Summary of the material for the final SPSS exam (2022) for Statistics II: Applied Quantitative
Analysis. INCLUDES a cheat sheet of the course’s general information, SPSS commands and
functions (Total: 35 pages).
1
Statistics II: Applied Quantitative Analysis SPSS Exam Cheat Sheet
Table of Contents
General 2
Bivariate Linear (OLS) Regression 5
Multiple (Multivariate) Linear (OLS) Regression 6
Hierarchical Regression 8
OLS Model Assumptions 9
Moderation/Interaction Terms 14
Outliers/Influential Cases 15
Logistic (MLE) Regression 17
Logistic Model Assumptions 21
Other Logistic Regressions (NOT on SPSS - Just for Reference) 23
SPSS Codes/Methods, Interpretations and Calculations by Hand 24
, 2
General
Variables in Models:
1. Dependent Variable (DV): The variable we want to predict/explain/understand (i.e.
outcome variable, Y).
2. Independent Variable (DV): The variable we are using to predict/explain the outcome (i.e.
predictor variable, X).
Statistical Models:
1. Ordinary Least Squares (OLS): Models continuous (scale) DVs, with a variety of different
IVs.
2. Logit Models: Models binary (two) outcome variables.
3. Multinomial and Ordered/Ordinal Logit Models: Models categorical (multiple categories)
and ordinal dependent variables.
Interpretations:
1. Do NOT interpret the slope coefficient as saying something about the constant.
➔ The constant gives the mean value of the DV when X=0.
➔ The slope for an IV tells us how Y changes on average for each one-unit increase in
X.
2. Include statistics + p-value + significance.
Levels of Measurement:
● Categorical: Contain a finite number of categories or distinct groups.
1. Nominal:
■ 2+ exclusive categories, with NO natural order.
■ NO arithmetic operations are possible (subtraction or logical operations).
■ Can only talk about these categories in frequency (mode).
■ E.g. political party affiliation.
2. Ordinal:
■ Clear ordering of the values (e.g. small or larger).
■ Spacing between the values is NOT the same across levels.
■ Comparison is possible, but only relative.
■ E.g. level of agreement.
■ IMPORTANT: If there is an ordinal variable choose between treating it as:
● Categorical (if told: “treat the variable as ‘ordinal’”):
○ Pick a category to serve as the reference/baseline and enter
dummy variables for the other categories.
○ Advantage = does NOT require any supplemental assumptions
to interpret the coefficients and is therefore easy to justify
(difference in means test).
○ Disadvantage = information about the variable is discarded
(i.e. it’s ordering), which can be more difficult to show and
discuss.
● Continuous (if told: “treat the variable as ‘interval/ratio’”):
○ Same interpretation as the continuous predictor.
○ Advantages = retains the ordering information, easy to
interpret and in nearly all cases does NOT affect conclusions
because the relationships are approximately linear enough.
○ Disadvantages = assumption can fail (inaccurate assessment),
, 3
and the assumption that each increment in X is equally spaced
is forced to be made, which may be more controversial.
● Continuous: Numeric variables that have an infinite number of values between any two
values (i.e. the difference = meaningful).
➔ Variables can be continuous, OR discrete:
◆ “Continuous”: Measured to any level of precision (e.g. height can be
measured to any value).
◆ “Discrete”: Only takes certain, countable values, usually whole numbers
(e.g. points in an exam).
➔ Interval/ratio variables are categorised together in SPSS.
3. Interval:
■ 0 = arbitrary or meaningless.
■ E.g. a temperature of 0.0°C to °F does not mean ‘no heat’.
4. Ratio:
■ Like interval variables, but have a meaningful 0.
■ E.g. 0 Kelvin means no heat.
Data Cleaning/Descriptive Statistics:
1. Investigate variables.
2. For completeness always run a frequency table before.
➔ Creating a frequency table = Analyse → Descriptive Statistics → Frequencies
3. Always inspect how missing variables are coded.
4. Recode variables into dummies (do NOT forget SYSMIS and add value labels).
➔ (Transform → Recode into Different Variables), always ADD variable labels (e.g.
0=bicameral, 1=unicameral).
5. Look at SPSS’ output.
Minimum/Maximum Values (of the Sample):
● Finding = data view, right-click on the variable name and sort ascending/descending.
● When asked to determine the magnitude of a relationship → minimum and maximum
and compare.
● Predicting:
1. Write down the formula.
2. Determine the variable observed minimum and maximum.
3. Determine the mode/mean for other variables in the formula that remain constant.
4. Fill all values into the model.
Binary/Dichotomous/“Dummy”: Variables that can take on one of two variables (typically 0 or 1),
talks about a difference in means test.
