SPSS is a software that can conduct basic and complex statistical analyses. In this assignment, you will use SPSS to explore the data and conduct descriptive statistics, create frequency charts, recode variables, and create new variables

SPSS is a software that can conduct basic and complex statistical analyses. In this assignment, you will use SPSS to explore the data and conduct descriptive statistics, create frequency charts, recode variables, and create new variables

SPSS is a software that can conduct basic and complex statistical analyses. In this assignment, you will use SPSS to explore the data and conduct descriptive statistics, create frequency charts, recode variables, and create new variables.

For this assignment, utilize SPSS Statistics and the “Health Behavior Data Set.”

Refer to the topic Resources for assistance with accessing and using SPSS.

Follow the steps below to complete the assignment.

Part 1

1. Import the Excel file into SPSS.
2. Sort by age in SPSS to determine the age-range of participants in the data set.
3. Use the Descriptive Statistics Feature in SPSS to find the mean and standard deviation for the “Age” and “Annual Income.”
4. Use the “Frequencies” feature to create frequency tables for the employed, education level and sex variables.
5. Recode the sex variable where “Male”=1 and “Female”=2. Create a frequency table for the new variable.
6. Compute a new “BMI” variable based on “Weight” and “Height.” Use the Descriptive Statistics Feature in SPSS to find the mean and standard deviation for the new BMI variable.

Part 2

In 200-250 words, compare Excel and SPSS. Discuss specific SPSS software features that make it preferable to Excel for data management. Provide examples illustrating when electing to use SPSS could be preferable to Excel in regard to analyzing survey data.

General Requirements

Submit the SPSS exported output and the 200–250-word Excel and SPSS comparison to the assignment dropbox.

APA style is not required, but solid academic writing is expected.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to LopesWrite.

Expert Answer and Explanation

Part 1

GET DATA
/TYPE=XLSX
/FILE=’C:\Users\PRIMERA\Downloads\PUB-550-RS-T2-T3-T4-HealthBehaviorDataset (2).xlsx’
/SHEET=name ‘Data’
/CELLRANGE=FULL
/READNAMES=ON
/DATATYPEMIN PERCENTAGE=95.0
/HIDDEN IGNORE=YES.
EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
SORT CASES BY Age(A).
MEANS TABLES=Age BY Annual_Income
/CELLS=MEAN COUNT STDDEV.
DESCRIPTIVES VARIABLES=Age Annual_Income
/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics 

FREQUENCIES VARIABLES=Employed Education_Level Sex

/ORDER=ANALYSIS.

Frequencies

Statistics

 Frequency Table

Employed

 Education_Level*** 

 Sex

 RECODE Sex (‘Male’=’1’) (‘Female’=’2’). EXECUTE.

FREQUENCIES VARIABLES=Sex

/ORDER=ANALYSIS.

Frequencies

Statistics

Sex

Sex

COMPUTE BMI= Weight / Height. EXECUTE.

DESCRIPTIVES VARIABLES=BMI

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics

Part 2

Comparison between SPSS and Excel

Excel and SPSS (Statistical Package for the Social Sciences) are the most common data analysis and management tools. Despite featuring some standard outputs, these statistical tools serve varied functionalities. Excel is the most popular of the two, given its basic data entry, calculations, and visualisations (Cainas et al., 2021). Additionally, users could employ the two when dealing with small data sets and straightforward analyses. For instance, excel is suitable when evaluating averages, counts, or trend lines. Nonetheless, the application of Excel is limited, especially with large datasets, which calls for a complex tool like SPSS.

The SPSS is the most dominant tool used by statisticians and other users of large data sets. The preference is because of its ability to provide outputs such as regression analysis, ANOVA, factor analysis, and non-parametric tests. Other significant characteristics of the statistical tool include reliability testing, complex data transformations, missing data handling, and variable labeling. These features are instrumental, especially in quantitative and qualitative data analyses.

