Interpreting Output for Multiple Regression in SPSS

1. Audio
   Observation

   The speaker says there is an ID variable and 100 participants in the design.

2. Diagram
   Observation

   SPSS Data Editor shows a column labeled ID with numeric case identifiers.

ID

Participant identifier variable in the fictitious dataset.

Dataset cases; 100 participants are stated.

1. Audio
   Observation

   The speaker names the first predictor variable Career Limitations and explains it as an index of limitations facing re-entry into the workforce.

2. Diagram
   Observation

   SPSS Data Editor shows a column labeled Career_limitations with integer values such as 63, 30, 58, 32, 65, 35, 30, 53, 37, 50, 45, 52, 38, 42, 47, 38, 63, 40, 67, 36, 57, 45, 42, 32, 38, 60, 68, 40, 49, 31.

Career_limitations

First predictor variable; an index score representing how many or how severe career limitations a participant faces when returning to the workforce.

Higher values represent more limitations or more severe limitations.

1. Audio
   Observation

   The speaker says Experience would be measured in years and refers to full-time or part-time job history.

2. Diagram
   Observation

   SPSS Data Editor shows a column labeled Experience with integer values such as 40, 37, 39, 33, 7, 2, 20, 18, 8, 30, 13, 22, 10, 176, 3, 39, 33, 11, 9, 2, 36, 33, 3, 32, 10, 12, 0, 7, 0, 3, 49, 15.

1. Some visible Experience values are large relative to the stated years-of-job-history definition, but the video does not explain this discrepancy.

Experience

Second predictor variable; number of years a participant had either a full-time or part-time job.

Measured in years according to the narration.

1. Audio
   Observation

   The speaker names Days Until Employed as the outcome variable and says the maximum would be 365 days.

2. Diagram
   Observation

   SPSS Data Editor shows a column labeled Days_until_employed with integer values such as 213, 17, 9, 189, 365, 320, 311, 77, 98, 164, 51, 121, 261, 279, 320, 126, 202, 24, 182, 341, 36, 299, 35, 115, 73, 206, 326, 347, 78, 108, 15.

Days_until_employed

Outcome variable; number of days after completion of the career training program until the participant finds employment.

Stated maximum is 365 days.

1. Diagram
   Observation

   The data editor shows a column labeled Days_until_employed.

2. Audio
   Observation

   The speaker says the dependent or outcome variable is days until employed.

Days_until_employed

Dependent variable representing the number of days until a participant becomes employed.

Continuous numeric outcome variable in the displayed dataset.

1. Diagram
   Observation

   The data editor and Linear Regression dialog show Career_limitations as one of the variables.

2. Audio
   Observation

   The speaker names career limitations as one of two independent variables.

Career_limitations

Independent/predictor variable entered into the multiple regression model.

Numeric predictor variable in the displayed dataset.

1. Diagram
   Observation

   The data editor and Linear Regression dialog show Experience as one of the variables.

2. Audio
   Observation

   The speaker names experience as the second independent variable.

Experience

Independent/predictor variable entered into the multiple regression model.

Numeric predictor variable in the displayed dataset.

1. Formula
   Observation

   Model Summary table shows Adjusted R Square = .237.

2. Audio
   Observation

   The speaker says they will interpret adjusted R square and states it is point two three seven.

Adjusted $R^2$

Adjusted coefficient of determination used to describe the proportion of variance in the dependent variable explained by the predictors.

Reported in the Model Summary table for the fitted regression model.

1. Formula
   Observation

   ANOVA table shows Sig. = .000.

2. Audio
   Observation

   The speaker says there is a statistically significant finding here, less than point zero five for the p value.

p

Significance level for the overall regression ANOVA test.

Displayed in the ANOVA table as Sig.

1. Formula
   Observation

   Coefficients table shows unstandardized B for Career_limitations = 2.658.

2. Audio
   Observation

   The speaker says for career limitations it's two point six five eight.

This formula needs review. Please report it using the page feedback control.

Unstandardized regression coefficient for Career_limitations.

Displayed in the Coefficients table.

1. Formula
   Observation

   Coefficients table shows unstandardized B for Experience = -4.044.

2. Audio
   Observation

   The speaker says for experience it's negative four point zero four four.

$B_{\text{Experience}}$

Unstandardized regression coefficient for Experience.

Displayed in the Coefficients table.

1. Formula
   Observation

   Coefficients table includes a Standardized Coefficients Beta column.

2. Audio
   Observation

   The speaker says, "We also have the standardized coefficients."

1. The clip ends before the speaker explains how to interpret the standardized coefficients.

$\beta$

Standardized regression coefficients shown in the Coefficients table.

Displayed in the Coefficients table under Standardized Coefficients Beta.

1. Audio
   Observation

   The speaker introduces the video as being about interpreting the output from a multiple regression using SPSS.

2. Diagram
   Observation

   Title card reads Interpreting Output for Multiple Regression in SPSS and Dr. Todd L. Grande.

This segment frames the lesson as an SPSS-based interpretation task for multiple regression. The actual regression output is not shown yet; the clip first establishes the software context and the data setup that will be used before interpretation.

1. Uses SPSS.

2. Focus is on interpreting output from multiple regression.

3. In this excerpt, only the dataset and variable roles are introduced.

1. Audio
   Observation

   The speaker says there are four variables in the SPSS Data Editor, that the data are fictitious, that there is an ID variable with 100 participants, and that there are two predictor variables and one outcome variable.

2. Diagram
   Observation

   SPSS Data Editor displays columns ID, Career_limitations, Experience, and Days_until_employed.

