Preparing data for spss analysis
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The document summarizes steps taken to prepare data for analysis in SPSS, including replacing missing data, importing a file into SPSS, and modifying data types. It then describes two analyses conducted: 1) using a general linear model to predict total perceived stress based on marital status, age group, and their combination, finding the model and some factors insignificant; and 2) predicting total perceived stress based on additional variables of total life satisfaction, with some combined factors found to have no significant difference and low overall model significance.
Preparing data for spss analysis
- 1. Research Methodology Project on how to Prepare Data for SPSS Analysis
- 2. Step 1. Opened file in Excel and replaced the N/A data. – Data Reduction Step 2. Import the updated file into SPSS from .csv file Step 3. Modified the data types to scale, nominal or ordinal as per the question. Preparing Data for SPSS Analysis
- 3. Ques1. Prediction of Total Perceived Stress(TPS) by Marital Status and Age Group? General Linear Model- Univariate is used for prediction of TPS by marital status(Nominal), age group(ordinal) and combined.
- 4. • Our overall model’s significance value is .026 i.e. it is less that .05, so it is significant. • But the agerp3 shows a insignificant result. • TPS is not significant with Combined age group 3 and marital status. • Adjusted R^2 is 4.2% which is very low, so the model can be modified.
- 5. Ques. 2 Prediction of Total Perceived Stress(TPS) by Marital Status, Age Group and Total Life Satisfaction General Linear Model- Univariate is used for prediction of TPS by marital status(Nominal), age group(Ordinal), total life satisfaction(Scale) and combined. Age group3 -1 with marital status 1 and 4 appear to show no significant difference. There is no 0 between upper and lower boundary limit.
- 6. • Combined significance of all variables is very low that is why overall model is significant. • The r-squared value is 0.252 which means that 25% proportion of the variance for a dependent variable that's explained by an independent variable.