AUSB’s QNT 190: Statistics for Behavioral Sciences course.
Successfully complete the statistics requirement in 10 weeks!
Thank you for your interest in the QNT 190: Statistics for Behavioral Sciences course. If you have been notified to proceed with registration for Spring quarter by the Office of Admission, your registration will begin processing. If you have not filled out the registration form, you will find the form on the tab below. If you are on the waiting list, we will let you know if space becomes available.
In order to confirm your space in the course, you must provide tuition payment as advised by the Registrar’s email. If payment is not received by the date specified, you will be automatically dropped from registration. A separate e-mail will be sent to you providing a link to your MyAntioch account where you will be able to provide payment using a debit/credit card.
For more information, please contact: Molika Oum at email@example.com or 805.962.8179 x 5172.
- 10 weeks of instruction
- Applied Learning
- Small Class Size
- Individualized Attention From Faculty
- Convenient Downtown Santa Barbara Location
About the Course
A course in statistics that is useful for all majors in the behavioral and life sciences.
- QNT 190: Statistics for Behavioral Sciences
- 4 Semester units (6 Quarter units)
- Next class offering is Spring Quarter: April 6 to June 20, 2015
- Grading policy: narrative evaluation with optional letter grade equivalent
- Cost $349
- Prerequisite: Intermediate Algebra
For more information, please contact: Molika Oum, Admission Advisor at firstname.lastname@example.org or 805.962.8179 ext. 5172
Course: QNT 190 Statistics For Behavioral Sciences
Quarter Units: 6
Catalog Course Description: A general education course in statistics that is useful for all majors in the behavioral and life sciences. Students are introduced to principles and procedures of measurement, data base management, data analysis, probability, sampling theory and statistical significance. The course covers Descriptive Statistics: measurement scales, frequency distributions, measures of central tendency, measures of variability, measures of linear relationships, standard scores; and Inferential Statistics: logic of hypothesis testing, z-tests, independent-samples and dependent-samples t-tests, one way analysis of variance, correlation procedures, and non-parametric statistics. In addition, a conceptual introduction of two-way analysis of variance is covered in this course.
Prerequisite: Intermediate algebra
BA Program Core Purposes:
Critical and Creative Thinking are the necessary thought processes of an effective thinker who uses divergent and convergent thought patterns to arrive at an appropriate conclusion in a given situation. This objective cultivates students’ skills in reaching conclusions founded on their examination of a variety of authorities within and across various disciplines and with engaging in innovation and risk taking.
Effective Communication is the co-creation of meaning focusing on how people use content to generate understanding within and across various contexts, cultures, channels, and media. It always includes a communicator, an audience, a subject, and a situation. Effective communicators create a purposeful message designed to increase knowledge, to foster understanding, or to promote change in the listeners’ attitudes, values, beliefs, or behaviors.
Global and Intercultural Awareness is a collection of skills that promote effective interaction in a variety of cultural contexts. Global awareness is an understanding of the interconnections between nations, socio-cultural groups, individuals, and the elements that influence them. Intercultural awareness is knowledge of and sensitivity to diversity in all its forms, and a variety of factors that shape culture including worldview, communication, cultural rules, and personal biases.
Holistic Personal Development is the multifaceted process of becoming self-actualized. It involves all aspects of the self–including the physical, mental, emotional and spiritual–and includes taking personal responsibility for one’s own learning and development through a process of assessment, reflection, and action.
Competence for Professional Pursuits is an understanding and a disposition that a student builds across the curriculum and co-curriculum, from making simple connections among ideas and experiences to synthesizing and transferring learning to new, complex situations beyond the classroom into a professional field. Students explore the central knowledge, skills, and professional conduct of their chosen field or profession to prepare for engagement in meaningful and socially responsible work.
Praxis for Social Justice combines learning and doing for the purpose of encouraging critical consciousness, ethical reasoning, and socially responsible behavior through civic engagement. This objective advances critical awareness of the social, economic, political, and environmental justice issues that demarcate the terrain of power, oppression, and resistance. Praxis for social justice includes developing the commitment, skills, and knowledge necessary to contribute to the on-going work for justice through activism and engagement that embraces local and global communities.
- Identify and distinguish the four kinds of measurement scales used in assigning numbers/values to variables.
- Construct frequency distributions: histograms, box-plots, stem-and-leaf diagrams, bar graphs, raw frequency counts to data sets.
- Calculate measures of central tendency: mean, median, mode.
- Calculate measures of variation: range, percentile rank, variance, standard deviation.
- Compute derived scores, including z-scores.
- Calculate measures of association: correlation coefficient.
- Calculate inferential statistics: independent-samples and dependent-samples t-tests and one-way ANOVA; demonstrate a conceptual understanding of two-way ANOVA.
