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Q:

Main parameters for Variation, spread or dispersion?

A:

Standard deviation: Measure of dispersion around the mean. In a

normal distribution, 68% of cases fall within one standard deviation

of the mean and 95% of cases fall within two standard deviations.

Variance: Measure of dispersion around the mean, equal to the sum

of squared deviations from the mean divided by one less than the

number of cases.

The variance is measured in units that are the square of those of

the variable itself.

Q:

Main parameters for Variation, spread or dispersion (2)

A:

- Range: The difference between the largest and smallest values of a

numeric variable, the maximum minus the minimum - Minimum: The smallest value of a numeric variable
- Maximum: The largest value of a numeric variable
- S. E. mean: A measure of how much the value of the mean may

vary from sample to sample taken from the same distribution. It can

be used to roughly compare the observed mean to a hypothesized

value (that is, you can conclude the two values are different if the

ratio of the difference to the standard error is less than -2 or greater

than +2).

Q:

Main parameters of Distribution

A:

Skewness and kurtosis are statistics that describe the shape and

symmetry of the distribution.**Skewness**

- A measure of the asymmetry of a distribution. The normal distribution is

symmetric and has a skewness value of 0. - A distribution with a significant positive skewness has a long right tail. A

distribution with a significant negative skewness has a long left tail. - As a guideline, a skewness value far away from 0 indicates a departure

from symmetry

**Kurtosis**

- A measure of the extent to which observations cluster around a central point
- For a normal distribution, the value of the kurtosis statistic is zero
- Positive kurtosis indicates that the observations cluster more and have

longer tails than those in the normal distribution - Negative kurtosis indicates that the observations cluster less and have

shorter tails

Q:

Explore procedure

A:

Produces summary statistics and graphical displays, either for all of

your cases or separately for groups of cases

To use only for interval/ratio scaled variables

There are many reasons for using the EXPLORE

procedure

- data screening
- outlier identification
- description
- assumption checking
- characterizing differences among subpopulations (groups of cases)

Q:

Explore statistics

M-estimators

A:

**M-estimators:** Robust alternatives to the sample mean and median for

estimating the location

- The estimators calculated differ in the weights they apply to cases.

Huber’s M-estimator, Andrews’ wave estimator, Hampel’s redescending M-estimator, and Tukey’s biweight estimator are displayed. - Outliers: Displays the five largest and five smallest values with case

labels - Percentiles: Displays the values for the 5th, 10th, 25th, 50th, 75th,

90th, and 95th percentiles

Q:

Main Characteristics for Boxplots

A:

- A boxplot consists of box and 2 tails
- The horizontal line inside the box tells the position of the median and its upper and lower boundaries are its upper and lower quartiles
- The tails run to the most extreme values
- The boxplot in sum shows structure of the data along with its skewness and spread

Q:

How to build a table in Crosstabs

A:

- Independent variables should be column variables
- Dependent variables should be in rows
- If not looking at independent and dependent variable relationships, use the variable that can logically be said to influence the other as your column variable
- Using this rule calculate column percentages to interpret your results

Example: If we were looking at the relationship between gender and voting, gender would be the column variable and voting would be the row variable. Logically gender can determine voting. Voting does not determine your gender.

Q:

Recoding variables

A:

- Recoding into different variables
- Recoding into the same variable
- It is recommended to use recoding into different variables and not using the into same variable option

Q:

Sort cases

A:

- This procedure sorts cases (rows) of the data file based on the values of one or more sorting variables
- You can sort cases in ascending or descending order
- If you select multiple sort variables, cases are sorted by each variable within categories of the preceding variable on the sort list

Q:

Sort variables

A:

- You can sort the variables in the active dataset based on the values of any of the variable attributes (e.g., variable name, data type, measurement level), including custom variable attributes
- Values can be sorted in ascending or descending order
- You can save the original (pre-sorted) variable order in a custom variable attribute
- Sorting by values of custom variable attributes is limited to custom variable attributes that are currently visible in Variable View

Q:

Weight Cases

A:

- Gives cases different weights (by simulated replication) for statistical analysis
- The values of the weighting variable should indicate the number of observations represented by single cases in your data file or gross up the sample
- Cases with zero, negative, or missing values for the weighting variable are excluded from analysis
- Fractional values are valid; they are used exactly where this is meaningful and most likely where cases are tabulated

Q:

The main parameters for Central tendency?

A:

- Mean: Measure of central tendency; The arithmetic average, the

sum divided by the number of cases - Median: The value above and below which half of the cases fall, the

50th percentile. The median is a measure of central tendency not

sensitive to outlying values - Mode: The most frequently occurring value. If several values share

the greatest frequency of occurrence, each of them is a mode. The

Frequencies procedure reports only the smallest of such multiple

modes. - Sum: The sum or total of the values, across all cases with non

missing values.

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