➔ When analysing/recoding different types of variables:
◆ Categorical = use mode (when running dummy variables, exclude one category
from the analyses ⇒ becomes included in the constant).
● Constant represents the number if all X variables = 0 (i.e. excluded
category).
◆ Continuous = use means.
, 4
Creating Dummy Variables:
1. Create a series of binary or dummy variables for each category (1 = member of that
category, 0 = member of one of the other categories).
2. When choosing a reference category, considerations can be:
● Theoretical; choose the category most expected to deviate from the others.
● Practical; choose the category with a large number of observations.
➔ Do NOT use a category with few observations, as resulting estimates will
be imprecise.
3. Include all but one (the reference/baseline category) of these dummy variables in the
model, against which the others will be compared.
➔ Constant Term: The expected value of the DV when the IVs = 0. In a bivariate
model, the constant = the average for cases in the reference category (e.g. Labour).
➔ Coefficient for Categories: The difference in means between category and
reference group holding the remaining variables constant.
Statistical Significance:
● Statistical significance (precision) ≠ Substantive importance/significance (size).
➔ More data = less uncertainty (generally).
➔ A “null” effect can be practically/socially important.
● Null hypothesis = NO relationship; an increase in X does NOT = increase in Y (just a
straight line).
If you see: What it means: Write p-value as: Interpretation:
.000 p = 0.000… p < 0.001 Reject H0.
.001 p = 0.001 p <0.01 or p <0.05, depends on the threshold value. Reject H0.
< 0.001 0.0005 < p <0.001 p <0.001 Reject H0.
.061 p = 0.061 p = 0.061 or p <0.01 or p <0.05, depends on the Do NOT reject H0.
threshold value.
Missing Values:
1. System Missing (SYSMIS = SYSMIS): Data is missing in
the values boxes; a blank cell. Nothing needs to be done.
2. User-Defined Missing Variable (MISSING = SYSMIS): A
specific numeric value for missing data. Usually, holding
a negative/extreme value (look at the Values column in
SPSS or create a frequency table).
➔ CAUTION: Ensure variables are coded as a specific number (value label
column).
➔ Write if numbers were added to the Missing Column.
Analysis. INCLUDES a cheat sheet of the course’s general information, SPSS commands and
functions (Total: 35 pages).
1
Statistics II: Applied Quantitative Analysis SPSS Exam Cheat Sheet
Table of Contents
General 2
Bivariate Linear (OLS) Regression 5
Multiple (Multivariate) Linear (OLS) Regression 6
Hierarchical Regression 8
OLS Model Assumptions 9
Moderation/Interaction Terms 14
Outliers/Influential Cases 15
Logistic (MLE) Regression 17
Logistic Model Assumptions 21
Other Logistic Regressions (NOT on SPSS - Just for Reference) 23
SPSS Codes/Methods, Interpretations and Calculations by Hand 24
, 2
General
Variables in Models:
1. Dependent Variable (DV): The variable we want to predict/explain/understand (i.e.
outcome variable, Y).
2. Independent Variable (DV): The variable we are using to predict/explain the outcome (i.e.
predictor variable, X).
Statistical Models:
1. Ordinary Least Squares (OLS): Models continuous (scale) DVs, with a variety of different
IVs.
2. Logit Models: Models binary (two) outcome variables.
3. Multinomial and Ordered/Ordinal Logit Models: Models categorical (multiple categories)
and ordinal dependent variables.
Interpretations:
1. Do NOT interpret the slope coefficient as saying something about the constant.
➔ The constant gives the mean value of the DV when X=0.
➔ The slope for an IV tells us how Y changes on average for each one-unit increase in
X.
2. Include statistics + p-value + significance.
Levels of Measurement:
● Categorical: Contain a finite number of categories or distinct groups.
1. Nominal:
■ 2+ exclusive categories, with NO natural order.
■ NO arithmetic operations are possible (subtraction or logical operations).
■ Can only talk about these categories in frequency (mode).
■ E.g. political party affiliation.
2. Ordinal:
■ Clear ordering of the values (e.g. small or larger).
■ Spacing between the values is NOT the same across levels.
■ Comparison is possible, but only relative.
■ E.g. level of agreement.
■ IMPORTANT: If there is an ordinal variable choose between treating it as:
● Categorical (if told: “treat the variable as ‘ordinal’”):
○ Pick a category to serve as the reference/baseline and enter
dummy variables for the other categories.
