一夜。統計學:相關分析
Summary
TLDRThis script introduces students to the statistical technique of correlation analysis, which is used to interpret the relationships between variables and to check for multicollinearity. It emphasizes the simplicity of correlation analysis and its role as a preliminary step before regression analysis in social science research. The script demonstrates how to perform correlation analysis using SPSS software, focusing on the relationship between three variables: corporate reputation, continuous commitment, and turnover intention. It explains the symmetrical nature of the correlation matrix and the significance of the diagonal elements being 1, representing perfect correlation with oneself. The summary of the correlation coefficients is suggested as a concise way to present the results, which is fundamental for further regression analysis and hypothesis testing.
Takeaways
- 📊 **Correlation Analysis Introduction**: The speaker introduces the concept of correlation analysis as a statistical technique to understand the relationship between variables.
- 🔍 **Understanding Variables**: Correlation analysis helps to determine if there is an association between variables before proceeding to regression analysis.
- 🔗 **Causality and Correlation**: In social science research, correlation analysis is used to preliminarily understand if there's a potential causal relationship between variables.
- 📈 **SPSS Software Utilization**: The script demonstrates the ease of performing correlation analysis using the SPSS statistical software.
- 📋 **Selecting Variables**: The example given involves analyzing the relationship between 'corporate reputation', 'continuous commitment', and 'job retention intention'.
- 🔢 **Correlation Coefficient**: The correlation coefficient table is symmetrical, with the diagonal elements representing the correlation of a variable with itself (always 1).
- 🤝 **Bivariate Correlation**: The 'bivariate' option in SPSS is used to calculate the correlation between two variables, but it can also handle more than two variables.
- ⚖️ **Symmetry in Correlation**: The correlation between two variables is reciprocal, meaning the correlation of A with B is the same as B with A.
- ✅ **Operation Simplicity**: Performing a correlation analysis in SPSS is straightforward, with minimal settings required.
- 📝 **Report Interpretation**: The output report from the correlation analysis provides a clear view of the relationship between selected variables.
- 📉 **Practical Application**: Correlation analysis serves as a foundation for further regression analysis and hypothesis testing in research.
Q & A
What is the purpose of correlation analysis?
-Correlation analysis is used to explain the association between variables and to understand if they have collinearity, which is essential before conducting regression analysis for further hypothesis testing.
Why is it important to check for collinearity among variables before regression analysis?
-Checking for collinearity is important because it can affect the stability and interpretability of regression coefficients, and it helps in identifying if the variables are suitable for further causal analysis.
What does the script suggest about the complexity of correlation analysis in mathematical terms?
-The script implies that while correlation analysis might seem complex mathematically, it is straightforward to perform using statistical software like SPSS.
How is correlation analysis conducted in SPSS according to the script?
-In SPSS, correlation analysis is conducted by selecting 'Analyze' from the menu, choosing 'Correlate', and then selecting 'Bivariate' to perform the analysis on the chosen variables.
What variables are used as an example in the script for demonstrating correlation analysis?
-The example in the script uses 'corporate reputation', 'continuous commitment', and 'job retention intention' as the variables for demonstrating the correlation analysis.
What does the script suggest about the diagonal values in a correlation matrix?
-The script explains that the diagonal values in a correlation matrix are always 1, representing the perfect correlation of a variable with itself.
Why are the values in the correlation matrix symmetrical around the diagonal?
-The values are symmetrical around the diagonal because the correlation between two variables is a mutual relationship, meaning the correlation of variable A with B is the same as B with A.
What is the correlation coefficient between 'corporate reputation' and 'continuous commitment' as per the script?
-According to the script, the correlation coefficient between 'corporate reputation' and 'continuous commitment' is 0.125.
How can the correlation matrix be simplified for reporting purposes in academic papers?
-The script suggests that for reporting in academic papers, one can simplify the correlation matrix by only including the left half or the right half, as they are symmetrical and provide the same information.
What is the primary reason for conducting correlation analysis before hypothesis testing in social science research?
-The primary reason is to preliminarily understand if there is a significant association between the variables, which is a prerequisite for further testing of causal relationships in social science research.
Outlines
📊 Introduction to Correlation Analysis
This paragraph introduces the concept of correlation analysis, a statistical technique used to understand the relationships between variables and to check for multicollinearity. It emphasizes the simplicity of this method and its role as a preliminary step before conducting regression analysis. The speaker explains that correlation analysis helps to determine if there is a certain degree of association among the variables, which is crucial before testing for causal relationships in social science research. The mathematical formulas may seem complex, but the process is straightforward in statistical software like SPSS. The example provided involves examining the relationship between three variables: corporate reputation, continuous commitment, and job retention intention. The speaker guides the audience through the SPSS interface, demonstrating how to select the variables and execute the correlation analysis to obtain a report.
🔍 Understanding Correlation Coefficients and Symmetry
In this paragraph, the discussion focuses on interpreting the results of the correlation analysis, specifically the correlation coefficients. The speaker points out the symmetry in the correlation matrix, where the right and left halves are mirror images, reflecting the mutual nature of correlation (i.e., the correlation between variable A and B is the same as between B and A). The diagonal of the matrix is consistently 1, representing perfect correlation with oneself. The audience is advised that for reporting purposes, it is sufficient to present only half of the matrix to illustrate the pairwise correlation coefficients between variables. The speaker also highlights the importance of correlation analysis as a foundational step for subsequent regression analysis and hypothesis testing, providing a helpful tool for students in their research.
