Interpreting Output for Multiple Regression in SPSS

Summary

In this informative video, Dr. Grande teaches viewers how to interpret multiple regression output using SPSS. With a fictitious dataset involving career limitations and work experience as predictors, and days until employment as the outcome, the video guides through the process of conducting a regression analysis. It explains the significance of R-square, ANOVA, and p-values, and how to understand the impact of each predictor on the outcome. The video concludes with a clear explanation of the confidence intervals for the coefficients, offering a helpful guide for those new to regression analysis.

Takeaways

• 👨‍⚕️ Dr. Grande introduces a video tutorial on interpreting multiple regression output in SPSS.
• 📊 The video uses fictitious data with four variables: an ID variable, two predictor variables, and one outcome variable.
• 🔑 The predictor variables are 'career limitations', an index reflecting potential barriers to employment, and 'experience', measured in years of work.
• 📈 The outcome variable is 'days until employed', tracking the time it takes for participants to find work post-training.
• ❓ Hypotheses suggest that higher career limitations may increase unemployment duration, while more experience may decrease it.
• 📚 The video emphasizes checking assumptions before running multiple regression to ensure data suitability for the analysis.
• 📝 The SPSS process involves selecting 'Analyze', 'Regression', and 'Linear', with 'days until employed' as the dependent variable.
• 📋 The model includes career limitations and experience as independent variables, with options for statistics like R-squared change and confidence intervals.
• 📊 The model summary reveals an adjusted R-square of 0.237, indicating that 23.7% of variance in employment time is explained by the predictors.
• 🔍 The ANOVA table shows a statistically significant result (p < .05), validating the model's predictive power.
• 📈 The coefficients table provides unstandardized and standardized coefficients, with p-values indicating the statistical significance of each predictor.
• 📉 Career limitations have a positive impact on unemployment duration, with each unit increase associated with 2.658 more days until employed.
• 📈 Experience has a negative impact, with each additional year reducing the days until employed by approximately 4 days.
• 📊 Standardized coefficients indicate the effect size in terms of standard deviations, with career limitations increasing days until employed by 0.233 and experience decreasing it by 0.436.
• 📐 Confidence intervals provide a range within which the true effect of each predictor is likely to fall, offering a measure of precision for the estimates.
• 🤔 The video concludes by encouraging viewers to reach out with questions, emphasizing support for further understanding.

Q & A

What is the purpose of the video by Dr. Grande?

The purpose of the video is to explain how to interpret the output from a multiple regression analysis using SPSS.

What are the four variables mentioned in the SPSS data editor in the video?

The four variables mentioned are an ID variable, two predictor variables (career limitations and experience), and one outcome variable (days until employed).

What does the career limitations variable represent in the study?

The career limitations variable represents an index of potential barriers to employment, such as criminal history, low educational level, active substance use disorder, etc., with higher values indicating more severe limitations.

How is the experience variable measured in the study?

The experience variable is measured in years, representing the number of years a participant had either a full-time or part-time job.

What is the maximum number of days considered for the 'days until employed' variable?

The maximum number of days considered for the 'days until employed' variable is 365 days.

What are the hypotheses regarding the relationship between the predictor variables and the outcome variable?

The hypotheses are that a higher number on the career limitations variable would be associated with a longer time to become employed, and more experience would be associated with fewer days until employment.

What does the adjusted R-square value of 0.237 indicate about the model in the video?

An adjusted R-square of 0.237 indicates that 23.7% of the variance in the outcome variable (days until employed) is explained by the independent variables (career limitations and experience).

What does a statistically significant p-value in the ANOVA table suggest?

A statistically significant p-value (less than 0.05) in the ANOVA table suggests that the overall model is significant and that at least one predictor variable has a statistically significant effect on the outcome variable.

What is the interpretation of the unstandardized coefficient for career limitations being 2.658?

The unstandardized coefficient of 2.658 for career limitations means that for every one-unit increase in the career limitations index, there is an expected increase of 2.658 days until employed.

How does the experience variable affect the outcome variable according to the unstandardized coefficients?

The unstandardized coefficient for experience is -4.044, indicating that for every additional year of experience, the number of days until employed decreases by approximately 4 days.

What do the standardized coefficients tell us about the effect of each variable in terms of standard deviations?

The standardized coefficients indicate that for every one standard deviation increase in career limitations, the dependent variable increases by 0.233 standard deviations, and for every one standard deviation increase in experience, the dependent variable decreases by 0.436 standard deviations.

What is the purpose of the confidence intervals provided for the unstandardized coefficients?

The confidence intervals provide a range within which we can be 95% confident that the actual value of the unstandardized coefficient lies, indicating the precision of the estimates.

Outlines

00:00

📊 Introduction to Multiple Regression Analysis in SPSS

Dr. Grande introduces a tutorial on interpreting multiple regression output using SPSS. The video focuses on a hypothetical study with four variables: an ID variable, two predictor variables (career limitations and experience), and one outcome variable (days until employed). The career limitations variable represents potential barriers to workforce re-entry, while experience is measured in years of previous employment. The study aims to understand the relationship between these variables and the time to find employment post-training. The video will guide viewers through the SPSS interface for setting up a linear regression analysis, including selecting variables and choosing statistical options such as R-squared change, model fit, and confidence intervals.

