What Is Coefficients? How to Read Regression Coefficients in SPSS

What Is Coefficients?

When you run a linear regression in SPSS, the Coefficients table shows how each independent variable is related to the dependent variable, in which direction, and whether the statistical evidence is sufficient. This is usually the first table you open when testing hypotheses such as “X affects Y”.

You will usually see the columns B, Std. Error, Beta, t and Sig. in this table. B is the unstandardized regression coefficient, while Beta is the standardized regression coefficient. For a simple regression model, the equation can be written as:

Y = a + B1X1 + B2X2 + ... + e

Here, a is the constant, B1 and B2 are the unstandardized regression coefficients of the independent variables, and e is the error term. When the variables are converted to the same standardized scale, Beta helps you compare the relative effect of the independent variables.

If you are reading results from a model with a mediator or moderator, identify the role of each variable before interpreting the coefficients. You can also review moderator in a research model and moderating in quantitative analysis to avoid confusing a direct effect with an interaction effect.

Why Coefficients Matters in Quantitative Research

The Coefficients table answers four practical questions. First, does the independent variable have a positive or negative relationship with the dependent variable? Second, how much is Y expected to change when X increases by one unit? Third, is the coefficient statistically significant? Fourth, which variable has the stronger relative effect when the variables are compared in the same model?

The B column answers the question about change in the original measurement units. For example, if B = 0.420, a one-unit increase in X is associated with a predicted increase of 0.420 units in Y, while the other variables remain constant. This interpretation is useful when you want to present the regression equation or predict a specific value.

The Beta column answers the question about relative effect size. Because Beta is standardized, you can compare the Beta for X1 with the Beta for X2 in the same model. A positive Beta indicates a positive relationship, while a negative Beta indicates a negative relationship. The larger the absolute value of Beta, the larger the relative effect, provided that the variables are tested in the same model and with the same data sample.

The Sig. column is the p-value used to test the hypothesis about the coefficient. When Sig. < 0.05, many theses use this result as evidence for rejecting H0 that the coefficient equals 0. You still need to read it together with the sign of B or Beta. A small p-value shows that there is statistical evidence for a relationship, but it does not by itself show whether the relationship is positive or negative.

You also need to read Coefficients alongside the other output tables. Model Summary reports R Square and Adjusted R Square. ANOVA shows whether the overall model is statistically significant. Checks of VIF, residuals and outliers help you assess whether the model violates any assumptions. If the data contain unusual observations, check outliers in quantitative data before deciding whether to remove an observation.

What Coefficients Value Is Acceptable?

There is no single threshold that applies to every column in the Coefficients table. For a regression coefficient, the important points are whether the direction matches the hypothesis, whether Sig. meets the level you specified, and whether the model seriously violates any regression assumptions. The thresholds below are common reporting practices that must be interpreted in the context of your topic and theoretical foundation.

Do not conclude that “Beta must be at least 0.5 to be acceptable”. Beta is not a pass or fail score like Cronbach's Alpha, AVE or outer loading. A small Beta can still be statistically and practically meaningful depending on the context. Conversely, a large Beta with an unacceptable Sig. does not provide enough evidence to conclude that the hypothesis is supported.

If the model contains several independent variables, report B, Beta, t, Sig. and VIF together. Selecting only the largest Betas for ranking while ignoring the reliability of the estimates makes the interpretation difficult to defend.

How to Read Coefficients in the Output

After running the regression in SPSS, go to Analyze > Regression > Linear. Move the dependent variable into Dependent and the explanatory variables into Independent(s), then select the required statistics under Statistics. Under Estimates, SPSS will produce the Coefficients table.

Reading B and Constant

The (Constant) row is the constant in the equation. The B value in this row is the predicted value of Y when all independent variables equal 0. With a Likert scale, a situation in which all variables equal 0 may not occur in the actual data, so do not overinterpret the constant.

In the rows for the independent variables, Unstandardized Coefficients B shows the expected change in Y when that variable increases by one unit, while the other variables remain unchanged. If B equals 0.420, the expected change in Y is 0.420 units in the positive direction. If B equals -0.180, Y decreases by 0.180 units when X increases by one unit, with the other variables held constant.

Reading Beta, t and Sig.

Standardized Coefficients Beta is used for relative comparison. For example, the Beta for X1 is 0.452 and the Beta for X2 is 0.210. If both variables have an acceptable Sig., X1 has a larger relative effect than X2 in this model.

The t column is the test statistic for the coefficient. The Sig. column is the corresponding p-value. Read them in sequence: check the sign of Beta, check Sig., and then compare the result with the hypothesis. A hypothesis stating that “X has a positive effect on Y” is supported when Beta is positive and Sig. meets the selected significance level.

The table below is illustrative output, not the result of a real study. The column names remain as SPSS displays them in the Coefficients table.

Based on the illustrative table, the unstandardized regression equation can be written as follows:

Y = 1.125 + 0.452X1 + 0.217X2 + 0.098X3

Here, X1 is Service quality, X2 is Convenience and X3 is Trust. X1 has a Beta of 0.438 and a Sig. of 0.000, so it can be interpreted as having a positive and statistically significant effect at the 0.05 threshold. X3 has a positive Beta but a Sig. of 0.169, so there is not enough statistical evidence to conclude that X3 has an effect in the illustrative model.

How to write this in your thesis

You can adjust the following sentence to match your actual results: “The regression results show that [name of independent variable] has a [positive/negative] effect on [name of dependent variable], with a standardized regression coefficient of Beta = [value] and a significance level of Sig. = [value]. Since [Sig. is less than/greater than] 0.05, hypothesis H[ number ] is [supported/not supported].”