The example dataset contains four columns: one identifier variable, two predictor variables, and one outcome variable. The speaker explicitly states that the data are fictitious and that the design includes 100 participants.

1. Dataset is fictitious.

2. There are 100 participants.

3. There are two predictors and one outcome variable.

1. Purpose of the clip: interpreting multiple regression output in SPSS

1. Audio
   Observation

   The speaker identifies Career Limitations and Experience as the two predictor variables.

2. Diagram
   Observation

   The SPSS columns Career_limitations and Experience are highlighted while the speaker discusses them.

In this example, the predictors are Career_limitations and Experience. A predictor variable is the explanatory side of the regression model: it is used to account for variation in the outcome variable Days_until_employed.

1. There are exactly two predictors in this example.

2. Predictors are distinct from the outcome variable Days_until_employed.

1. Structure of the example dataset

1. Audio
   Observation

   The speaker identifies Days Until Employed as the outcome variable and defines it as the number of days after completion of the career training program until the participant finds employment.

2. Diagram
   Observation

   The SPSS column Days_until_employed is highlighted while the speaker discusses it.

The outcome variable is Days_until_employed, defined as the number of days after completing the career training program until the participant finds employment. In regression terminology, this is the dependent variable whose variation the predictors are intended to explain.

1. Outcome is measured in days.

2. Stated maximum is 365 days.

1. Structure of the example dataset

1. Audio
   Observation

   The speaker says Career Limitations comes from a series of questions, goes through a scoring process, and ends up as an index; higher values mean more limitations or more severe limitations.

2. Diagram
   Observation

   The Career_limitations column is selected in SPSS while the explanation is given.

Career_limitations is not described as a raw single question score but as an index produced by scoring a series of questions. The speaker gives examples of possible limitations, including criminal history, low educational level, and active substance use disorder, and states that larger values indicate more or more severe limitations.

1. Variable is an index derived from multiple questions.

2. Higher values indicate greater severity or quantity of limitations.

1. Predictor variables in the multiple regression example

1. Audio
   Observation

   The speaker says Experience would be measured in years and would be the number of years a participant had either a full-time or part-time job.

2. Diagram
   Observation

   The Experience column is selected in SPSS during the explanation.

Experience is defined operationally as the number of years a participant had either a full-time or part-time job. This makes it a quantitative predictor variable in the regression setup.

1. Unit is years.

2. Includes both full-time and part-time job history.

1. Predictor variables in the multiple regression example

1. Audio
   Observation

   The speaker says that for Career Limitations, they would believe the higher the number on the variable, the longer it would take to become employed.

Before running the regression, the speaker proposes a directional expectation: as Career_limitations increases, Days_until_employed should also increase. This is a positive association between the predictor and the outcome.

1. Applies to the predictor Career_limitations.

2. Refers to the outcome Days_until_employed.

3. Stated as a hypothesis before conducting the regression.

1. Career_limitations as an index score
2. Outcome variable in the multiple regression example

1. Audio
   Observation

   The speaker says that for the experience variable, the more experience is associated with fewer days.

The second proposed expectation is inverse: greater Experience should correspond to fewer days until employment. Thus the speaker predicts a negative association between Experience and Days_until_employed.

1. Applies to the predictor Experience.

2. Refers to the outcome Days_until_employed.

3. Stated as a hypothesis before conducting the regression.

1. Experience measured in years
2. Outcome variable in the multiple regression example

1. Audio
   Observation

   The speaker says there are assumptions for multiple regression but this segment focuses on the output rather than checking assumptions.

The video explicitly frames this segment as an interpretation of multiple regression output in SPSS. The speaker notes that multiple regression has assumptions, but says those assumptions will not be checked here; instead, attention is directed to reading the output tables after running the analysis.

1. Applies to the demonstrated workflow in this clip.

2. Assumption checking is acknowledged but omitted from the shown procedure.

1. Audio
   Observation

   The speaker says, "So here under Analyze, Regression, and Linear, I have the dialog for linear regression."

2. Diagram
   Observation

   The Linear Regression dialog is opened and variables are assigned to Dependent and Independent(s) boxes.

The demonstrated method is to run multiple regression through SPSS by choosing Analyze → Regression → Linear. In the dialog, one variable is placed in the Dependent box and multiple variables are placed in the Independent(s) box. In this example, Days_until_employed is the dependent variable, while Career_limitations and Experience are the predictors.

1. One dependent variable is specified.

2. Multiple independent variables may be specified.

1. Scope of the demonstration: interpreting output, not testing assumptions

1. Audio
   Observation

   The speaker says there is one space for a dependent variable or outcome variable, and multiple independent variables or predictor variables.

2. Diagram
   Observation

   Days_until_employed is moved to Dependent; Career_limitations and Experience are moved to Independent(s).

The video distinguishes the single outcome variable from the set of predictor variables. The dependent variable is the outcome being modeled, here Days_until_employed. The independent variables are the predictors used to explain variation in that outcome, here Career_limitations and Experience.

1. The model shown has exactly one dependent variable.

2. The model shown has two independent variables.

1. Opening the SPSS Linear Regression dialog and assigning variables

1. Audio
   Observation

   The speaker says estimates and model fit are default, then adds R squared change, descriptives, and confidence intervals at the 95% level.

2. Diagram
   Observation

   Linear Regression Statistics dialog shows Estimates, Model fit, Descriptives, Confidence intervals, and R square change selected.

Before running the model, the speaker opens the Statistics options and requests additional output beyond the defaults. The selected items include Estimates, Model fit, Descriptives, Confidence intervals at the 95% level, and R square change. These choices determine which tables appear in the SPSS output viewer.

