- 495
- 458 142
the outlier 73
India
Приєднався 22 лют 2021
How to perform Principal component Analysis (PCA) on LIKERT SCALE ITEMS for QUESTIONNAIRE using SPSS
In this video, we'll guide you through the step-by-step process of conducting Principal Component Analysis (PCA) on Likert scale items in a questionnaire using SPSS. This tutorial is perfect for research scholars looking to simplify complex data into meaningful components.
Key Highlights:
- Kaiser-Meyer-Olkin (KMO) Measure: Understand the importance of checking sampling adequacy to ensure PCA is appropriate for your data.
- Bartlett's Test of Sphericity: Learn how to test whether your data's correlation matrix is suitable for PCA.
- Eigenvalue and the K1 Rule: Discover how to determine the number of components to retain using the eigenvalue-greater-than-one rule.
- Rotation (Varimax): See how to apply rotation to make your components more interpretable.
- Interpreting PCA Results: Walk through how to interpret component loadings and label your components for further analysis.
By the end of this video, you'll be able to confidently run PCA on Likert scale items using SPSS, making your questionnaire analysis more robust and insightful. Don’t forget to like, share, and subscribe for more research tips!
Key Highlights:
- Kaiser-Meyer-Olkin (KMO) Measure: Understand the importance of checking sampling adequacy to ensure PCA is appropriate for your data.
- Bartlett's Test of Sphericity: Learn how to test whether your data's correlation matrix is suitable for PCA.
- Eigenvalue and the K1 Rule: Discover how to determine the number of components to retain using the eigenvalue-greater-than-one rule.
- Rotation (Varimax): See how to apply rotation to make your components more interpretable.
- Interpreting PCA Results: Walk through how to interpret component loadings and label your components for further analysis.
By the end of this video, you'll be able to confidently run PCA on Likert scale items using SPSS, making your questionnaire analysis more robust and insightful. Don’t forget to like, share, and subscribe for more research tips!
Переглядів: 140
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How to Calculate Z-Scores for SCALE variables in SPSS: A Step-by-Step Guide for Beginners
Переглядів 3314 годин тому
Z-Score: Overview What is a Z-Score? - A Z-score (or standard score) represents the number of standard deviations a data point is from the mean of a dataset. - It is a measure of how far and in what direction a single data point deviates from the mean. Applications of Z-Scores: 1. Standardizing Data: - Z-scores standardize different datasets, making them comparable by putting them on the same s...
LIKERT SCALE analysis and interpretation using Descriptive Statistics (FREQUENCY and PERCENTAGE)
Переглядів 64День тому
When analyzing a Likert scale using descriptive statistics, you can summarize the data using frequencies and percentages. This approach helps in understanding the distribution of responses across different scale points. Steps to Analyze and Interpret a Likert Scale 1. Data Collection: Collect responses on a Likert scale (e.g., 1 = Strongly Disagree, 5 = Strongly Agree). 2. Frequency Distributio...
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Переглядів 64День тому
Are you looking to streamline your research process? In this video, we'll walk you through the step-by-step process of creating a codebook in SPSS, an essential tool for organizing and understanding your data. Whether you're a beginner or an experienced researcher, this tutorial will simplify the process, making it easy to generate a clear and concise codebook for your project. Learn how to doc...
How to Rank Likert Scale Items using SPSS(For Research Scholars, MBA students and PHD Thesis)
Переглядів 50День тому
In this video, you'll learn how to effectively rank Likert scale items using SPSS. Whether you're a research scholar, MBA student, or working on your PhD thesis, this guide will walk you through the step-by-step process of ranking responses from Likert scales to analyze your survey data. We'll cover key techniques and best practices to ensure accurate and meaningful results. Perfect for anyone ...
How to easily import and automatically recode a 5-point LIKERT SCALE data from EXCEL to SPSS
Переглядів 2314 днів тому
A step-by-step guide to import, Recode and Analyze 5-point LIKERT SCALE data in SPSS
How to import, code and analyze 5 point LIKERT SCALE ITEMS from Google form into SPSS
Переглядів 4514 днів тому
To import, code, and analyze Likert scale data from Google Forms into SPSS, follow these steps: 1. Export Data from Google Forms: - After collecting responses, go to the Google Forms Responses tab. - Click on the Google Sheets icon to view the responses in a spreadsheet. - Download the spreadsheet as a .csv file by selecting File -Download Comma Separated Values (.csv). 2. Import Data into SPSS...
