Anxiety Level Analysis with SPSS
Anxiety is a significant psychological state that can directly affect individuals' performance in many areas such as academic achievement, work performance, and social life. The scientific measurement and analysis of this emotion ensures that the psychological support processes needed by individuals are planned correctly. At this point, the SPSS program is one of the most reliable tools for the statistical evaluation of anxiety levels. If you also want to have your individual or academic anxiety data analyzed, or receive consultancy or SPSS services, you can contact us through Kutup Akademi Instagram page or our contact page and get professional support.
Anxiety is an important psychological state that can affect individuals' daily lives. Today, the effect of anxiety in many areas such as academic achievement, work performance, and social relationships is a research topic. The systematic evaluation of these effects is possible with statistical analysis methods. At this point, SPSS (Statistical Package for the Social Sciences) is a powerful tool frequently preferred in measuring anxiety levels and analyzing the relationship of these levels with various variables.
Definition and Measurement of Anxiety
Anxiety is an emotional response experienced by a person in the face of a perceived threat or danger. This emotion, which can reach levels that can be defined as anxiety disorder in clinical psychology, is measured through various scales.
Common Anxiety Scales and Analysis Process with SPSS
To scientifically measure anxiety levels and obtain meaningful data, various standardized scales are used in psychological research. These scales allow objective evaluation and comparison of individuals' anxiety states. The obtained data is processed in statistical analysis software such as SPSS to find answers to research questions.
1. Beck Anxiety Inventory (BAI)
This scale developed by Beck is used to evaluate the anxiety symptoms experienced by individuals in the last week. It consists of 21 items and each item is scored from 0-3. SPSS Analysis: Correlation, regression and group comparison tests can be performed on the total score. Relationships between anxiety and other variables (such as sleep quality, academic achievement, social support) can be analyzed.
2. State-Trait Anxiety Inventory (STAI)
This scale measures anxiety in two dimensions:
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State anxiety: Reflects the current anxiety level.
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Trait anxiety: Measures the individual's general anxiety tendency. There are 20 items for each dimension. STAI is particularly preferred for measuring anxiety levels before and after intervention studies. SPSS Analysis: Data can be processed with paired t-test, ANOVA, correlation and regression analyses.
3. Hamilton Anxiety Rating Scale (HAM-A)
This scale, administered by specialists, measures both psychological and somatic anxiety symptoms of individuals. This 14-item scale is frequently preferred in clinical settings. SPSS Analysis: Independent samples t-test or ANOVA can be performed for comparing clinical and control groups, and repeated measures analyses for symptom changes.
4. Anxiety Sensitivity Index (ASI)
Anxiety sensitivity is the individual's perception of anxiety symptoms as dangerous or threatening. ASI measures the level of this perception and is especially used in panic disorder research. SPSS Analysis: Relationships between ASI and variables such as anxiety, panic, and stress can be examined with predictive analyses (regression). Additionally, sensitivity differences between different age groups or genders can be analyzed.
Get Support for Your Research
If you are having difficulty applying such analyses or if your time is limited, we offer you expert support with our Stress and Coping Strategies Analysis with SPSS and SPSS analysis services.
Anxiety Analysis Process with SPSS
1. Data Collection
In order to perform analysis with SPSS, data must first be collected from a sample group through surveys or test applications. Participants are asked demographic information (age, gender, education level, etc.) and questions measuring anxiety levels.
2. Data Entry and Coding
The collected data is entered into the SPSS interface. Responses to survey questions are converted into scores and coded. For example:
| Participant No | Gender | Age | BAI Total Score |
|---|---|---|---|
| 1 | Female | 22 | 26 |
| 2 | Male | 28 | 15 |
3. Descriptive Statistics
In the first step, descriptive statistics such as mean, median, and standard deviation are obtained. This step is important for observing general trends in the data set.
Descriptive Statistics:
Mean BAI Score = 21.45
Std. Deviation = 7.234. Reliability Analysis (Cronbach's Alpha)
Cronbach's Alpha coefficient is calculated to test the internal consistency of scales. If this coefficient is above 0.70, the scale is reliable.
Cronbach's Alpha = 0.84 → Reliable5. Correlation Analysis
Pearson or Spearman correlation analysis can be performed to see the relationship of anxiety level with variables such as age, gender, and education.
Anxiety ↔ Age: r = -0.35, p < 0.01Interpretation: As age increases, anxiety level tends to decrease.
6. T-Test and ANOVA
For binary groups (e.g., female/male), independent samples t-test, and for more than two groups, one-way ANOVA is used to compare anxiety levels.
Females (Mean = 23.1), Males (Mean = 19.8)
t = 2.35, p = 0.021 → Significant difference exists.7. Regression Analysis
A regression model in which anxiety level is the dependent variable is created to test which variables affect anxiety.
