SPSS:比较多个模型的回归系数 [英] SPSS: Comparing regression coefficient from multiple models

问题描述

假设我将房屋价格作为因变量，将以下作为自变量:

Assuming I have price of houses as the dependent variable and the following as the independent variable:

1. 年龄
2. 面积
3. 地板
4. 步行到最近的火车站所用的时间 (time_walk)
5. 乘坐火车从最近的车站到 CBD 车站的通勤时间 (time_train)

有没有办法比较 time_walk 给定不同范围的 time_train 的系数.从本质上讲，我想要实现的是调查人们是否对步行的价值有所不同，因为地铁旅行时间发生了变化.

Is there a way to compare the coefficient of time_walk given different ranges of time_train. In essence what I would like to achieve is to investigate if people value walking differently, given a change in the mrt traveling time.

• 模型 A:(0-9 分钟 time_train):Walking_Time 将如何影响房价?

• Model A: (0-9 mins time_train): How would Walking_Time affects house pricing?

模型 B:(10-19 分钟 time_train):Walking_Time 将如何影响房价?

Model B: (10-19 mins time_train): How would Walking_Time affects house pricing?

模型 C:(20-29 分钟 time_train):Walking_Time 将如何影响房价?

Model C: (20-29 mins time_train): How would Walking_Time affects house pricing?

我知道我不能创建 4 个模型，每个模型只包含相关的详细信息(例如 0-9 分钟的火车时间，10-19 分钟的火车时间...等)，因为 n 数字会有所不同.这种比较系数估计是不公平的.

I understand that I can't create 4 models, each containing only the relevant details (eg. 0-9 mins train time, 10-19 mins train time ...etc) as the n number would be different. This comparing the coefficient estimates wouldn't be fair.

推荐答案

您的建议是 walking_time 的效果取决于 time_train.这是一个交互假设！如果time_train 是一个序数级变量，你可以用变量walking_time 为每个类别虚拟人添加交互项.如果 time_train 是一个连续变量，那么一个额外的术语就足够了(walking_time * time_train).

What you are suggesting is that the effect of walking_time is dependent on time_train. That's an interaction hypothesis! If time_train is an ordinal level variable, you can add interaction terms for each category dummy with the variable walking_time. If time_train is a continuous variable, one extra term is sufficient (walking_time * time_train).

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