05:01

📈 Analyzing Regression Results and Hypothesis Testing

This paragraph delves into the results of the multiple regression analysis. The p-values for both predictor variables, career limitations and experience, are statistically significant, indicating their impact on the outcome variable. The unstandardized coefficients reveal that an increase in career limitations is associated with a longer time to employment, while more experience is linked to a shorter period of unemployment. The standardized coefficients provide insight into the effect size in terms of standard deviations, showing that both variables have a substantial influence on the dependent variable. Confidence intervals for the unstandardized coefficients are also discussed, providing a range within which the true effect is likely to fall with 95% certainty. The summary concludes with an invitation for questions and further assistance, emphasizing the educational value of the video.

Keywords

💡Multiple Regression

Multiple regression is a statistical method used to analyze the relationship between one dependent variable and multiple independent variables. In the video, Dr. Grande uses multiple regression to understand how career limitations and experience impact the time it takes for participants to find employment after a career training program. The script mentions conducting a multiple regression in SPSS, which is a statistical software package, to analyze the data.

💡SPSS

SPSS, or Statistical Package for the Social Sciences, is a widely used software for statistical analysis in various fields. The video script discusses using SPSS to perform a multiple regression analysis, indicating its role as a tool for interpreting the output from such statistical tests. Dr. Grande guides the viewers through the process of inputting data and running the regression analysis within the SPSS interface.

💡Dependent Variable

A dependent variable is the outcome or the variable that is being measured or tested in an experiment or study. In the context of the video, 'days until employed' is the dependent variable, representing the number of days it takes for participants to find employment after their career training program. The script explains how this variable is used in the regression analysis to determine its relationship with other variables.

💡Independent Variables

Independent variables are the factors that are manipulated or changed in an experiment to determine their effect on the dependent variable. In the video, 'career limitations' and 'experience' are the independent variables that are hypothesized to influence the time to employment. The script describes how these variables are entered into the regression model in SPSS to assess their impact.

💡Predictor Variables

Predictor variables are synonymous with independent variables and are used to predict the outcome of the dependent variable. In the script, 'career limitations' and 'experience' are referred to as predictor variables, which are hypothesized to predict the dependent variable 'days until employed'. The video explains how these variables are analyzed to determine their predictive power.

💡R-squared

R-squared is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. The script mentions an adjusted R-square of 0.237, indicating that 23.7% of the variance in 'days until employed' can be explained by the predictor variables 'career limitations' and 'experience'.

💡ANOVA

ANOVA, or Analysis of Variance, is a statistical method used to compare means of two or more samples to determine if there is a statistically significant difference between them. In the video, the script refers to a statistically significant ANOVA finding, which suggests that the regression model with the predictor variables is significantly different from zero, indicating a meaningful relationship.

💡Coefficients

In the context of regression analysis, coefficients are numerical values that represent the relationship between the independent and dependent variables. The script discusses unstandardized and standardized coefficients for 'career limitations' and 'experience', explaining how they indicate the magnitude and direction of the effect these variables have on the dependent variable.

💡Standardized Coefficients

Standardized coefficients are coefficients that have been converted to a common scale, typically in terms of standard deviations, to allow for comparison of the relative strength of the effect of each independent variable on the dependent variable. The script explains that for every one standard deviation change in 'career limitations', there is a 0.233 standard deviation change in 'days until employed', and similarly for 'experience'.

💡Confidence Intervals

Confidence intervals provide a range of values within which the true population parameter is likely to fall, with a certain level of confidence. In the script, 95% confidence intervals are given for the unstandardized coefficients of 'career limitations' and 'experience', indicating the range in which the true effect of these variables on 'days until employed' is likely to be.

💡Statistical Significance

Statistical significance refers to the probability that the observed results are not due to chance. In the video, the script mentions p-values for 'career limitations' and 'experience', indicating that both variables have a statistically significant impact on 'days until employed', as their p-values are less than 0.05, suggesting that the effects observed are unlikely to be due to random chance.

Highlights

Dr. Grande introduces a video on interpreting multiple regression output from SPSS.

The video uses fictitious data with four variables: an ID variable, two predictor variables, and one outcome variable.

The first predictor variable, 'career limitations', represents potential barriers to workforce re-entry.

The 'experience' variable measures years of full-time or part-time job experience.

The outcome variable, 'days until employed', measures the time to find employment after a Career Training Program.

Hypotheses suggest that higher career limitations may increase the time to employment, while more experience may decrease it.

The video explains the process of conducting a multiple regression in SPSS, including setting up the model and selecting statistics.

The output includes descriptive statistics, correlations, model summary, and ANOVA results.

Adjusted R-square is used to interpret the proportion of variance explained by the model, which is 23.7% in this case.

ANOVA results show the model is statistically significant with a p-value less than 0.05.

Coefficients table reveals the unstandardized and standardized coefficients for 'career limitations' and 'experience'.