Mindmap
Keywords
💡Correlation Analysis
💡Regression Analysis
💡Multicollinearity
💡SPSS
💡Bivariate
💡Correlation Coefficient
💡Corporate Reputation
💡Continuous Commitment
💡Job Retention Willingness
💡Statistical Report
Highlights
Introduction to correlation analysis as a statistical technique.
Correlation analysis explains the relationship between variables and checks for multicollinearity.
Correlation analysis is a preliminary step before regression analysis.
The importance of understanding variable relationships before testing causality.
Correlation analysis is mathematically complex but easy to perform in SPSS.
Demonstration of how to perform correlation analysis in SPSS software.
Example of analyzing the relationship between corporate reputation, continuous commitment, and job retention willingness.
Explanation of selecting variables for bivariate correlation analysis.
Clarification that 'bivariate' in SPSS refers to 'between' variables, not just two variables.
Instructions on how to execute correlation analysis in SPSS.
No additional settings are needed for correlation analysis in SPSS.
Interpretation of the correlation matrix output in SPSS.
The diagonal of the correlation matrix is always 1, representing perfect correlation with oneself.
Symmetry in the correlation matrix indicates mutual correlation.
Suggestion to simplify the presentation of the correlation matrix in academic papers.
Correlation analysis serves as a foundation for regression analysis and hypothesis testing.
Final remarks on the usefulness of the correlation analysis for students.
Transcripts
親愛的各位同學 現在要跟各位同學介紹的統計技術是相關分析
那相關分析呢 它是可以簡單的去解釋我們所有變數之間的關聯性
也可以瞭解他們是否具有共線性
好 所以相關分析 它其實是一個很簡單的一個報告資料 我們一般來講
為了做後續的迴歸分析
我們會先做一個相關 先去判斷我們的使用變數 中
有沒有一定的關聯度
如果有 我們才進一步的再去做迴歸分析 因為在我們的社會科學研究裡
我們的研究假設
是有一點類似像因果關係的邏輯
那當我們要去驗證因果關係之前
我們要先簡單的瞭解我們的變數 它有沒有相當的關聯性
如果有一定的關聯程度 我們再去判斷他們是否有類似的因果關係
好 那相關分析
在數學式 看起來好像有點複雜
不過 在spss的統計軟體中 它其實是非常容易操作的
我們現在就看一下我們的spss檔案
好在這個spss檔案裡面 我們假設
一個問題 我們想要知道 比如說
企業聲望
還有 持續承諾 留職意願 這三個變數彼此之間的關係
再講一次 我們想要知道
企業聲望 持續承諾
還有 留職意願 這三者的關係 我們要怎麼樣進行相關呢
一樣 我們選取起手式 分析
這個相關非常的簡單 因為在第一個下拉式的選單裡面 我們就看到了相關
好 這個相關你可以看到右邊的下拉式選單裡面有一個叫做雙變數
我們等一下就是會用這個雙變數來執行我們的相關分析
這裡的雙變數並不是只有兩個 而是between的意思
他指的是我們選取的變數 他會幫我們算出他們之間兩兩相關的相關係數
好 我們點選了雙變數之後 出現了這個工作視窗 我們剛才的命題是指
我們要計算 企業聲望 還有 持續承諾
以及 留職意願
這三者的關聯度 所以我們就把它選取到右邊空白的工作欄位
那相關分析並沒有什麼其他的設定 所以我們就直接按下確定 他就會跑出報表來了
我們 檢查一下我們的統計報表
他已經跑出了這個相關分析 好 我們接著來看一下這個相關分析的報表
他寫的是 correlation 相關
好 這個相關係數表呢 首先各位同學可以很
直接的觀察到 它有一個現象 也就是它的對角線
都是1 有發現嗎
為什麼對角線都是1呢 因為對角線是 自己跟自己的相關
當然是百分之百 也就是1 所以對角線是1 是一個非常正常的現象
好 我們接著再來看其他的數字 例如說 這邊有一個 叫做企業聲望
這裡有一個叫做 持續承諾
我想要知道 企業聲望 跟 持續承諾 他的相關有多少 我們可以看到這個表格寫的是0.125
0.125
好 那另外一種呢 是說我的 持續承諾
跟
企業聲望 的 相關 是多少呢 我們看到這個表格 他們寫的是0.125
好 所以你發現了一件事情
以這個紅色對角線為準
右半邊 跟 左半邊 他是相互對稱的 這也不難理解 因為
我跟你的相關
這句話 等於 你跟我的相關 所以這個相關的程度是一樣的
所以各位同學在論文上可以把這張表格稍微整理一下 我們其實只要留
左半邊或者是右半邊就足夠了 我們可以留
左半邊
這樣子也許就可以足夠呈現我們
兩兩之間變數的相關係數 好 這就是有關於相關係數的一個分析
他基本上非常的簡單 通常是為了後續的迴歸分析 還有研究假設的檢驗
先做的一個基礎
希望對各位同學有幫助 謝謝
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