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SPSS Session 1: Levels of Measurement and Frequency Distributions PowerPoint Presentation

SPSS Session 1: Levels of Measurement and Frequency Distributions

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### SPSS Session 1:Levels of Measurement and Frequency Distributions

Learning Objectives

- Measurement of data
- Levels of measurement
- Measures of Central Tendency
- Measures of Dispersion

Review from Lecture 7

- Identified and defined levels of measurement and measures of central tendency
- Described situations in which different levels of measurement and measures of central tendency were useful and appropriate
- Calculated frequency, percentage, range, measures of central tendency
- Critiqued and justified their use for the exercise problems

Levels of Measurement

- The level of measurement used in collecting data determines the statistical techniques which can be used in analysis.
- Levels of measurement:
- Nominal
- Ordinal
- Interval/Ratio

Nominal Level Measurement

- Classifying items into groups
- No implied value of the groups as in a hierarchy or quantitative value
- In the dataset from our child protection study, nominal variables include
- Gender of the respondent and child
- male or female

- General Health Questionnaire elevated scores
- Subclinical score or clinically elevated score

- Gender of the respondent and child

Ordinal Level Measurement

- Classifying values of a variable in an order
- Quantitatively ordered items with an implied qualitative order
- An example is a Likert scale question with possible responses:
- 1. Never, 2. Sometimes, 3. Occasionally, 4. Often, 5. Always

- An example in our child protection study of an ordinal variable:
- Previous_Involvement - Have Social Services been involved with this child/ family previously?
- 1. Yes – Long standing involvement
- 2. Yes – Occasional involvement
- 3. No – No previous involvement

- Previous_Involvement - Have Social Services been involved with this child/ family previously?

Interval/Ratio Level Measurement

- Interval/Ratio level variables have equal units between variables and offer a range of possible values in that variable
- Age, time, and weight are examples
- Examples in our child protection study of interval/ratio variables are:
- GHQ-12 total score
- FES scores
- WAI scores
- Age of child
- Other total scores from standardized measures

Frequency Distributions

- A distribution provides a summary of how the data exists on a range of possible or actual scores.
- A frequency distribution combines all of the like values of a variable and graphical groups them.
- Which is to say how many times a value was recorded in a variable

- Charts such as a histogram provide a visual display of a frequency distribution where the frequencies of similar values in a variable are grouped

Frequency Distributions

- In our child protection study:
- Gender of the child
- Age of the child
- Previous involvement with social services

- Gender of the child is a nominal variable
- Previous Involvement with social services is an ordinal variable
- Age of the child is an interval/ratio variable
- Use the Analyze Menu in SPSS to find “Frequencies”

Select the variables from the list on the left and place in the “Variable(s)” list on the right.

- Click on “Statistics” and select “Mean”, “Median”, “Mode”, “Standard Deviation”, “Minimum”, and “Maximum”
- Click “Continue”

- Click on “Charts”, and select “Histograms” with “Show normal curve on histogram”
- Click “Continue”

Frequency Distributions “Show normal curve on histogram”

- Click “OK” for the Frequency Distributions and the descriptive statistics for these three variables.
- The results will appear in a new Output window

- In the first table, the descriptive statistics for the three variables are displayed.

Frequency Tables and Histograms variables are displayed.

- The next three slides give the Frequency Table and Histogram for each of the three variables we selected.
- When comparing the tables to the histograms, look to see how similar values are combined and visually displayed in the chart.
- Also, compare the distribution in the histogram to the curve of that the distribution would be if the variable were normally distributed.

Measures of Central Tendency variables are displayed.

- Mean – summing all the scores in a dataset and dividing by the total number of scores. Provides an average score.
- Median – The middle most score in a list of scores
- Mode – The most frequent or common score in a list of scores

Measures of Central Tendency variables are displayed.

- From these results, we can see that the mean of the children in the study was 7.69 years.
- Remember that it is inappropriate to take means (averages) of nominal or ordinal variables, thus the Means and Std. Deviation scores for Child Gender and Previous Involvement should be ignored.

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