How to test one way ANOVA in SPSS?

ANOVA or Analysis of Variance is conducted to determine the significant differences between the means of three or more independent variables. Specifically, this test is conducted to test Null hypothesis.

When to use ANOVA?

When a group is randomly split into 3 or more smaller groups, in order to undertake different tasks and measure the outcome of the dependent variables. For example we are studying the Marathon running speed of a group of individuals who belong to 3 different ‘Course’ levels: Beginner, Intermediate, and Advanced. Now if the researcher wants to know the outcome of different pacing strategies with respect to the course, then he/she would randomly divide the group of individuals participating in the marathon into three different groups with different speed. The time taken by different groups to complete the marathon would indicate the outcome of the study (i.e. time) which is regarded as the dependent variable (because time is dependent on ‘Course’ and Speed).

One way ANOVA in SPSS

  • Step 1: In Menu, Click Analyze -> Compare Means -> One way ANOVA (figure 1)
Figure 1: One way Anova
Figure 1: One way Anova
  • Step 2: Once clicked, One-Way ANOVA dialog box will open as shown in Figure 2 given below:
Figure 2: One way Anova
Figure 2: One way Anova

 

  • Step 3: Next transfer the “Dependent List” i.e. Time taken to complete the Marathon; and Independent variable i.e. course (Beginner, Intermediate and Advanced) into “FACTOR” (Figure 3)
Figure 3: One way Anova
Figure 3: One way Anova

 

  • Step 4: Once that is done Click on the Post Hoc button and select “Tukey” and Click on Continue (Figure 4).
Figure 4: One way Anova
Figure 4: One way Anova
  • Step 5: Next Click on Options, and a new dialog box would open. In this dialog box, check ‘Descriptive’ as shown below. Click Continue and then Click OK (Figure 5).
Figure 5: One way Anova
Figure 5: One way Anova

Output and Results

The Descriptives Table given below (Table 1) provides useful information about the descriptive statistics including Mean, Median, Standard Deviation and 95% confidence interval with respect to Dependent variable i.e. Time for three groups i.e. Beginners, Intermediate and Advanced. Here “N” reflects the sample size for each group. Mean is the average time taken to complete the marathon within individual group. This reflects that the average time taken by advanced course individual was least although in comparison to Intermediate, we can see that there is not much difference in the average mean time. 95% confidence interval for Mean reflects the minimum and maximum time taken by the individual at 95% significance.

Table 1: Descriptive Statistics
Table 1: Descriptive Statistics

 Legends:

  • N: Number of respondents in the sample size
  • Mean: Average Time taken by respondents within the sample
  • Standard Deviation: Deviation in average time of each pacer within the sample.
  • 95% Confidence Interval for Mean: It estimates the mean response for each group level i.e. Beginner, Intermediate and Advanced at 95% confidence interval. The difference in Upper Bound and Lower Bound values (i.e. since they are not overlapping) indicates that there is difference in population means between three group levels.
  • Maximum/Minimum: Simply reflects the maximum and minimum time scored by pacers within each group i.e. Beginner, Intermediate and Advanced

Interpretation of ANOVA table

The Table 2 shown below reflects the results of ANOVA analysis which would enable the researcher to determine the difference between the group means. The results reflect statistically significant p-value i.e. p=0.021 (p< 0.05) and shows that the difference between the course taken is significantly related to mean length of time. However, this table does not give the measure of difference for different runners within the individual groups and therefore the results of Post Hoc will be analyzed for same.

Table 2: Anova
Table 2: Anova

The previous results indicate that there is significant difference between groups. Now Table 3 shows the Multiple Comparison Table which reflects the differences between individual groups.

Tukey is considered to be the preferred test for Post Hoc analysis in case of One Way ANOVA. As can be seen in Table 3 given below, there is significant difference in time to complete the marathon between the group which took intermediate and beginner course to complete the marathon (p =0.046). Also significant difference is reflected between groups who took beginner and advanced course (p =0.034).  However, there is no difference between groups who took intermediate and advanced course (p =0.989)

Table 3: Multiple comparisons
Table 3: Multiple comparisons

Inference: On the basis of above results, one can be interpret that there is a statistically significant difference between groups as by one-way ANOVA shows (F = 4.467, p = .021). A Tukey post-hoc test revealed that the time to complete the race was statistically significantly lower after taking the intermediate (23.6 ± 3.3 min, p = .046) and advanced (23.4 ± 3.2 min, p = .034) course compared to the beginners course (27.2 ± 3.0 min). There were no statistically significant differences between the intermediate and advanced groups (p = .989).

Special cases within chi square test

Priya Chetty

Partner at Project Guru
Priya is a master in business administration with majors in marketing and finance. She is fluent with data modelling, time series analysis, various regression models, forecasting and interpretation of the data. She has assisted data scientists, corporates, scholars in the field of finance, banking, economics and marketing.
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2 thoughts on “How to test one way ANOVA in SPSS?”
  1. Avatar shilpa katira 2 years ago

    very helpful spss interpretations

    • Avatar Shruti Datt 2 years ago

      Dear Shilpa,
      Thank you. Do scroll through other articles in SPSS.

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