor analysis is an umbrella term where the whole subject area is known as factor analysis but within that subject there's two types of main analyses that are run. The first type is called principal components analysis and that's what we'll be running in SPSS today. And the other type is known as common factor analysis and you'll see that come up sometimes. But in my experience principal components analysis is the most commonly used procedure and it's also the default procedure in SPSS. And if you look on the screen here you can see there's five variables: SWLS 1, 2 3, 4 and 5. And what these variables are they come from the items of the Satisfaction with Life Scale published by Diener et al. And what people do is they take these five items they respond to the five items where SLWS1 is "In most ways my life is close to my ideal;" and then we have "The conditions of my life are excellent;" "I am satisfied with my life;" "So far I've gotten the important things I want in life;" and then SWLS5 is "If I could live my life over I would change almost nothing." So what happens is the people respond to these five questions or items and for each question they have the following responses, which I've already input here into SPSS value labels: strongly disagree all the way through strongly agree, which gives us a 1 through 7 point scale for each question. So what we want to do here in our principal components analysis is we want to go ahead and analyze these five variables or items and see if we can reduce these five variables or items into one or a few components or factors which explain the relationship among the variables. So let's go ahead and start by running a correlation matrix and what we'll do is we're going to Analyze, Correlate, Bivariate, and then we'll move these five variables over. Go ahead and click OK and then here notice we get the correlation matrix of SWLS1 through SWLS5. So these are all the intercorrelations that we have here. And if we look at this off-diagonal where these ones here are the diagonal. And they're just a one because of variable is correlated with itself so that's always 1.0. And then the off-diagonal here represents the correlations of the items with one another. So for example this .531 here; notice it says in SPSS that the correlation is significant at the .01 level, two tailed. So this here is the correlation between SWLS2 and SLWS1. So all of these in this triangle here indicate the correlation between the different variables or items on the Satisfaction with Life Scale. And what we want to see here in factor analysis which we're about to run is that these variables are correlated with one another and at a minimum significantly so. Because what factor analysis or principal components analysis does is that it analyzes the correlations or relationships between our variables and basically we try to determine a smaller number of variables that can explain these correlations. So notice here we're starting with five variables, SWLS1 through five. Well hopefully in this analysis when we run our factor analysis we'll come out with one component that does a good job of explaining all these correlations here. And one of the key points of factor analysis is it's a data reduction technique. What that means is we enter a certain number of variables, like five in this example, or even 20 or 50 or what have you, and we hope to reduce those variables down to just a few; between one and let's say 5 or 6 is most of the solutions that I see. Now in this case since we have five variables we really want to reduce this down to 1 or 2 at most but 1 would be good in this case. So that's really a key point of factor analysis: we take a number of variables and we try to explain the correlations between those variables through a smaller number of factors or components and by doing that what we do is we get more parsimonious solution, a more succinct solution that explains these variables or relationships. And there's a lot of applications of factor analysis but one of the primary ones is when you're analyzing scales or items on a scale and you want to see how that scale turns out, so how many dimensions or factors doesn't it have to it.
Views: 63170 Quantitative Specialists
A complete thesis analysis for your guidance. It shows the step by step process of analyzing a research thesis. I have used all the important procedures to assess the information. The five independent variables in this paper are: Power Distance Collectivism Uncertainty Avoidance Masculinity Long-term Orientation and the dependent variable is Performance of Multinationals
Views: 173 Knowledge Abundance
This video provides a brief overview of how to use AMOS (structural equation modeling program) to carry out confirmatory factor analysis of survey scale items. The data for this video can be downloaded at: https://drive.google.com/open?id=1_VM6wOnBfUbpmkLyLXByVqpz3UKnRYqs Check out my blog at: https://mikesstatsblog.blogspot.com/
Views: 70429 Mike Crowson
Visual explanation on how to read the ANOVA table generated by SPSS. Includes step by step explanation of each calculated value. Includes explanation plus visual explanation. Newer Versions Available http://www.youtube.com/watch?v=UpbT_heNiBs Like MyBookSucks on FaceBook http://www.FaceBook.Com/partymorestudyless Playlist on One Way ANOVA http://www.youtube.com/playlist?list=PL3A0F3CC5D48431B3&feature=view_all
Views: 90106 statisticsfun
This video explains multicollinearity and demonstrates how to identify multicollinearity among predictor variables in a regression using SPSS. Correlation, tolerance, and variance inflation factor (VIF) are reviewed.