1. Confidence intervals are requested at the 95% level.

2. R square change is requested in addition to default estimates and model fit.

1. Opening the SPSS Linear Regression dialog and assigning variables

1. Audio
   Observation

   The speaker says there are assumptions for multiple regression and that they would need to be checked before running the statistic.

1. The specific assumptions are not named in this clip.

Before running multiple regression, its assumptions should be checked to ensure the data are appropriate for that statistic.

1. A multiple regression analysis is being considered.

2. The analyst wants to verify that the data are suitable for the statistic.

General statement about multiple regression practice.

1. Audio
   Observation

   The speaker says adjusted R square is point two three seven and explains that this tells us 23.7% of the variance in the dependent variable is explained by the independent variables.

2. Formula
   Observation

   Model Summary table shows Adjusted R Square = .237.

For this model, an adjusted R squared value of .237 means that 23.7% of the variance in the dependent variable is explained by the independent variables.

1. The fitted model has two predictor variables and one dependent variable.

2. The reported adjusted R squared is .237.

Statement about the specific displayed regression model.

1. Audio
   Observation

   The speaker says the ANOVA result is statistically significant, less than point zero five for the p value.

2. Formula
   Observation

   ANOVA table shows Sig. = .000.

The ANOVA result indicates a statistically significant overall regression model because the p value is less than .05.

1. The ANOVA table from the fitted regression model is being examined.

2. The displayed Sig. value is .000.

Statement about the specific displayed regression model.

1. Audio
   Observation

   The speaker says, "For career limitations, we have point zero zero nine. That's statistically significant."

2. Diagram
   Observation

   The Sig. entry for Career_Limitations is .009.

Because the p-value for Career_Limitations is .009, the video concludes that this predictor has a statistically significant impact on Days_until_employed.

1. The displayed model is a multiple regression with dependent variable Days_until_employed.

2. The significance threshold used in the clip is .05.

For the predictor Career_Limitations in the displayed fitted model.

1. Audio
   Observation

   The speaker says the p-value associated with experience is "also less than .05" and therefore has a statistically significant contribution.

2. Diagram
   Observation

   The Sig. entry for Experience is .000.

Because the p-value for Experience is less than .05, the video concludes that Experience makes a statistically significant contribution to Days_until_employed.

1. The displayed model is a multiple regression with dependent variable Days_until_employed.

2. The significance threshold used in the clip is .05.

For the predictor Experience in the displayed fitted model.

1. Audio
   Observation

   The speaker says that one additional point on the career limitations index is associated with 2.658 more days on the dependent variable, so more career limitations means it takes longer to find employment.

2. Diagram
   Observation

   The unstandardized coefficient for Career_Limitations is 2.658.

In this fitted model, a one-unit increase in Career_Limitations is associated with an increase of 2.658 in Days_until_employed.

1. The coefficient is read from the Unstandardized Coefficients B column.

2. Other predictors in the model are held constant.

Per one-unit increase in Career_Limitations.

1. Audio
   Observation

   The speaker says that as experience increases by one year, the number of days until employed decreases by about four.

2. Diagram
   Observation

   The unstandardized coefficient for Experience is -4.044.

In this fitted model, a one-year increase in Experience is associated with a decrease of about 4.044 in Days_until_employed.

1. Experience is measured in years.

2. The coefficient is read from the Unstandardized Coefficients B column.

3. Other predictors in the model are held constant.

Per one-year increase in Experience.

1. Audio
   Observation

   Speaker says: 'we have a decrease on the dependent variable days unemployed of negative point four three six standard deviations.'

2. Diagram
   Observation

   Coefficients table row for Experience shows Beta = -.436.

Holding career motivation constant, a one standard deviation increase in Experience is associated with a 0.436 standard deviation decrease in Days_unemployed.

1. The regression model is correctly specified

2. Experience and Career_motivation are the only predictors in the model

For every one standard deviation increase in Experience

1. Audio
   Observation

   Speaker says: 'increases by point two three three standard deviations.'

2. Diagram
   Observation

   Coefficients table row for Career_motivation shows Beta = .233.

Holding experience constant, a one standard deviation increase in Career_motivation is associated with a 0.233 standard deviation increase in Days_unemployed.

1. The regression model is correctly specified

2. Experience and Career_motivation are the only predictors in the model

For every one standard deviation increase in Career_motivation

1. Audio
   Observation

   The speaker moves through the output in order: descriptive statistics, correlations, variables entered and removed, model summary, ANOVA, coefficients.

2. Diagram
   Observation

   The output viewer scrolls downward through these tables in that sequence.

1. Expression

   $$$$
   Explanation

   Identify the Descriptive Statistics table at the top of the output.

   Justification

   The speaker explicitly says, "We have the descriptive statistics here up top."

   Shown in the video
2. Expression

   $$$$
   Explanation

   Move to the Correlations table.

   Justification

   The speaker names "correlations" immediately after the descriptive statistics.

   Shown in the video
3. Expression

   $$$$
   Explanation

   Check the Variables Entered/Removed table to confirm which predictors were used.

   Justification

   The speaker says both variables put into the model were used here.

   Shown in the video
4. Expression

   $$$$
   Explanation

   Read the Model Summary table and focus on Adjusted R Square.

   Justification

   The speaker says they will interpret adjusted R square and reads .237.

   Shown in the video
5. Expression

   $$$$
   Explanation

   Inspect the ANOVA table for overall significance.