Replace every bracketed part with the result in your SPSS file. Do not call a hypothesis accepted only because Beta has the expected sign. You need to check Sig., the way the hypothesis was stated and the selected significance level.

What to Do When Coefficients Is Not Significant

First, identify where the problem appears. If Sig. is greater than 0.05, the available statistical evidence for the effect is insufficient. If the Beta sign is opposite to the hypothesis, the observed relationship runs against your expectation. If VIF is high, multicollinearity may be affecting the coefficient. If the standard error is too large, the estimate may be unstable.

Check the Coding and Scale

Open Variable View and check the variable names, data types, missing values, reverse-coded variables and the way the composite scores were calculated. An item that should have been reverse-coded but was not recoded can distort the scale score and produce an unexpected regression coefficient. If the scale is calculated from several items, review what a scale is and how a scale is formed.

Check Correlations and Multicollinearity

Review the correlation matrix for the independent variables. Variables that are too similar can increase VIF and cause the partial coefficients to change substantially. In the Coefficients table, VIF below 5 is commonly referenced according to (Hair et al., 2019), but you still need to consider the theory before removing a variable.

Check the Data and the Model

Review observations with high influence, the residuals and the way the data were entered. Do not remove a row simply because it makes Sig. look better. Remove an observation only when there is a clear data-related reason, such as an invalid response, an entry error or a respondent who did not meet the sample criteria, and record that decision in your data-processing procedure.

If a variable remains nonsignificant after reasonable checks, report the nonsignificant result instead of rerunning the analysis repeatedly until you find a significant version. You may consider an alternative model only when it has a theoretical basis and was specified in advance, not because you want to change the result.

Distinguishing Coefficients from Similar Statistics

Coefficients is the table name, while coefficient is a general term for a coefficient. You therefore need to distinguish each type of coefficient by looking at the table and its purpose.

B is the unstandardized coefficient, used for an equation in the original units. Beta is the standardized coefficient, used to compare relative effects. These two values are usually different, and you should not replace B with Beta when writing a prediction equation.

Correlation coefficient is the correlation coefficient, usually written as r, and describes the relationship between two variables before controlling for other variables. The Regression coefficient in the Coefficients table describes the partial effect of one variable while the other variables in the model are held constant. A high correlation does not guarantee that the partial regression coefficient will remain high.

Covariance describes how variables vary together but depends on the measurement units, while correlation is standardized to a range from -1 to 1. In AMOS, you may encounter variance, covariance and regression weight values. Regression weight is close to the unstandardized regression coefficient, while standardized regression weight is close to Beta.

Factor loading is the loading of an item on a factor in EFA or CFA. It is not the same as Beta in regression. For EFA, a factor loading from 0.5 up is commonly referenced according to (Hair et al., 2010). In SmartPLS, outer loading belongs to the measurement model, while path coefficient belongs to the structural model. Read these statistics at the appropriate stage of the analysis.

Cronbach's Alpha assesses the internal consistency of a scale. It does not show how much an independent variable affects a dependent variable. R Square shows the proportion of variation in the dependent variable explained by the model, and it is also not the Beta coefficient. If your topic includes a mediator, separate the direct, indirect and total effects. The concept of mediator is often confused with an ordinary independent variable.

Common Errors

The first error is looking only at Beta and ignoring Sig. A Beta of 0.600 does not automatically mean that the hypothesis is supported if Sig. is not significant. The second error is looking only at Sig. without reading the sign of Beta, which can lead you to state that a variable has a positive effect when the actual coefficient is negative.

The third error is using Beta to write an unstandardized regression equation. If the sentence says “Y increases by 0.452 units”, you must take the value from the B column, not the Beta column. The fourth error is comparing Beta values from different models, different samples or different measurement methods as though they were obtained under the same conditions.

The fifth error is calling every value in the table a “regression coefficient”. Std. Error, t, Sig. and VIF have different roles. When reporting the results, state the column name, value, direction of the effect and decision about the hypothesis.

The final error is rerunning the regression many times and keeping only the table with the most attractive result. If you adjust the data or model, record the reason, criteria and order of the adjustment. Thesis readers often ask about this part when results change between tables.

Frequently asked questions

What is Coefficients in SPSS?

Coefficients is an output table from linear regression. It presents the coefficients for the constant and independent variables together with the standard error, Beta, t, Sig. and sometimes VIF. Use this table to examine the direction of the effect, relative effect size and statistical significance of each variable.

What Beta value is acceptable?

Beta has no universal pass threshold. Check whether Beta has the sign predicted by the hypothesis, whether Sig. meets the selected significance level and whether the model satisfies the regression conditions. A larger Beta does not automatically mean a better result.

What is the difference between B and Beta in Coefficients?

B is the unstandardized coefficient. It retains the original units of the variables and is used to write the regression equation in those units. Beta is the standardized coefficient. It is used to compare the relative effects of independent variables in the same model.

What Sig. value in the Coefficients table is acceptable?

In many theses, Sig. < 0.05 is used as the criterion for concluding that a coefficient is statistically significant. State the significance level in the research methods section and read Sig. together with the sign of B or Beta, rather than using the p-value separately from the hypothesis.

Why is Beta in the expected direction but Sig. not significant?

This means that the estimated coefficient has the expected sign, but the current data do not provide enough statistical evidence at the selected significance level. Check the sample size, data quality, multicollinearity, measurement error and model specification, then report the result honestly if the hypothesis is not supported.

Open your SPSS file, go to the correct Coefficients table, check B, Beta, t, Sig. and VIF in order, then write each hypothesis in one sentence using the actual values. If you need to run and interpret the analysis using your own .sav or .csv file, you can use M4 data analysis by DoThesis.