For instance, SPSS is superior to Excel when an advanced statistical test is needed (Rahman & Muktadir, 2021). The tool provides outputs such as t-tests, ANOVA, chi-square tests, or regression analysis, while Excel requires activation of add-ins before getting these results. Moreover, users can categorize nominal and ordinal data when using SPSS, which is lacking in Excel. Most importantly, SPSS allows the management of missing numbers, which is only possible in Excel via manual detection.

SPSS utilizes listwise or pairwise deletion and imputation approaches to identify missing numbers. Although Excel is a useful statistical tool, its limitations, such as identifying missing numbers, dealing with small data sets, and issues with advanced statistical tests, make SPSS the preferable data management and analysis tool.

References

Cainas, J. M., Tietz, W. M., & Miller-Nobles, T. (2021). KAT insurance: Data analytics cases for introductory accounting using Excel, Power BI, and/or Tableau. Journal of Emerging Technologies in Accounting, 18(1), 77-85. https://www.researchgate.net/profile/Jennifer-Cainas/publication/346999741_KAT_Insurance_Data_Analytics_Cases_for_Introductory_Accounting_Using_Excel_Power_BI_andor_Tableau/links/649302a88de7ed28ba429c0c/KAT-Insurance-Data-Analytics-Cases-for-Introductory-Accounting-Using-Excel-Power-BI-and-or-Tableau.pdf?origin=journalDetail&_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9

Rahman, A., & Muktadir, M. G. (2021). SPSS: An imperative quantitative data analysis tool for social science research. International Journal of Research and Innovation in Social Science, 5(10), 300-302. https://www.academia.edu/download/79116055/300-302.pdf

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FAQs

Can SPSS be used for descriptive statistics?

Yes, SPSS (Statistical Package for the Social Sciences) can be used for descriptive statistics. In fact, it is one of the most common tools used for this purpose in research and data analysis.

Using SPSS, you can easily generate descriptive statistics such as:

• Measures of central tendency (mean, median, mode)

• Measures of dispersion (standard deviation, variance, range)

• Frequencies and percentages

• Minimum and maximum values

• Skewness and kurtosis (for distribution shape)

These outputs help summarize and understand the basic features of a dataset before conducting more complex analyses.

How to Run Descriptive Statistics in SPSS:

1. Open your dataset in SPSS.

2. Go to Analyze > Descriptive Statistics > Descriptives or Frequencies.

3. Select the variables you want to analyze.

4. Choose the specific statistics you want to calculate.

5. Click OK to generate the output.

What is the explore function in SPSS?

The Explore function in SPSS is a powerful tool used to perform descriptive and exploratory data analysis. It provides detailed statistics, plots, and tests that help you understand the distribution, central tendency, spread, and normality of your data.

Key Features of the Explore Function:

• Descriptive statistics: Mean, median, standard deviation, minimum, maximum, etc.

• Outlier detection: Helps identify outliers and extreme values.

• Normality tests: Shapiro-Wilk and Kolmogorov-Smirnov tests.

• Visuals: Boxplots, histograms, stem-and-leaf plots, and normal Q-Q plots.

When to Use the Explore Function:

• When you want a more detailed analysis than basic descriptive statistics.

• To check for normality before running parametric tests (like t-tests or ANOVA).

• To compare multiple groups on one or more variables.

How to Use the Explore Function in SPSS:

1. Open your dataset.

2. Go to Analyze > Descriptive Statistics > Explore.

3. Move the variable(s) you want to analyze into the Dependent List.

4. (Optional) Move a categorical variable into the Factor List to split the analysis by groups.

5. Click on Plots to choose options like histograms, boxplots, or normality plots.

6. Click OK to run the analysis.

How to conduct data analysis on SPSS?

Conducting data analysis in SPSS involves several steps, depending on your research questions and the type of analysis you want to perform (e.g., descriptive statistics, correlation, regression, t-tests, ANOVA, etc.).