   Justification

   The speaker says moving down to ANOVA shows a statistically significant finding with p < .05.

   Shown in the video
6. Expression

   $$$$
   Explanation

   Read the Coefficients table, first the unstandardized coefficients and then note the standardized coefficients.

   Justification

   The speaker names the coefficients table, reads B for Career_limitations and Experience, and then says standardized coefficients are also present.

   Shown in the video

The clip demonstrates a left-to-right, top-to-bottom reading strategy for SPSS regression output: start with sample descriptions, confirm entered variables, assess model fit with adjusted R squared, test overall significance with ANOVA, and then inspect coefficient estimates.

1. Audio
   Observation

   The speaker sequentially moves from the Sig. column to the Unstandardized Coefficients B column and then to the Standardized Coefficients Beta column.

2. Diagram
   Observation

   The cursor highlights cells in the Coefficients table in that same order.

1. Expression

   $$$$
   Explanation

   First identify the predictor rows in the Coefficients table: Career_Limitations and Experience, with dependent variable Days_until_employed.

   Justification

   The table footnotes explicitly name the dependent variable and predictors.

   Shown in the video
2. Expression

   $$$$
   Explanation

   Then inspect the Sig. column to decide whether each predictor is statistically significant using the .05 rule mentioned in the audio.

   Justification

   The speaker explicitly starts with p-values and compares them to .05.

   Shown in the video
3. Expression

   $$B_{Career\_Limitations}=2.658$$
   Explanation

   Next read the unstandardized coefficient for Career_Limitations and interpret it as the change in Days_until_employed for a one-unit increase in Career_Limitations.

   Justification

   The speaker states this interpretation directly after highlighting 2.658.

   Shown in the video
4. Expression

   $$B_{Experience}=-4.044$$
   Explanation

   Then read the unstandardized coefficient for Experience and interpret it as the change in Days_until_employed for a one-year increase in Experience.

   Justification

   The speaker states that experience is measured in years and that the negative sign means fewer days until employment.

   Shown in the video
5. Expression

   $$\beta_{Career\_Limitations}=.223$$
   Explanation

   Finally, the speaker turns to the standardized coefficients column to shift the discussion from raw units to standard-deviation units.

   Justification

   The audio explicitly says that thinking in terms of standard deviations requires looking at standardized coefficients.

   Shown in the video

The clip demonstrates a practical workflow for interpreting regression output: check significance first, then interpret raw-effect size, then consider standardized effect size.

1. Audio
   Observation

   The speaker walks through the SPSS Data Editor, naming the variables, their roles, and the hypothesized directions of their relationships with the outcome.

2. Diagram
   Observation

   The screen shows SPSS Data Editor with columns ID, Career_limitations, Experience, and Days_until_employed, and the speaker highlights these columns in sequence.

Prepare and interpret the variable structure for a multiple regression example in SPSS using a fictitious dataset with 100 participants.

1. Four variables are present: ID, Career_limitations, Experience, and Days_until_employed.

2. The data are fictitious.

3. There are 100 participants.

4. Career_limitations and Experience are predictors.

5. Days_until_employed is the outcome variable.

6. Career_limitations is an index where higher values mean more severe limitations.

7. Experience is measured in years of full-time or part-time work.

8. Days_until_employed is measured in days after program completion, with a stated maximum of 365 days.

Identify the role and meaning of each variable and state the expected directional relationships before running the regression.

1. Expression

   $$$$
   Explanation

   Open the SPSS Data Editor and inspect the dataset layout.

   Justification

   The speaker explicitly says, "I have in the SPSS Data Editor four variables."

   Shown in the video
2. Expression

   $$$$
   Explanation

   Recognize ID as the participant identifier and note that the design contains 100 participants.

   Justification

   The speaker says, "I have an ID variable. I have 100 participants in this design."

   Shown in the video
3. Expression

   $$$$
   Explanation

   Classify Career_limitations and Experience as the two predictor variables and Days_until_employed as the outcome variable.

   Justification

   The speaker says, "And I have two predictor variables and one outcome variable," then names each variable in turn.

   Shown in the video
4. Expression

   $$$$
   Explanation

   Define Career_limitations as an index built from multiple questions about barriers to re-entering the workforce.

   Justification

   The speaker explains that a series of questions is scored into an index and gives examples such as criminal history, low educational level, and active substance use disorder.

   Shown in the video
5. Expression

   $$$$
   Explanation

   Define Experience as years of full-time or part-time job history.

   Justification

   The speaker states, "Experience would be measured in years" and describes it as the number of years with either a full-time or part-time job.

   Shown in the video
6. Expression

   $$$$
   Explanation

   Define Days_until_employed as the number of days after completing the career training program until employment is found, with a maximum of 365 days in this example.

   Justification

   The speaker says the outcome measures days until the participant finds employment and adds, "the maximum would be 365 days."

   Shown in the video
7. Expression

   $$$$
   Explanation

   State the first directional hypothesis: higher Career_limitations should predict longer time to employment.

   Justification

   The speaker says, "we would believe the higher the number on the career limitations variable, that the longer it would take to become employed."

   Shown in the video
8. Expression

   $$$$
   Explanation

   State the second directional hypothesis: higher Experience should predict fewer days to employment.

   Justification

   The speaker says, "for the experience variable, the more experience is associated with fewer days."

   Shown in the video

The example sets up a multiple regression with predictors Career_limitations and Experience and outcome Days_until_employed, with expected signs + for Career_limitations and - for Experience.

Verification is internal to the setup: the variable roles and hypothesized directions match the spoken definitions and the highlighted SPSS columns. No regression output is shown in this excerpt, so statistical confirmation is absent.