Anxiety = β₀ + β₁(Gender) + β₂(Recent graduation) + ε
R² = 0.41 → Model explains 41% of anxiety.8. Factor Analysis (If Applicable)
In the scale development process or to test the validity of the structure, Exploratory Factor Analysis (EFA) can be performed together with Kaiser-Meyer-Olkin (KMO) and Bartlett's Test.
Applied Example: Anxiety Level Analysis in University Students
Research Questions:
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Do students' anxiety levels vary according to age, gender, and grade levels?
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Is there a significant relationship between academic achievement and anxiety?
Data:
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Sample: 300 students
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Scale: STAI
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Variables: Gender, Age, Grade Point Average, STAI Score
Findings:
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Anxiety score was higher in female students (p < 0.05).
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As age increased, a decrease in anxiety level was observed.
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A negative correlation was found between grade point average and anxiety (r = -0.42).
Additional Studies That Can Be Done with SPSS Anxiety Analysis
| Research Topic | Analysis Type | Application and Example Scenario |
|---|---|---|
| Anxiety and sleep quality | Correlation, Regression | The relationship between university students' anxiety levels and sleep quality is investigated. Correlation analysis is performed with SPSS to examine whether sleep quality decreases as anxiety increases. The direction and strength of this relationship is tested with regression. |
| Relationship between test anxiety and academic achievement | T-test, ANOVA | Students are grouped according to their test anxiety levels. Their exam score averages are compared with SPSS. ANOVA is used if there are 3 groups, t-test if there are 2 groups. |
| Anxiety levels after natural disasters | Repeated Measures ANOVA | Anxiety data is collected from individuals in an earthquake zone at 3 different time periods: immediately after the disaster, 3 months later, and 6 months later. Time-dependent change is evaluated by performing repeated measures ANOVA analysis with SPSS. |
| Anxiety change after psychological counseling | Paired t-test | Anxiety scores obtained from the same individuals before and after counseling are compared. The effect of the intervention is evaluated with paired t-test. |
| Socioeconomic status and anxiety | Regression | The extent to which variables such as income, education, and employment status explain anxiety level is tested with multiple regression. SPSS shows which variables are significant predictors. |
| Relationship between social support, self-efficacy and anxiety | Mediation Effect Analysis with SEM | It is investigated whether anxiety level decreases as perceived social support increases in university students. However, social support is not the only factor in this relationship. The researcher thinks that students' self-efficacy perceptions may also play an important role in this relationship. Therefore, whether there is an indirect effect in the direction of "Social support → Self-efficacy → Anxiety" is tested with Structural Equation Modeling (SEM). This analysis performed using SPSS AMOS or PROCESS add-on can reveal that individual psychological empowerment (increased self-efficacy) work needs to be done at the same time for providing support to students to be effective. |
| Effect of gender on anxiety level | Moderator Effect Analysis with SEM | The researcher knows that test anxiety affects students' academic achievement. However, they wonder whether this effect occurs at different levels in male and female students. Therefore, whether the "Gender" variable plays a moderator role on this relationship is tested with SEM (AMOS) or PROCESS macro (SPSS). |
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In studies on anxiety and sleep quality, whether there is a statistical relationship between students' anxiety levels and sleep quality is examined. Correlation and regression analyses are frequently used in such research. If you also want to easily perform similar analyses, you can check out our SPSS data analysis service.
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In studies investigating the relationship between test anxiety and academic achievement, students are grouped according to their test anxiety levels and the exam achievement averages of these groups are compared. These differences can be statistically tested using t-test or ANOVA in SPSS. You can get support from our SPSS analysis page for such an analysis process.
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In research measuring individuals' anxiety levels after natural disasters, repeated measures ANOVA analysis is performed with data collected at different time periods. You can benefit from our SPSS data analysis service when applying such longitudinal data analyses with SPSS.
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In studies comparing anxiety levels before and after psychological counseling, the effect of the intervention is tested using paired t-test. Our SPSS analysis service can help you for detailed analysis in such intervention studies.
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In studies examining the effect of socioeconomic status on anxiety level, explanatory models can be established by including variables such as income, education level, and occupation in regression analysis. You can visit our SPSS data analysis page for detailed support on multiple regression applications with SPSS.
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In studies examining the relationships between social support, self-efficacy and anxiety, researchers often use mediation effect analysis. Especially for testing indirect effects in the direction of "Social support → Self-efficacy → Anxiety", our mediation variable analysis service offers a detailed solution.
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In studies aimed at understanding how gender affects the relationship between test anxiety and academic achievement, moderator variable analysis is preferred. You can review our moderator variable analysis service to test such moderator effects.
Conclusion
Determining anxiety levels is of great importance for evaluating individuals' psychological health and establishing necessary intervention programs. Anxiety analyses performed with SPSS have an important place both in academic research and in institutional psychological evaluations. Data analyzed with the right methods contributes not only to individual awareness but also to the development of social solution proposals.
While conducting detailed research on anxiety analysis with SPSS, those who want to receive both academic consultancy and applied data analysis support can visit the instagram page. Here, you can find information about sample studies, educational content, and consultancy services related to analysis processes.
