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Using SPSS for Confidence Intervals: Estimating Population Means and Proportions, Exercises of Statistics

London School of Hygiene & Tropical Medicine (LSHTM)Statistics

Instructions on how to use spss software to estimate population means and proportions with confidence intervals. It covers the use of spss for known and unknown population standard deviations, and includes steps for creating and importing data. The document also explains how to define variable types and values in spss.

Typology: Exercises

2021/2022

Uploaded on 09/12/2022

claire67
claire67 🇬🇧

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Using SPSS, Chapter 7:
Confidence Intervals
•Chapter 7.2 - Estimating a Population Mean (σknown)
SPSS doesn’t do this the same way it is done in the book.
Click here for online calculators that work well.
•Chapter 7.3 - Estimating a Population Proportion
SPSS doesn’t do this the same way it is done in the book.
Click here for online calculators that work well.
•Chapter 7.4 - Estimating a Population Mean (σunknown) 2
SPSS does this really well but you do need the raw data.
If you are working with summary statistics use one of the online calculators found here.
•Creating and Importing Data 4
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Download Using SPSS for Confidence Intervals: Estimating Population Means and Proportions and more Exercises Statistics in PDF only on Docsity!

Using SPSS, Chapter 7:

Confidence Intervals

• Chapter 7.2 - Estimating a Population Mean (σ known)

SPSS doesn’t do this the same way it is done in the book. Click here for online calculators that work well.

• Chapter 7.3 - Estimating a Population Proportion

SPSS doesn’t do this the same way it is done in the book. Click here for online calculators that work well.

• Chapter 7.4 - Estimating a Population Mean (σ unknown) 2

SPSS does this really well but you do need the raw data. If you are working with summary statistics use one of the online calculators found here.

• Creating and Importing Data 4

Chapter 7.4 - Estimating a Population Mean (σ unknown)

  • Open or create the sample data.
  • In the Statistics Viewer choose Analyze → Compare Means → One-Sample T Test...
  • This opens another dialogue box.
  • The results of this test are displayed in the Statistics Viewer.

Creating and Importing Data

  • There are two ways to get data into SPSS.
    • You can enter the data by typing it directly into the data editor.
    • You can open an existing data file by selecting the File tab, then Open , then Data.... Then select the type of file from the list of options. If it is not already an SPSS (.sav) data file, you will be prompted to answer some questions. For example, if you open an Excel file it may ask which worksheet and whether or not the first row contains labels.
  • Make sure your data is formatted as described below.
    • Rows = Cases Each row represents a case such as each respondent to a questionnaire.
    • Columns = Variables Each column represents a variable being tracked or measured. For example, the answers to a specific question on a questionnaire defines it’s own variable (column). As such, each row represents an individual case for all variables.
    • Cells contain values Each cell contains a single value of a variable for a case.

It is possible to enter data in the form of a frequency table but then you must do some alterations before analyzing such data.

  • Once you have the data opened in the data editor, click the Variable View tab at the bottom of the data editor. In this view, each variable is now a row and you must make sure all your variables are defined appropriately. The most important distinctions are - TYPE : The most common types are ∗ Numeric: Used for quantitative data. These are numbers with no commas and a period delimiting the decimal places. SPSS will not allow you to enter non-numeric characters into a cell of numeric type. ∗ Date: Used for dates or times from a menu of formats. ∗ String: Used for qualitative data. Avoid symbols such as *, -, +, ?, etc. - Measure : There are three levels of measurement. ∗ Scale is for ratio or interval levels of measurement. ∗ Ordinal is for ordinal or ranked data. ∗ Nominal is for qualitative data. - Values : If you have numeric values representing qualitative data such a 1=male and 0=female, you will probably want this to be labelled accordingly in graphs and outputs. Click on the cell in the Values column for that variable and assign labels for each value.