Psychological Statistics Course Orientation

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Questions and Answers

Which of the following is NOT a basic concept in psychological statistics?

  • Variables and Measurement
  • Socialization and Conformity (correct)
  • Sampling Techniques
  • Data Collection

What is the primary purpose of descriptive statistics?

  • Predicting future outcomes based on past data
  • Drawing conclusions about a population based on a sample
  • Summarizing and organizing data to understand trends and patterns (correct)
  • Testing hypotheses to determine if there is a significant relationship between variables

Which of the following is an example of inferential statistics?

  • Calculating the average age of students in a classroom
  • Creating a bar chart to show the distribution of gender in a survey
  • Estimating the average income of all Americans based on a sample of 1,000 people (correct)
  • Finding the most popular color of cars in a parking lot

What are the three main requirements for this course?

<p>Journal entry/reflection, quizzes, and examination</p> Signup and view all the answers

The course grading system is based on 65% activities/performance/recitation, 15% quizzes, and 20% term exams.

<p>True (A)</p> Signup and view all the answers

What is the key reason why statistics are important in psychology?

<p>Statistics allow us to make sense of the information observed or studied in a reliable way.</p> Signup and view all the answers

Statistics is ______ the collection, organization, display, and interpretation of data in a scientific manner.

<p>not</p> Signup and view all the answers

What is the primary benefit of using appropriate techniques in collecting data?

<p>Increasing the accuracy and reliability of the data (B)</p> Signup and view all the answers

How does statistics add credibility to an argument?

<p>By providing evidence based on numbers, it makes the argument more convincing (B)</p> Signup and view all the answers

Which of the following is NOT a characteristic of descriptive statistics?

<p>Draws inferences about a population from a sample (A)</p> Signup and view all the answers

Match the examples with either Descriptive or Inferential Statistics:

<p>Calculating the average height of students in a class = Descriptive Statistics Estimating the average income of all Filipinos based on a sample of 1,000 people = Inferential Statistics Creating a bar graph to show the distribution of gender in a survey = Descriptive Statistics Analyzing the relationship between stress levels and sleep quality = Descriptive Statistics Predicting the likelihood of a student graduating based on their GPA = Inferential Statistics</p> Signup and view all the answers

Which example best illustrates the concept of inferential statistics?

<p>A researcher using a sample of 50 participants to study the effects of a new drug on anxiety (A)</p> Signup and view all the answers

Which of the following is NOT a key characteristic of inferential statistics?

<p>Creates a visual representation of data (A)</p> Signup and view all the answers

Descriptive statistics is used to draw inferences or conclusions about a population.

<p>False (B)</p> Signup and view all the answers

Inferential statistics can be used to make predictions about future outcomes.

<p>True (A)</p> Signup and view all the answers

Which of the following best defines the purpose of statistics in psychology?

<p>To objectively analyze and interpret data related to psychological phenomena (D)</p> Signup and view all the answers

The main requirement for this course are: journal entry/reflection, quizzes and examinations.

<p>True (A)</p> Signup and view all the answers

Which of the following are included as part of descriptive statistics?

<p>All of the above (D)</p> Signup and view all the answers

What is the purpose of inferential statistics?

<p>To draw inferences or conclusions about the population from a sample.</p> Signup and view all the answers

Flashcards

Course Orientation

An initial session introducing course structure, rules, and grading.

Psychological Statistics

Application of statistical methods to analyze psychological data.

Descriptive Statistics

Summarizes and presents data in a meaningful way.

Inferential Statistics

Uses sample data to infer conclusions about a larger population.

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Grading System

Structured criteria used to evaluate students' performance.

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Hypothesis Testing

Procedure for testing a hypothesis about a statistical parameter.

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Type I Error

Incorrectly rejecting a true null hypothesis.

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Type II Error

Failing to reject a false null hypothesis.

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Sampling Techniques

Methods used to select individuals from a population for a study.

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Data Collection Methods

Approaches to gather data for research analysis.

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Measures of Central Tendency

Statistics that represent the center of a dataset (mean, median, mode).

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Measures of Variability

Statistics that describe the spread or dispersion of data (range, variance, standard deviation).

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Normal Distribution

A bell-shaped distribution where most values cluster around the mean.

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Statistical Hypothesis

A specific statement about a population parameter to be tested.

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Parametric Statistical Techniques

Methods based on assumptions about data distributions, often related to the normal distribution.

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Non-Parametric Statistical Techniques

Methods that do not assume data follows any distribution.

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One-Way ANOVA

Statistical test used to compare means among three or more groups.

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Regression Analysis

Statistical technique for modeling the relationship between variables.

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Chi-Square Test

A non-parametric test to determine if there’s a significant association between categorical variables.

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Critical Values

Threshold values in hypothesis testing, determines decisions about null hypotheses.

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Reflection Paper

A written assignment reflecting personal insights and learning from course materials.

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Pi Graph

A circular statistical graphic divided into slices to illustrate numerical proportions.

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Learning Materials

Resources provided to aid students in understanding course content.

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Performance Recitation

An assessment component that contributes significantly to the final grade.

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Journal Entry Requirement

Reflective writing assignment required to process learning.

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Data Interpretation

The process of making sense of collected data to draw conclusions.

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Statistics for Research

Essential for credible data collection, interpretation, and conclusion in studies.

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Academic Performance Estimation

Using sample data to predict outcomes for a larger group.

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Research Credibility

The trustworthiness of conclusions drawn from data analysis.

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Variables

Characteristics or attributes that can take different values.

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Qualitative Variables

Variables that represent categories or labels.

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Quantitative Variables

Variables that can be measured numerically.

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Continuous Variables

Variables that can take any value within a range, including fractions.