How to test the VALIDITY of a 5point LIKERT SCALE QUESTIONNAIRE by using Pearson CORRELATION in SPSS
Переглядів 17014 днів тому
In this video, we will delve into how to use Pearson's correlation method to assess the validity of Likert scale questionnaires in SPSS. Designed specifically for research scholars and PhD students, this comprehensive guide will provide you with the tools to enhance the accuracy and reliability of your research data. You will learn: - How to calculate Pearson's correlation coefficient to explor...
How to increase CRONBACH ALPHA: RELIABILITY ANALYSIS on 5-Point LIKERT SCALE (For Research Scholars)
Переглядів 11414 днів тому
Welcome to our channel! 📊 In this video, we'll guide you through a simple and effective method for performing reliability analysis using Cronbach's Alpha on a 5-point Likert scale. Whether you're a research scholar, student, or professional, this tutorial is designed to help you understand and improve the reliability of your data. 🌟 What You'll Learn: 1. What is Reliability Analysis? - Explanat...
How to analyze 5-point LIKERT SCALE in SPSS and INTERPRET the results in word-For Research Scholars
Переглядів 34714 днів тому
Analyzing and interpreting Likert scale data can be done using various methods. Here are three common approaches: Descriptive Statistics: - Mean and Standard Deviation: Though controversial, the mean can sometimes be used if you assume the intervals between points are approximately equal. The standard deviation can provide insight into the variability of responses. - Median and Mode: More appro...
SPSS Graphs: How to create scatterplots in SPSS
Переглядів 6921 день тому
Unlock the power of scatterplots in SPSS with this step-by-step guide! Scatterplots are essential for visualizing the relationship between two variables, helping you uncover patterns and correlations in your data. Whether you're new to SPSS or looking to refine your graphing skills, this tutorial will help you master scatterplot creation.
Transform Your Data: Step-by-Step Variable Recoding in SPSS
Переглядів 5621 день тому
Unlock the power of SPSS with our comprehensive guide to variable recoding! Whether you're a beginner or looking to refine your skills, this video walks you through each step of the recoding process in SPSS. Learn how to transform your data efficiently and accurately with easy-to-follow instructions and practical examples. In this video, you will discover: - The basics of variable recoding and ...
Step by Step guide to Principal Component analysis (PCA) in SPSS
Переглядів 6921 день тому
Welcome to our comprehensive guide on Principal Component Analysis (PCA) using SPSS. In this tutorial, we'll walk you through the entire process, from loading your wholesale price index data to interpreting the final results. You'll learn how to perform PCA, create and analyze scree plots, and build path diagrams. Whether you're a beginner or looking to refine your skills, this step-by-step gui...
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Переглядів 9621 день тому
Welcome to our deep dive into the fascinating world of election data analysis! 📊 In this video, we explore the 1992 Presidential Election dataset using advanced statistical techniques and decision tree analysis. Join us as we: 1. Perform an Exploratory Data Analysis (EDA) to uncover hidden patterns and trends. 2. Use the Chi-Square test to analyze the relationship between categorical variables....
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Переглядів 65Місяць тому
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Переглядів 136Місяць тому
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I have 7 variables, and 37 items, while performing PCA analysis, fixed number of factor ( how many should i keep, 7 or should i leave it empty for software to decide?
Let the sofware decide
Thank you so much.
All the best
Hi I got a value of log price in mine as -3.1 what does this mean?
hi, The log value -3.1 means that the price is significantly less than 1. This is because, on a logarithmic scale, negative values correspond to numbers between 0 and 1.
Just a few questions. While fitting a decision tree, isn't a node split into two nodes only? Here, specifically for medium income group, with respect to age, the node has been split into four nodes, instead of two. Also, the two terminal nodes at the extreme left provide the same value of the dependent variable, which is "Bad" credit risk, following the majority class rule. But weren't the two nodes supposed to provide two different values of the dependent variable? Otherwise these terminal nodes would not have been created since they are not providing any different prediction from the node from which they got created (because the goodness of split value is low for the mother node here). Same goes for the terminal nodes at extreme right. Is all this due to the CHAID algorithm being used here?
CART model supports binary splits. however chaid supports multiple splits
Thank u sir .🙏😀
Thank you!! 😊
Glad you liked it
V nice n informative
Good explanation and liked it very much
I am glad you found it useful, all the best!
Commendable 🎉🎉
Glad you liked it
Very helpful sir 🔥
Thanks
👍
Did Perot pull votes from Clinton or Bush?