○ Advantage = does NOT require any supplemental assumptions
to interpret the coefficients and is therefore easy to justify
(difference in means test).
○ Disadvantage = information about the variable is discarded
(i.e. it’s ordering), which can be more difficult to show and
discuss.
● Continuous (if told: “treat the variable as ‘interval/ratio’”):
○ Same interpretation as the continuous predictor.
○ Advantages = retains the ordering information, easy to
interpret and in nearly all cases does NOT affect conclusions
because the relationships are approximately linear enough.
○ Disadvantages = assumption can fail (inaccurate assessment),
, 3
and the assumption that each increment in X is equally spaced
is forced to be made, which may be more controversial.
● Continuous: Numeric variables that have an infinite number of values between any two
values (i.e. the difference = meaningful).
➔ Variables can be continuous, OR discrete:
◆ “Continuous”: Measured to any level of precision (e.g. height can be
measured to any value).
◆ “Discrete”: Only takes certain, countable values, usually whole numbers
(e.g. points in an exam).
➔ Interval/ratio variables are categorised together in SPSS.
3. Interval:
■ 0 = arbitrary or meaningless.
■ E.g. a temperature of 0.0°C to °F does not mean ‘no heat’.
4. Ratio:
■ Like interval variables, but have a meaningful 0.
■ E.g. 0 Kelvin means no heat.
Data Cleaning/Descriptive Statistics:
1. Investigate variables.
2. For completeness always run a frequency table before.
➔ Creating a frequency table = Analyse → Descriptive Statistics → Frequencies
3. Always inspect how missing variables are coded.
4. Recode variables into dummies (do NOT forget SYSMIS and add value labels).
➔ (Transform → Recode into Different Variables), always ADD variable labels (e.g.
0=bicameral, 1=unicameral).
5. Look at SPSS’ output.
Minimum/Maximum Values (of the Sample):
● Finding = data view, right-click on the variable name and sort ascending/descending.
● When asked to determine the magnitude of a relationship → minimum and maximum
and compare.
● Predicting:
1. Write down the formula.
2. Determine the variable observed minimum and maximum.
3. Determine the mode/mean for other variables in the formula that remain constant.
4. Fill all values into the model.
Binary/Dichotomous/“Dummy”: Variables that can take on one of two variables (typically 0 or 1),
talks about a difference in means test.
➔ When analysing/recoding different types of variables:
◆ Categorical = use mode (when running dummy variables, exclude one category
from the analyses ⇒ becomes included in the constant).
● Constant represents the number if all X variables = 0 (i.e. excluded
category).
◆ Continuous = use means.
, 4
Creating Dummy Variables:
1. Create a series of binary or dummy variables for each category (1 = member of that
category, 0 = member of one of the other categories).
2. When choosing a reference category, considerations can be:
● Theoretical; choose the category most expected to deviate from the others.
● Practical; choose the category with a large number of observations.
➔ Do NOT use a category with few observations, as resulting estimates will
be imprecise.
3. Include all but one (the reference/baseline category) of these dummy variables in the
model, against which the others will be compared.
➔ Constant Term: The expected value of the DV when the IVs = 0. In a bivariate
model, the constant = the average for cases in the reference category (e.g. Labour).
➔ Coefficient for Categories: The difference in means between category and
reference group holding the remaining variables constant.
Statistical Significance:
● Statistical significance (precision) ≠ Substantive importance/significance (size).
➔ More data = less uncertainty (generally).
➔ A “null” effect can be practically/socially important.
● Null hypothesis = NO relationship; an increase in X does NOT = increase in Y (just a
straight line).
If you see: What it means: Write p-value as: Interpretation:
.000 p = 0.000… p < 0.001 Reject H0.
.001 p = 0.001 p <0.01 or p <0.05, depends on the threshold value. Reject H0.
< 0.001 0.0005 < p <0.001 p <0.001 Reject H0.
.061 p = 0.061 p = 0.061 or p <0.01 or p <0.05, depends on the Do NOT reject H0.
threshold value.
Missing Values:
1. System Missing (SYSMIS = SYSMIS): Data is missing in
the values boxes; a blank cell. Nothing needs to be done.
2. User-Defined Missing Variable (MISSING = SYSMIS): A
specific numeric value for missing data. Usually, holding
a negative/extreme value (look at the Values column in
SPSS or create a frequency table).
➔ CAUTION: Ensure variables are coded as a specific number (value label
column).
➔ Write if numbers were added to the Missing Column.