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Discrete Variables

Variables that can only take whole number values and cannot be divided.

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Independent Variable

The variable that is manipulated or controlled in an experiment.

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Dependent Variable

The variable that is measured to see how it changes due to the independent variable.

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Nominal Scale

A scale that classifies data into distinct categories without any order.

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Ordinal Scale

A scale that classifies data into categories that can be ranked or ordered.

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Interval Scale

A scale that shows ordered categories with equal distances between them but no absolute zero.

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Ratio Scale

A scale that shows ordered categories with equal distances and includes an absolute zero.

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Magnitude in Scales

The property of a scale indicating 'more' or 'less' of a certain attribute.

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Equal Intervals in Scales

When the difference between two scale points has the same meaning throughout the scale.

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Absolute Zero

A point where the quantity being measured has no value.

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Classification of Variables by Value

Variables classified into qualitative or quantitative based on values assigned.

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Classification of Variables by Continuity

Variables classified as continuous or discrete based on their measurement capability.

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Functional Relationship of Variables

Classifying variables based on whether they predict or are affected by others.

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Example of Independent Variable

Stress is affected by the level of cortisol in the body.

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Example of Dependent Variable

Stress levels depend on cortisol levels.

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Research Definition

A systematic investigation to establish facts or principles.

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Scales of Measurement

Various methods to categorize, rank, or quantify data.

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Example of Nominal Variable

Gender or race, which are categories without order.

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Example of Ordinal Variable

Academic rankings that show order but not exact differences.

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Example of Interval Variable

Temperature, where differences are meaningful but zero isn’t an absence.

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Example of Ratio Variable

Weight, which has meaningful zero and equal intervals.

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Study Notes

Course Information

  • Course title: Psychological Statistics
  • Instructor: Dana Soliven
  • University: Wesleyan University - Philippines

Today's Agenda

  • Course Orientation
  • Basic Concepts in Psychological Statistics

Learning Objectives

  • Familiarization with the grading system, class requirements, and rules for the entire semester
  • Explanation of the basic concepts, meaning, nature, and importance of statistics in research
  • Differentiation between descriptive and inferential statistics

Course Description

  • Aligned with CMO 34 of 2017
  • Covers concepts and methods of descriptive and inferential statistics
  • Includes design, analysis, and interpretation of psychological studies

Course Outcomes

  • Students will be able to apply statistical principles
  • Ability to perform statistical tests
  • Ability to interpret statistical data

Course Outline (Weeks 1-18)

  • Week 1: Introduction to Psychological Statistics (course orientation, basic concepts)
  • Week 2: Basic Concepts in Statistics (variables and measurement)
  • Week 3: Sampling Techniques and Data Collection
  • Week 4: Descriptive Statistics (organizing, presenting, describing data, measures of center, variability, and position)
  • Week 5: Descriptive Statistics (shapes of distribution, measures of normality)
  • Week 6: PRELIM EXAM
  • Week 7: Descriptive Statistics (standard scores, normal distribution curve)
  • Week 8: Inferential Statistics (statistical hypothesis, directional and non-directional, Type I and Type II errors)
  • Week 9: Steps in Hypothesis Testing (basic terminologies, critical values, and critical regions)
  • Week 10: Steps in Hypothesis Testing (parametric statistical techniques, quantitative data)
  • Week 11: Steps in Hypothesis Testing (non-parametric statistical techniques, qualitative data)
  • Week 12: MIDTERM EXAM
  • Week 13: Mean Comparison (one-sample t-test, paired-sample t-test, independent sample t-test)
  • Week 14: Analysis of Variance (one-way ANOVA, factorial ANOVA, correlational analysis)
  • Week 15: Regression Analysis (simple regression, multiple regression)
  • Week 16: Non-Parametric Test Assumptions (Chi-Square Test, Phi-Coefficient Analysis, Cramer's V Test, Mann-Whitney U Test)
  • Week 17: Non-Parametric Test Assumptions (Kruskal-Wallis H Test, Wilcoxon Signed-Rank Test, Friedman Test)
  • Week 18: FINAL EXAM

Course Grading System

  • Activities/Performance/Recitation: 65%
  • Quizzes: 15%
  • Term Exams: 20%
  • Total: 100%

Class Requirements

  • Modules, activities, learning materials, and other course requirements will be provided ahead of time
  • Students should answer activities and quizzes honestly and not copy from others
  • Students with no internet access can take pictures of assignments and send them via email
  • Journal Entry/Reflection Papers required, reflecting on learned material for each topic
  • Modules will be uploaded every second Monday of the month
  • Activities, quizzes, and exams are due on the last Friday of the month

Importance of Statistics in Psychology

  • Statistics is used to understand observed information reliably
  • Helps make sense of data objectively and derive conclusions.

Importance of Statistics in Research

  • Use appropriate techniques to collect data effectively
  • Objectively interpreting information from the data
  • Drawing properly supported conclusions from the data
  • Enhancing the credibility of arguments

Levels of Analysis

  • Descriptive Statistics: Summarizes data (e.g., averages, distributions) to provide a general view
  • Inferential Statistics: Makes inferences about a population based on sample data, enabling generalizations.

Try and Learn

  • Examples to practice identifying levels of analysis (descriptive or inferential) in various scenarios:
    • Pie graph of sexual orientations: Descriptive
    • Negative relationship between stress and sleep quality: Inferential
    • Age range of students: Descriptive
    • Approval rating of a politician: Descriptive
    • Mean height of Filipino women: Descriptive

Reference

  • Howell, D. C. (2010). Statistical methods for psychology. Belmont, CA: Thomson Wadsworth.

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Psychological Statistics Course Outline - Wesleyan University
Psychological Statistics - Wesleyan University PDF

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