1. Diagram
   Observation

   A dataset with ID, Career_limitations, Experience, and Days_until_employed is shown, then a Linear Regression analysis is configured and run in SPSS.

2. Audio
   Observation

   The speaker narrates variable assignment, statistics selection, execution, and interpretation of the resulting tables.

1. The clip ends before interpretation of the standardized coefficients is completed.

Run and interpret a multiple regression in SPSS using Days_until_employed as the dependent variable and Career_limitations and Experience as predictors.

1. Dataset displayed in IBM SPSS Statistics Data Editor.

2. Dependent variable: Days_until_employed.

3. Independent variables: Career_limitations and Experience.

4. Requested statistics include Estimates, Model fit, Descriptives, Confidence intervals at 95%, and R square change.

Produce the regression output and interpret the main tables, especially adjusted R squared, ANOVA significance, and unstandardized coefficients.

1. Expression

   $$$$
   Explanation

   Open Analyze → Regression → Linear in SPSS.

   Justification

   The speaker states this menu path and the Linear Regression dialog appears.

   Shown in the video
2. Expression

   $$$$
   Explanation

   Place Days_until_employed in the Dependent box.

   Justification

   The speaker identifies it as the dependent or outcome variable and moves it into the dialog.

   Shown in the video
3. Expression

   $$$$
   Explanation

   Place Career_limitations and Experience in the Independent(s) box.

   Justification

   The speaker says there can be multiple independent variables and names these two predictors.

   Shown in the video
4. Expression

   $$$$
   Explanation

   Open Statistics and select Descriptives, Confidence intervals, and R square change in addition to the defaults.

   Justification

   The speaker explicitly says these are the additions being made.

   Shown in the video
5. Expression

   $$$$
   Explanation

   Click OK to run the regression.

   Justification

   The speaker says, "So ready to conduct the multiple regression, click OK."

   Shown in the video
6. Expression

   $$\text{Adjusted } R^2 = .237$$
   Explanation

   Read the Model Summary table and interpret adjusted R squared as 23.7% explained variance.

   Justification

   The speaker says adjusted R square is point two three seven and explains its meaning.

   Shown in the video
7. Expression

   $$F = 16.550,\quad p < .05$$
   Explanation

   Read the ANOVA table and conclude the overall model is statistically significant.

   Justification

   The speaker points to ANOVA and says the finding is significant because the p value is less than .05.

   Shown in the video
8. Expression

   $$B_{\text{Career\_limitations}} = 2.658,\quad B_{\text{Experience}} = -4.044$$
   Explanation

   Read the unstandardized coefficients from the Coefficients table.

   Justification

   The speaker names the coefficients table and reads these two values aloud.

   Shown in the video

The demonstrated model has Adjusted R Square = .237, a significant overall ANOVA result with Sig. = .000, and unstandardized coefficients $B = 2.658$ for Career_limitations and $B = -4.044$ for Experience.

Verification is by direct comparison of the spoken interpretation with the visible SPSS output tables: Model Summary, ANOVA, and Coefficients.

1. Diagram
   Observation

   The screen shows an SPSS output window titled "Variables Entered/Removed" with Model Summary, ANOVA, and Coefficients tables.

2. Audio
   Observation

   The speaker interprets the displayed regression results for Career_Limitations and Experience predicting Days_until_employed.

1. Some small numeric entries outside the highlighted cells are difficult to verify precisely from the compressed frames.

Interpret the displayed SPSS regression output for the model predicting Days_until_employed from Career_Limitations and Experience.

1. Dependent Variable: Days_until_employed.

2. Predictors: (Constant), Experience, Career_limitations.

3. Sig. for Career_Limitations = .009.

4. Sig. for Experience = .000.

5. Unstandardized B for Career_Limitations = 2.658.

6. Unstandardized B for Experience = -4.044.

7. Standardized Beta for Career_Limitations = .223.

8. Standardized Beta for Experience = -.416.

Determine which predictors are statistically significant and explain the direction and magnitude of their unstandardized effects, then note how standardized coefficients would be used.

1. Expression

   $$p_{Career\_Limitations}=.009<.05$$
   Explanation

   Compare the p-value for Career_Limitations with the .05 threshold.

   Justification

   The speaker explicitly calls .009 statistically significant.

   Shown in the video
2. Expression

   $$p_{Experience}=.000<.05$$
   Explanation

   Compare the p-value for Experience with the .05 threshold.

   Justification

   The speaker says this p-value is also less than .05 and therefore significant.

   Shown in the video
3. Expression

   $$\Delta Y=2.658 \text{ when } \Delta X_{Career\_Limitations}=1$$
   Explanation

   Read the unstandardized coefficient for Career_Limitations as a change in the dependent variable per one-unit increase in the predictor.

   Justification

   The speaker states that one additional point on the career limitations index corresponds to 2.658 more days on the dependent variable.

   Shown in the video
4. Expression

   $$\Delta Y=-4.044 \text{ when } \Delta X_{Experience}=1 \text{ year}$$
   Explanation

   Read the unstandardized coefficient for Experience as a change in the dependent variable per one-year increase in experience.

   Justification

   The speaker notes that experience is measured in years and that the negative coefficient means days until employed decrease by about four.

   Shown in the video
5. Expression

   $$\beta_{Career\_Limitations}=.223,\ \beta_{Experience}=-.416$$
   Explanation

   Identify the standardized coefficients as the quantities to use when thinking in standard-deviation units.

