Data Analysis SPSS Flashcards

Flashcards covering key vocabulary related to data analysis using SPSS.

SPSS

Statistical Package for Social Sciences developed by IBM Corporation.

.sav

data and variables.

.sps

commands and comments.

.spv

contains results.

EDIT

includes the typical cut, copy, and paste commands, and allows you to specify various options for displaying data and output

VIEW

allows you to select which toolbars you want to show, select font size, add or remove the gridlines that separate each piece of data, and to select whether or not display your raw data or the data labels

DATA

allows you to select several options ranging from displaying data that is sorted by specific variable to selecting certain cases for subsequent analyses.

ANALYZE

Includes all of the commands to carry out statistical analyses and to calculate descriptive statistics.

GRAPH

Includes the commands to create various types of graphs including box plots, histograms, line graphs, pie graph, chart and bar charts.

UTILITIES

• allows you to list file information which is a list of all variables, their labels, values, 

locations in the data file, and type.

EXTENSIONS

• Custom components that extend capabilities of IBM SPSS statistics. 

• Can be created by any users by sharing the associated extension bundle

WINDOW

Can be used to select which window you want to view (i.e., Data Editor, Output Viewer, or Syntax)

Data View

The area in SPSS where you input your data.

Variable View

Contains information about the data set, including name, type, width, decimals, label, values, missing, columns, align, measure, and role.

Variable Name

Must start with a letter, be less than 64 characters, and be unique; no special characters or spaces allowed.

Nominal Variable

Categorical data with no order or ranking (e.g., gender, religion).

Ordinal Variable

Ordered categories with unequal gaps (e.g., satisfaction rankings).

Interval Variable

Ordered categories with equal intervals but no true zero (e.g., temperature in Celsius).

Ratio Variable

Ordered categories with equal intervals and a true zero (e.g., age, weight).

Syntax Editor

Text editor in SPSS used to create files and run analyses using syntax code.

Output Viewer

Window in SPSS where graphs, tables, and analyses results are displayed.

Frequency

The number of times a certain event has taken place; a count of occurrences.

Hypothesis

A specific, testable prediction about the relationship between two or more variables.

Statistical Hypothesis

An assertion or conjecture concerning one or more populations.

Null Hypothesis (H0)

A statement about a population parameter assumed to be true; expresses no significant difference or relationship.

Alternative Hypothesis (Ha or H1)

Statement that contradicts the null hypothesis, proposing a difference or relationship.

Confidence Level

Measure of the reliability of a result; e.g., a 95% confidence level means a 0.95 probability the result is reliable.

• 0.05 (commonly used)

Level of Significance (alpha)

Criterion for judging a decision regarding the null hypothesis, based on the probability of obtaining a statistic if the null hypothesis were true.

Critical Value Approach

A method to decide whether to reject the null hypothesis by comparing the test statistic to a critical value.

P-Value Approach

An alternative way of conducting tests of significance using the probability of getting a sample statistic if the null hypothesis is true.

I. if p ≤ a : reject null hypothesis

ii. If p > a : accept null hypothesis

One-Tailed Test

A statistical test where the critical region is on one side of the distribution mean.

Two-Tailed Test

A statistical test where the critical region is two-sided, testing if a sample is greater or less than a range of values.

Type 1 Error

Rejecting the null hypothesis when it is actually true (false positive).

Type 2 Error

Failing to reject the null hypothesis when it is actually false (false negative).

1. SETTING HYPOTHESIS

2. CRITERION

3. COMPUTATION (SPSS)

4. DECISION

5. CONCLUSION

FIVE STEPS IN HYPOTHESIS TESTING

Parametric Test

Statistical test that assumes normal distribution of data; typically uses interval or ratio data.

T-Test

Compares the means of two groups to determine if there's a significant difference.

• Comparing exam scores of male vs. female students

Independent T-Test

Compares the means of two different, unrelated groups.

• Comparing test scores of Section A vs. Section B

Paired T-Test

Compares the same group’s performance before and after a treatment or over time.

ANOVA (Analysis of Variance)

Compares the means of three or more groups.

• Comparing test scores of students from 3 different sections

1. Go to Analyze → Compare Means → One-Way ANOVA...

2. Move your dependent variable (e.g., Exam Score) into the Dependent List box.

3. Move your independent/grouping variable (e.g., Department) into the Factor box.

Click Options → Check Descriptive and Homogeneity of variance test → Click Continue.

Pearson's r

Measures the correlation between two quantitative variables.

•  Relationship between hours studied and exam score

1. Analyze → Correlate → Bivariate

2. A dialog box will appear. Select the two numeric variables you want to correlate and move them to the Variables box using the arrow button.

Under Correlation Coefficients, make sure: Pearson is checked.

Simple Linear Regression

Predicts the value of a dependent variable based on one independent variable.

1. Analyze → Regression → Linear

2. In the dialog box:

• Move your dependent variable (what you want to predict) into the "Dependent" box.

• Move your independent variable (predictor) into the "Independent(s)" box.

3. Click Statistics...

• Check Estimates and Model Fit (already checked by default).

4. Click Plots...

• You can plot ZPRED (x-axis) and ZRESID (y-axis) to check assumptions

Non-Parametric Test

Statistical test that does not assume a normal distribution; typically uses ordinal or nominal data.

Chi-Square Test

Used for frequencies and categorical data to test relationships between variables.

TYPES OF CHI-SQUARE TESTS

1. GOODNESS-OF-FIT

2. TEST OF INDEPENDENCE

Chi-Square Goodness-of-Fit Test

Tests whether observed frequencies match expected frequencies for a single categorical variable.

• 1 categorical variable vs. expected distribution

• example:Are students equally likely to choose STEM, ABM, and HUMSS tracks?

Chi-Square Test of Independence

Tests whether two categorical variables are independent or associated.

• 2 categorical variables

• EXAMPLE: Is there a relationship between gender (male/female) and choice of ice cream flavor (vanilla/chocolate/strawberry)?