P-values indicate that both 'career limitations' and 'experience' have statistically significant impacts on employment time.

The unstandardized coefficient for 'career limitations' is 2.658, suggesting an increase in limitations leads to longer unemployment.

The negative unstandardized coefficient for 'experience' (-4.044) implies more experience results in shorter unemployment duration.

Standardized coefficients show the effect size in terms of standard deviations for each predictor variable.

Confidence intervals provide a range where the true coefficient values are likely to fall with 95% certainty.

The video concludes with an invitation for questions or concerns, offering further assistance.

Transcripts

hello this is dr. Grande welcome to my

video on interpreting the output from a

multiple regression using SPSS as always

if you find this video useful please

like it and subscribe to my channel

I certainly appreciate it I have in the

SPSS data editor four variables these

are fictitious data I have an ID

variable I have a hundred participants

in this design and I have two predictor

variables and one outcome variable so

for the first predictor variable this

one is named career limitations so let's

assume that we have participants that

are in a career training program and we

develop this series of questions to

determine how many limitations they're

facing in terms of getting back into the

workforce and these questions go through

a scoring process and end up in this

variable and this is an index so certain

characteristics or occurrences may be

weighted more heavily than others

potential limitations could include a

criminal history low educational level

an active substance use disorder and

other factors so a higher value in this

variable would represent more

limitations or more severe limitations

then we have experienced an experience

we've measured in years this be the

number of years a participant had either

a full-time or part-time job then we

have days until employed so after the

completion of the Career Training

Program the number of days until the

participant finds employment is measured

and for this example the maximum would

be 365 days so we could have a couple

hypotheses here

before we conduct a multiple regression

career limitations we believe the higher

the number on the career limitations

variable the longer it would take to

become employed and for the experience

variable the more experience associated

with fewer days with a smaller number of

days until the participant becomes

employed now there are assumptions from

multiple regression but here I'm gonna

be focused on the output so I'm not

going to check those assumptions but

just know there are assumptions before

running a multiple regression that would

need to be checked to make sure these

data would be appropriate for that

statistic so here under analyze

regression and linear I have the dialog

for linear regression and you can see

there's one space for a dependent

variable or an outcome variable and

that's going to be days until employed

and you can have multiple independent

variables or predictor variables in this

case I have two career limitations and

experience under statistics by default

we have estimates and model fit I'm just

going to add r-squared change and

descriptives here as well as the

confidence intervals at the 95% level

continue and I'm not going to make any

other changes here under the buttons on

the right so ready to conduct the

multiple regression click OK

and let's take a look at the tables we

have the descriptive statistics here up

top then correlations variables entered

and removed both of the variables I put

into the model used here model summary

we have R square and adjusted r-square

we're going to be interpreting adjusted

r-square so with this model we have the

two predictor variables the one

dependent variable and we have an

adjusted r-square of 0.23 seven

this tells us that 23.7% of the variance

in the dependent variable is explained

by the independent variables moving down

to ANOVA we have a statistically

significant finding here less than point

zero five for the p-value then we have

the coefficients table so we have your

career limitations experience and the

unstandardized coefficients for career

limitations it's two point six five

eight and for experience it's negative

four point zero four four we also have

the standardized coefficients and

p-values so let's start with the

p-values here for career limitations we

have point zero zero nine that's

statistically significant so this

variable has a statistically significant

impact on the outcome variable on the

days until employed taking a look here

at the p-value associated with

experience you can see this is also less

than point zero five so we have

statistically significant contribution

from the experience predictor variable

looking at the unstandardized

coefficients for career limitations we

have a value here of two point six five

eight and what this tells us is as the

career limitations index increases by a

value of one for every one unit of

change for career limitations we're

going to see a two points six five eight

change in the days until employed

variable so one point on the Kermit

Asians

one additional point is associated with

two point six five eight days increase

on the dependent variable so the more

career limitations we have as measured

by that scale by that index

the longer it takes the participant to

find employment experience however works

differently with the experienced

independent variable we have a negative

value for the unstandardized coefficient

negative four point zero four four so if

this tells us is as experience increases

by one year because experience is

measured in years that's the unit of

analysis for that variable the number of

days into employed decreases by about

four so more experience associated with

a smaller number of days of unemployment

now when thinking about this in terms of

standard deviations we would look at the

standardized coefficients so for every

full standard deviation of movement we

see in career limitations very one

standard deviation of movement we see

what this variable the dependent

variable days until employed increases

by 0.23 three standard deviations for

every one standard deviation of movement

we see an experience as an experience

increases by one standard deviation we

have a decrease on the dependent

variable base until employed of negative

0.4 three six standard deviations and

then moving over to the confidence

interval and this is for the

unstandardized coefficient we interpret

before we can see there's a 95% chance

that the actual value of the

unstandardized coefficient is between

0.67 one and 4.6 or four

and the actual value for experience we

can be 95% confident that is between

negative five point six six three and

negative two point four two six I hope

you found this video on interpreting the

output from multiple regression in SPSS

to be helpful as always if you have any

questions or concerns feel free to

contact me I'll be happy to assist you