   Justification

   The speaker introduces standardized coefficients for standard-deviation interpretation before the clip ends.

   Shown in the video

Both predictors are statistically significant. Career_Limitations has a positive effect: +1 unit is associated with +2.658 days until employed. Experience has a negative effect: +1 year is associated with -4.044 days until employed. The standardized coefficients shown are .223 for Career_Limitations and -.416 for Experience.

The conclusions match the highlighted cells in the SPSS Coefficients table and the spoken interpretation in the clip.

1. Diagram
   Observation

   The entire video focuses on reading specific cells from the SPSS Coefficients table for the variables Experience and Career_motivation.

2. Audio
   Observation

   Narrator walks through the Beta values and Confidence Interval bounds for both variables sequentially.

Interpret the standardized coefficients and 95% confidence intervals for unstandardized coefficients from the provided SPSS output table.

1. Dependent Variable: Days_unemployed

2. Predictors: Career_motivation, Experience

3. Beta for Career_motivation = .233

4. Beta for Experience = -.436

5. 95% CI for B (Career_motivation) = [.671, 4.644]

6. 95% CI for B (Experience) = [-5.663, -2.426]

Translate the numerical output into substantive statements about the relationships between variables.

1. Expression

   $$\beta_{Career\_motivation} = 0.233$$
   Explanation

   Identify the standardized coefficient for Career_motivation.

   Justification

   Direct observation from the Coefficients table.

   Shown in the video
2. Expression

   $$\Delta Y_{SD} = +0.233 \text{ SD}$$
   Explanation

   Interpret the magnitude and direction: a 1 SD increase in Career_motivation leads to a 0.233 SD increase in Days_unemployed.

   Justification

   Definition of standardized coefficients.

   Derived from the video
3. Expression

   $$\beta_{Experience} = -0.436$$
   Explanation

   Identify the standardized coefficient for Experience.

   Justification

   Direct observation from the Coefficients table.

   Shown in the video
4. Expression

   $$\Delta Y_{SD} = -0.436 \text{ SD}$$
   Explanation

   Interpret the magnitude and direction: a 1 SD increase in Experience leads to a 0.436 SD decrease in Days_unemployed.

   Justification

   Definition of standardized coefficients.

   Derived from the video
5. Expression

   $$CI_{95\%}(B_{Career\_motivation}) = [0.671, 4.644]$$
   Explanation

   State the confidence interval for the unstandardized coefficient of Career_motivation.

   Justification

   Direct observation from the Coefficients table.

   Shown in the video
6. Expression

   $$CI_{95\%}(B_{Experience}) = [-5.663, -2.426]$$
   Explanation

   State the confidence interval for the unstandardized coefficient of Experience.

   Justification

   Direct observation from the Coefficients table.

   Shown in the video

Career motivation has a positive association with days unemployed (Beta=.233), while experience has a stronger negative association (Beta=-.436). The true unstandardized effects are estimated to be between 0.671 and 4.644 days for career motivation, and between -5.663 and -2.426 days for experience.

The signs of the confidence intervals match the signs of the unstandardized coefficients (B) in the table, confirming consistency.

1. Diagram
   Observation

   A black title card displays the text Interpreting Output for Multiple Regression in SPSS and Dr. Todd L. Grande.

1. Black background

2. Title text

3. Presenter name

1. Text fades in and later fades out.

1. No mathematical notation appears on the title card.

The title establishes that the lesson concerns interpretation of multiple regression output in SPSS, although this excerpt only reaches the data-setup stage.

1. Diagram
   Observation

   The screen switches to IBM SPSS Statistics Data Editor showing a spreadsheet-like table with columns ID, Career_limitations, Experience, and Days_until_employed.

1. SPSS Data Editor window

2. Column headers

3. Rows of numeric data

1. The interface changes from title card to dataset view.

1. Four variable columns remain visible.

2. The dataset is presented in row-by-case, column-by-variable form.

This visual establishes the raw data matrix that will underlie the later regression analysis.

1. Diagram
   Observation

   As the speaker explains each variable, the corresponding SPSS column is selected and highlighted in yellow: first ID, then Career_limitations, then Experience, then Days_until_employed.

1. ID column

2. Career_limitations column

3. Experience column

4. Days_until_employed column

5. Yellow selection highlight

1. Selection moves from one column to the next in the order the variables are discussed.

1. The underlying data values do not change during the highlighting.

2. The table remains in Data View throughout.

The visual sequencing maps each spoken definition to a specific column, reinforcing the distinction among identifier, predictors, and outcome.

1. Diagram
   Observation

   The screen shows the SPSS Data Editor with columns ID, Career_limitations, Experience, and Days_until_employed.

1. SPSS Data Editor window

2. Column headers ID, Career_limitations, Experience, Days_until_employed

3. Rows of numeric data

1. The cursor highlights cells in the Days_until_employed column.

1. The dataset structure remains visible throughout this interval.

The visual establishes the variables that will be used in the regression analysis.

1. Diagram
   Observation

   The user opens Analyze → Regression → Linear..., and the Linear Regression dialog appears.

1. Analyze menu

2. Regression submenu

3. Linear... command

4. Linear Regression dialog

1. Menu expands.

2. Dialog box opens over the data editor.

1. The underlying dataset remains in the background.

This visual marks the transition from data viewing to model specification.

1. Diagram
   Observation

   Days_until_employed is moved to Dependent; Career_limitations and Experience are moved to Independent(s).

1. Source variable list

2. Dependent box

3. Independent(s) box

4. Arrow buttons

1. Days_until_employed transfers into the Dependent field.

2. Career_limitations and Experience transfer into the Independent(s) field.

1. The dialog remains open during assignment.

The visual encodes the regression structure: one outcome variable and two predictors.