The other way round!!!
Nice video Sir...very very useful 👍
thanks
Sir, can we use this factor as a variable for regression. Also, do we need to consider them as negative values or absolute values?
you can use the factor scores as variables
Also, When interpreting factor scores from factor analysis, it is important to use the scores as they are, including both negative and positive values. These scores indicate the relative positioning of observations along the factor dimensions. Converting them to absolute values would distort the interpretation and relationships identified by the factor analysis. Negative values are meaningful and represent observations that are below the mean of the factor, while positive values are above the mean.
Nice explanation 😊
thanks
Great👍👏 explanation sir
Thanks
Very important and helpful video..
thanks
Educative
Thanks for your kind words, Appreciate it
At 19:40 when u expain support @18% of the total samples, you say that out of 1000 customers 18% of them purchased Freshmeat, CannedVeg, SoftDrink and Dairy. Is it true that they purchased CannedVeg despite it being a False in the row? Thanks.
good observation .people have not purchased cannedved
Do we need to eliminate seasonality as we prepare data for Market Basket Analysis. For e.g., people may buy bread and butter in summer, versus the same people may buy bread and jam in winter. How would this affect the analysis? Or should I do a separate analysis for summer vs winter if I expect a strong seasonality signal? Thanks for ur views.
no need
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Mh
Thanks!
Thank you for this. My result is not displaying the KMO and bartlett result. Could there be any reason for this?
if you are using SPSS, pleasse go to analyze menu, dimension reduction, factor, this open up factor analysis dialog box. one you are inside factor analysis dialog box at the right hand side you have descriptive statistics tab. please click on descriptive statistics tab. this will open up a new dialog box . the last option here is KMO and bartketts test of shericity. please make sure you select this click ok. you should get the kmo result
if you are having issues please let me know
Excellent! Very clear and audible! Took your time to explain! Thank you so much!
Thanks All the best
i did survey, and had 4 study groups. in that case, is it possible to run PCA on SPSS
Yes you can go ahead with PCA
@@theoutlier7395 Could you help me with running PCA in SPSS? I'm analyzing a dataset with 4 study groups and 400 samples to find where risk factors cluster the most. Your guidance would be valuable!
The title is about homoschedasticity while the pressentation is about hetro😊
Hello my friend. I checked and made the Factor Analysis just like you, but within Excel there is alway just one Sheet. Can you help me so that I can see Heat Map / Eigen Value? I checked the descriptive statistic option in PCA but could not find it directly
Hi, i have created the factor loading and the other output by running analysis in SPSS. it is not part of the raw data provided
@@theoutlier7395 thank you very much for the clarification
All the best..
Nice video ...
Very clear
Thanks
Thank you for this video - very informative! I’ve only recently found Orange - it looks very interesting. Can you recommend anything for how to report these results as part of a research paper?
When it comes to research paper publication SPSS is preferred over orange. Orange output export capability is weak and sometimes a struggle.
Wonderful video.
Ma'am could you please explain how to do absolute principle component score - multiple linear regression (APCS MLR) modelling in SPSS.
sure
Wonderful explanation, very clear and precise. Thankyou.
Glad you liked it, All the best
great channel
Thanks, Glad you like it
What is the variable name of rc1, rc2, rc3 means mm
rotated component one, rotated component two
@@theoutlier7395 then Sir I have to remove the first three variable from my analysis and have to add this rc variable....is it? But in my paper what should I write about my factor/variable name, because this is different name variable from others variable name.
@@sureshmondal8702 you dont need to remove the original variables. Please look at the loading table to name the factors
You have to look at the highest loading for each component
Thanks for helping us finding the sample files!
All the best, Appreciate your comments...
Sir agar hum bar chart ya box plot ko use na kare. Toh sirf t test mein jo mean difference nikal k aya hai wo yeh dikha rha hai ki male ko jada wage mila hai ya female ko yeh kese pata kare? Sir yeh samjha dijiye na.
Thanks for the question, Please look at the first table namely group statistics table under t test section. You can clearly see that average female salary is 26031 and for males it is 41441. The difference between these two numbers is 15409 which is the mean difference
Since male employees are getting 41441 which is greater than female employees 26031. Male employees average salary is higher. Hopw that answers your question
I liked the way you explained the content, systematically and the summary at the end. Great ! Thanks for the explanation.
Excellent clarity...
super clear, this was so helpful! thank you!
thanks
Great 💯
thanks