Views: 47040 Dr. Todd Grande
This is a model fit exercise during a CFA in AMOS. I demonstrate how to build a good looking model, and then I address model fit issues, including modification indices and standardized residual covariances. I also discuss briefly the thresholds for goodness of fit measures. For a reference, you can use: Litze Hu & Peter M. Bentler (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6:1, 1-55
Views: 406213 James Gaskin
Structural equation modeling (SEM), as a concept, is a combination of statistical techniques such as exploratory factor analysis and multiple regression. The purpose of SEM is to examine a set of relationships between one or more Independent Variables (IV) and one or more Dependent Variables (DV). Structural equation modeling is also known as ‘causal modeling’ or ‘analysis of covariance structures’. Path analysis and confirmatory factor analysis (CFA) are special types of SEM ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 220 Big Edu
This is a step by step guide to create index using PCA in STATA. I have used financial development variables to create index. . . . For more videos please subscribe to my channel.
Views: 15983 Sohaib Ameer
Dr. Manishika Jain in this lecture explains factor analysis. Introduction to Factor Analysis Types NET Psychology postal course - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Psychology-Series.htm NET Psychology MCQs - https://www.doorsteptutor.com/Exams/UGC/Psychology/ IAS Psychology - https://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm IAS Psychology test series - https://www.doorsteptutor.com/Exams/IAS/Mains/Optional/Psychology/ Exploratory Factor Analysis Measure underlying factors affecting the variables in a data structure without setting any predefined structure to the outcome Not based on prior theory Confirmatory Factor Analysis (2 Types) Confirm predefined components already explored Implemented for reconfirming the effects and possible correlations existing between a set of predetermined factors and variables Larger sample size Produces inferential statistics Traditional Approach Based on principle factor analysis rather than common factor analysis Knows more about factor loading Factor loading - Relation of each variable to the underlying factor. Exploratory Factor Analysis @0:19 Confirmatory Factor Analysis @1:08 Traditional Approach @1:53 Structural Equation Modelling @1:30 #Scrutiny #Occupation #Component #Implemented #Predefined #Variable #Structure #Analysis #Confirmatory #Exploratory #Manishika #Examrace
Views: 3591 Examrace
Principal Component Analysis and Factor Analysis in Stata https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis
Views: 103563 econometricsacademy
In this video I demonstrate how to input matrix summary data into SPSS to run an exploratory factor analysis. I demonstrate how to do this using summary data published in Bear et al. (2010). You can obtain a copy of the syntax file used in this video here: https://drive.google.com/open?id=1AoUDRtMFFFou2YzLPBwv6bSsIZVtXvl4 Additional info on using matrix input in SPSS analyses (albeit demonstrated using regression) can be found at the following links: https://www.youtube.com/watch?v=_c1wqGteXwc&feature=youtu.be http://core.ecu.edu/psyc/wuenschk/SPSS/CorrMatrix_Input2SPSS.pdf http://www.spsstools.net/en/syntax/syntax-index/regression-repeated-measures/regression-with-correlation-matrix-as-input/ For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 371 Mike Crowson
In this video, we look at how to run an exploratory factor analysis (principal components analysis) in SPSS (Part 6 of 6). Video Transcript: 78% of the variance, approximately, is accounted for in SWLS3 by that component. 65% of the variance is accounted for in SWLS4 by the component, and 50%, or half of the variance, is accounted for by that component in SWLS5. So these are all very good in practice, and 78% is really, very very high. And SWLS3 once again is "I'm satisfied with my life," that question. So this is a measure of the amount of variance accounted for, once again, in the individual items by the component. And in a one-component solution, it's just literally, the component loading squared. In solutions with two or more components, it gets a little more complicated, so you can't just square the component loadings and get that. But in a one component solution, it's a direct translation, so that's very nice. Now one other thing, when we're naming a factor or a component, we have to go ahead and name those components, because they don't come out with a name, all we know is that we have one component. So our job is to name that. And what we would do is we would look at each of these items, look at the actual text in those items, what is it that they're measuring, and then see which items load high on this component. And the items that load high would give us a good indication as to what that component's measuring. We would literally read those items, see what it is that they're measuring, and then what those items have in common, that's what we would name the component. Now in this case all of the items load very high on the component, so they're all contributing. But if we just look at the highest one for a moment, .89, SWLS3, once again SWLS3 is "I am satisfied with my life." So we could say this is life satisfaction. That's exactly what this is, it's the Satisfaction with Life Scale. And the other items too they all measure life satisfaction in one way or another. Just as a couple here, number one, once again "In most ways my life is close to my ideal." Number two is "The conditions of my life are excellent," and so on. So all these items are really getting at life satisfaction. But if one of these was very low, for example, SWLS5, then we wouldn't want to use that item in contributing to naming the component. So typically we close in factor analysis after getting all of our values, determining how many components to extract or retain, we look at our loadings and then we want to name our components. OK that's it for now. This concludes the presentation on running a principal components analysis in SPSS. Thanks for watching. YouTube Channel: https://www.youtube.com/user/statisticsinstructor Channel Description: For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td
Views: 19443 Quantitative Specialists