1. Diagram
   Observation

   The Linear Regression Statistics dialog opens and checkboxes are selected for Descriptives, Confidence intervals, and R square change.

1. Statistics button

2. Linear Regression Statistics dialog

3. Checkboxes for Estimates, Model fit, Descriptives, Confidence intervals, R square change

1. Additional checkboxes are turned on.

2. Confidence interval level is shown as 95.

1. Estimates and Model fit remain selected as defaults.

The visual determines which tables will later appear in the output viewer.

1. Diagram
   Observation

   The SPSS Statistics Viewer opens and the output scrolls downward through Descriptive Statistics, Correlations, Variables Entered/Removed, Model Summary, ANOVA, and Coefficients.

1. Output Viewer

2. Descriptive Statistics table

3. Correlations table

4. Variables Entered/Removed table

5. Model Summary table

6. ANOVA table

7. Coefficients table

1. The view moves from upper tables to lower tables.

2. Specific rows and columns are highlighted as the speaker discusses them.

1. All tables belong to the same regression run.

The animation-like scrolling implements the ordered interpretation strategy described in the audio.

1. Animation
   Observation

   The cursor highlights the Sig. values .009 and .000 in the Coefficients table.

2. Audio
   Observation

   The speaker discusses p-values and statistical significance while those cells are selected.

1. Coefficients table

2. Sig. column

3. Career_Limitations row

4. Experience row

1. Selection moves from the Sig. value for Career_Limitations to the Sig. value for Experience.

1. The dependent variable remains Days_until_employed.

2. The table layout remains unchanged.

The visual emphasis directs attention to hypothesis-test results before effect-size interpretation.

1. Animation
   Observation

   The cursor highlights 2.658 in the Unstandardized Coefficients B column for Career_Limitations, then -4.044 for Experience.

2. Audio
   Observation

   The speaker interprets these numbers as changes in days until employed for one-unit or one-year changes in the predictors.

1. Unstandardized Coefficients B column

2. Career_Limitations row

3. Experience row

1. Selected cell changes from 2.658 to -4.044.

1. The same regression model and dependent variable remain on screen.

The animation links each numeric coefficient to its substantive directional interpretation in raw units.

1. Animation
   Observation

   The cursor moves to the Standardized Coefficients Beta column, where .223 and -.416 are visible.

2. Audio
   Observation

   The speaker says that thinking in terms of standard deviations requires looking at standardized coefficients.

1. The verbal explanation is incomplete because the clip ends mid-sentence.

1. Standardized Coefficients Beta column

2. Career_Limitations row

3. Experience row

1. Attention shifts from raw-unit coefficients to standardized coefficients.

1. The underlying model and predictors do not change.

The visual shift marks a change in measurement scale from original units to standard-deviation units.

1. Animation
   Observation

   Mouse cursor highlights the 'Std. Coefficients Beta' column, specifically moving between the values .233 and -.436.

1. SPSS Output Window

2. Coefficients Table

3. Mouse Cursor

1. Cursor moves to Beta column

2. Focus shifts between Career_motivation and Experience rows

1. Table structure remains static

2. Values do not change

Visual emphasis directs viewer attention to the standardized effect sizes being discussed in the audio.

1. Audio
   Observation

   The introduction promises interpretation of multiple regression output, but the excerpt only shows the dataset and variable definitions.

2. Diagram
   Observation

   No regression tables, coefficients, p-values, or model summaries appear in this segment.

Because the title mentions interpreting SPSS regression output, one might expect coefficient tables or significance tests in this excerpt.

This segment only introduces the variables and hypothesized directions. No actual regression output is displayed yet, so no numerical interpretation of model results can be made from this clip alone.

1. Audio
   Observation

   The speaker says the questions go through a scoring process and end up in this variable, and that this is an index.

One might treat Career_limitations as if it were the response to one direct question.

The video states that it is produced from a series of questions through a scoring process, so its meaning is that of a composite index rather than a single item score.

1. Audio
   Observation

   The speaker says there are assumptions for multiple regression but will not check them here, only focus on the output.

One might think that producing and reading regression output is sufficient evidence that the model is appropriate.

The video explicitly separates assumption checking from output interpretation and states that assumptions should be checked before running multiple regression, even though this clip does not perform that check.

1. Audio
   Observation

   The speaker says the Model Summary has R square and adjusted R square, and that they will interpret adjusted R square.

2. Formula
   Observation

   Both R Square = .252 and Adjusted R Square = .237 are visible.

A viewer might assume the plain R Square value is the one to report for explained variance in this demonstration.

In this clip, the speaker specifically selects adjusted R square for interpretation and reports .237 rather than .252.

1. Audio
   Observation

   The speaker contrasts career limitations, which increases days until employment, with experience, which decreases days until employment.

2. Diagram
   Observation

   The coefficients shown are +2.658 for Career_Limitations and -4.044 for Experience.

A predictor can be statistically significant without increasing the outcome; the sign determines direction.

In this example, both predictors are significant, but Career_Limitations has a positive association with Days_until_employed while Experience has a negative association.

1. Audio
   Observation

   The speaker says that when thinking in terms of standard deviations, one should look at the standardized coefficients.

2. Diagram
   Observation

   Both unstandardized B and standardized Beta columns are visible in the same table.

1. The clip does not fully develop the comparison before ending.

The raw coefficient and the standardized coefficient should not be treated as interchangeable.