General Steps to Conduct Data Analysis in SPSS:

1. Enter or Import Your Data

• You can type data directly into SPSS or import it from Excel, CSV, or other file formats.

• Go to File > Open > Data to load an existing file.

• Make sure variables are correctly labeled and coded.

2. Define Your Variables (if needed)

• Use Variable View to:

  • Name your variables.

  • Define variable types (numeric, string).

  • Set labels for variables and values (especially for categorical data).

  • Handle missing values.

3. Explore and Clean Your Data

• Check for:

  • Missing values – use Analyze > Descriptive Statistics > Frequencies or Explore.

  • Outliers – use boxplots via Analyze > Descriptive Statistics > Explore.

  • Data accuracy – verify data ranges and coding.

4. Run Descriptive Statistics

• Go to Analyze > Descriptive Statistics and choose:

  • Frequencies – for counts and percentages (useful for categorical data).

  • Descriptives – for mean, SD, min, max (useful for numeric data).

  • Explore – for detailed analysis and visualizations.

5. Conduct Inferential Statistics (Based on Your Research Question)

6. Interpret the Output

• SPSS provides output in tables and charts.

• Pay attention to:

  • P-values (typically significant if < 0.05).

  • Means, SDs, Correlation coefficients, etc., based on your test.

  • Graphs (boxplots, histograms) to visualize data trends and distributions.

7. Export or Report Results

• You can copy tables and graphs directly from SPSS Output Viewer into Word or Excel.

• Go to File > Export to save the output as PDF, Word, or other formats.

How to interpret SPSS output descriptive statistics?

Interpreting SPSS output for descriptive statistics involves understanding the meaning of the numbers and what they tell you about your data. Here’s a step-by-step guide to help you interpret the most common elements of the output.

1. Mean (Average)

• What it shows: The arithmetic average of your data.

• Interpretation: Gives you the central value. Useful for normally distributed data.

  • Example: A mean of 75 in a test score variable means the average score is 75.

2. Median

• What it shows: The middle value when data is ordered.

• Interpretation: Less sensitive to outliers than the mean.

  • Example: If the median is 60, half the values are below 60 and half are above.

3. Mode

• What it shows: The most frequently occurring value.

• Interpretation: Useful for identifying common responses, especially for categorical data.

4. Standard Deviation (SD)

• What it shows: How spread out the values are from the mean.

• Interpretation:

  • A small SD = values are close to the mean (less variability).

  • A large SD = values are more spread out.

  • Example: An SD of 2 is tight around the mean; an SD of 15 suggests a wide spread.

5. Minimum and Maximum

• What it shows: The smallest and largest values.

• Interpretation: Helps identify the range and potential outliers.

  • Example: Min = 10, Max = 100 → Range = 90.

6. Range

• What it shows: Difference between maximum and minimum values.

• Interpretation: Gives a quick idea of data spread.

7. Skewness

• What it shows: Whether the data is symmetrically distributed.

• Interpretation:

  • 0 = perfectly symmetrical

  • Positive skew = tail on the right (more low values)

  • Negative skew = tail on the left (more high values)

8. Kurtosis

• What it shows: The “peakedness” of the distribution.

• Interpretation:

  • 0 = normal distribution

  • Positive = more peaked than normal

  • Negative = flatter than normal

Example SPSS Output Interpretation:

• The average score is 85.2.

• Scores vary around the mean by about 10.3 points.

• The scores range from 60 to 100.

• The data is slightly negatively skewed (more high scores).

• Kurtosis suggests the data is a bit more peaked than normal.

What is the difference between SPSS and Excel?

The main difference between SPSS and Excel lies in their purpose and capabilities:

🔍 1. Purpose

📊 2. Statistical Analysis

🖥️ 3. Interface and Usability

📉 4. Graphs and Visualization

🔐 5. Data Handling