Unstandardized coefficients are interpreted in the original units of the variables; standardized coefficients are interpreted in standard-deviation units.

1. Audio
   Observation

   Speaker says: 'We can see there's a 95% chance that the actual value of the unstandardized coefficient is between...'

Believing that a 95% confidence interval means there is a 95% probability that the specific true population parameter lies within the calculated interval.

In frequentist statistics, the true parameter is fixed, not random. The correct interpretation is that 95% of such intervals constructed from repeated sampling would contain the true parameter. The speaker's phrasing is a common pedagogical shortcut but technically imprecise.

1. Audio
   Observation

   The speaker first announces the topic of interpreting multiple regression output in SPSS and then immediately describes the variables in the SPSS Data Editor.

The general task of interpreting SPSS multiple regression output is instantiated here by first specifying the dataset structure and variable roles.

1. Audio
   Observation

   After stating there are two predictor variables, the speaker names Career Limitations and Experience and explains each.

The dataset structure includes the subset of variables functioning as predictors.

1. Audio
   Observation

   After stating there is one outcome variable, the speaker defines Days Until Employed.

The dataset structure includes the single dependent variable used as the regression outcome.

1. Audio
   Observation

   The speaker defines the predictors first and then says, "So we could have a couple hypotheses here before we conduct a multiple regression."

Understanding which variables are predictors is required before stating expected directional relationships between predictors and the outcome.

1. Audio
   Observation

   The outcome variable is defined immediately before the speaker states the expected effects on time to employment.

The hypothesized sign of the Experience effect is expressed relative to the outcome Days_until_employed, so the outcome definition must be established first.

1. Audio
   Observation

   The speaker explains that higher Career Limitations means more severe limitations, then predicts longer time to employment.

The positive directional hypothesis depends on the prior definition that larger index values represent greater limitation severity.

1. Audio
   Observation

   The speaker defines Experience in years of job history and then predicts that more experience is associated with fewer days.

The negative directional hypothesis depends on the prior operational definition of Experience as accumulated years of work.

1. Audio
   Observation

   The speaker first frames the task as focusing on output, then opens the Linear Regression dialog.

The general decision to focus on output interpretation is implemented by opening the SPSS Linear Regression procedure.

1. Diagram
   Observation

   The dialog contains separate Dependent and Independent(s) boxes, and the speaker assigns variables accordingly.

The regression setup procedure includes the distinction between dependent and independent variables.

1. Diagram
   Observation

   After selecting Descriptives and other options, the output includes Descriptive Statistics and related tables.

Requesting descriptives in the Statistics dialog leads to the Descriptive Statistics table appearing in the output.

1. Audio
   Observation

   The speaker interprets adjusted R square in Model Summary and then says, "Moving down to ANOVA..."

The demonstrated reading order treats model fit summary as preceding the overall significance test in the output walkthrough.

1. Audio
   Observation

   After discussing ANOVA significance, the speaker says, "Then we have the coefficients table."

The output interpretation proceeds from the overall model test to the individual coefficient estimates.

1. Audio
   Observation

   The speaker introduces the video as being about interpreting multiple regression output using SPSS.

1. Purpose of the clip: interpreting multiple regression output in SPSS

1. Audio
   Observation

   The speaker states there are two predictor variables and one outcome variable, then names them.

1. Structure of the example dataset
2. Predictor variables in the multiple regression example
3. Outcome variable in the multiple regression example

1. Audio
   Observation

   The speaker explains Career Limitations as an index built from multiple questions and says higher values mean more severe limitations.

1. Career_limitations as an index score

1. Audio
   Observation

   The speaker defines Experience as years of full-time or part-time job history.

1. Experience measured in years

1. Audio
   Observation

   The speaker states the expected directions for both predictors before conducting the regression.

1. Directional hypothesis for Career_limitations
2. Directional hypothesis for Experience

1. Diagram
   Observation

   Only the SPSS Data Editor is shown; no regression tables appear.

1. Purpose of the clip: interpreting multiple regression output in SPSS

1. Audio
   Observation

   The speaker says they will interpret adjusted R square and explains .237 as 23.7% of variance explained.

1. Model Summary table and choice of adjusted R squared
2. Interpretation of Adjusted R Square = .237

1. Audio
   Observation

   The speaker says the ANOVA result is statistically significant because the p value is less than .05.

1. ANOVA table as the overall model significance test
2. Overall regression model is statistically significant

1. Audio
   Observation

   The speaker reads the unstandardized coefficients for Career_limitations and Experience.

1. Coefficients table and unstandardized regression weights

1. Audio
   Observation

   The speaker names the tables in sequence from descriptive statistics through coefficients.

2. Diagram
   Observation

   The output viewer scrolls through the tables in that order.

1. Ordered reading of SPSS multiple regression output
2. Descriptive Statistics table in SPSS regression output
3. Correlations table in SPSS regression output
4. Variables Entered/Removed table
5. Model Summary table and choice of adjusted R squared
6. ANOVA table as the overall model significance test
7. Coefficients table and unstandardized regression weights

1. Audio
   Observation

   The speaker says assumptions exist for multiple regression but are not checked in this clip.

1. The exact assumptions are not listed.

1. Scope of the demonstration: interpreting output, not testing assumptions
2. Multiple regression has assumptions that should be checked before use

1. Audio
   Observation

   The speaker explains that .009 and a value less than .05 indicate statistical significance.

2. Diagram
   Observation

   The Sig. column is highlighted.

1. Using p-values to judge statistical significance
2. Career_Limitations is statistically significant in this model
3. Experience is statistically significant in this model