- Compare and contrast the appropriate use of descriptive vs. inferential statistics.
- Explain the logic in the distinction between the “null” vs. “working” hypothesis.
- Explain “statistical significance” in the context of probability theory.
- Explain the use, strengths and weaknesses of sample statistics to estimate population parameters. Estimate parameters for various statistics, including point-estimate and confidence intervals.
- Demonstrate how to set up a spread sheet in the coding, entry and analysis of data.
- Apply SPSS (Statistical Package for the Social Sciences) in the statistical analysis of data sets provided.
- Interpret and explain the meaning and importance of statistics.
- Summarize and report in written format statistical findings consistent with the publication guidelines of the American Psychological Association.
- Identify and critique statistical shortcomings in research articles and/or mass media reports of pseudo-scientific findings.
- Identify the appropriate use of various statistics in answering research questions.
Student Learning Outcomes:
- Recognize – Recognize and apply core concepts, uses, techniques and the logic of descriptive and inferential statistics.
- Identify – Identify, perform, and interpret the appropriate inferential statistical tests for various research situations.
- Utilize – Utilize SPSS to construct databases, produce descriptive statistics, and to conduct and interpret inferential hypothesis tests.
Course Content and Scope:
- Theory of measurement of behavioral and psychological properties
- Scales of measurement, limits and assumptions of calculable statistics
- Examples of statistical applications in contemporary society
- Benefits of a statistics course for consumers of research information
- Organizing, Summarizing and Graphical Display of Data
- Frequency distribution, histograms, stem-and-leaf plots, box plots, normal distribution
- Variation and individual differences
- Descriptive Statistics (Measures of central tendency: mean, median, mode); Measures of variation: range, percentile, variance, standard deviation
- The Normal Distribution Model
- Normal and non-normal distribution of characteristics, conditions
- The normal curve
- Transformed raw scores into z Scores and percentiles
- Central Tendency and Central Limit Theorem
- Probability Theory and Hypothesis Testing
- The logic of hypothesis testing; the null and alternative hypothesis
- Population parameters vs. sample statistics
- Sampling theory, practice and confidence intervals
- Hypothesis testing in estimating population parameters
- Variables and Non-Causal Relationships
- Linear and nonlinear relationships
- Graphing relationships: scatter plots
- Summarizing relationships: correlation coefficients – Pearson, Product
- Moment and Spearman rank order
- Correlations: strength, direction, probability, significance, interpretation
- Two Variables and Simple Causal Relationships
- Cause and effect demonstrations in experiments
- Independent and dependent variables
- Hypothesis testing of mean differences between groups (test for two groups: t-tests; non-parametric test for two groups: chi-square)
- Distribution of t and levels of significance
- Conceptualizing Complex Causal Relationships: Multiple Variables
- Three or more levels in the Independent Variable
- One Way Analysis of Variance: ANOVA
- (Conceptual) Two or more independent variables with multiple levels in each
- (Conceptual) Two Way or Factorial Analysis of Variance
- (Conceptual) Correlational Modeling; uses of multiple r. and regression analysis
- Statistical Package for the Social Sciences (SPSS)
- Writing up and reporting statistics
- Reading Research Reports and Mass Media Reports
In addition, various computer lab assignments will be assigned, including:
- The Basics of SPSS: Spreadsheet, database management, statistical tools; starting SPSS, loading a file, data definition and data entry, creating and accessing a data set; SPSS output, printing output, saving output, exiting SPSS.
- Graphical Displays: Organization and presentation of Descriptive Data Frequency distributions and graphs; descriptive statistics (mean, median and mode); measures of variability (range, percentile, variance and standard deviation; normal and non-normal distributions (skewness, kurtosis); transformed scores (z-scores and other derived scores).
- Testing Research: Hypotheses using t-test procedure (independent-samples t-test; dependent-samples t-test).
- Describing the Linear Relationship Between Two or More Variables: Scatter plots; linear and nonlinear relationships; Pearson Product-Moment Correlation; Spearman rank order correlation; problems of interpreting correlations: Bi-directionality and the third variable problem; crosstabs and Chi-Square tests.
- Testing Research Hypotheses with Multiple Levels in the Independent Variable (one-way ANOVA procedure for independent samples; performing post-hoc tests; interpreting the output); analysis of variance with related (repeated measures) samples; conceptual presentation of two-way or factorial analysis of variance (main effects vs. interaction effects; graphing the results).
Methods of Instruction:
Lecture presentations using computer-aided instruction, classroom demonstrations and activities, and individual and team assignments in a computer lab with SPSS.
- Reading assigned chapters each week in the basic text and supplemental reading materials.
- Weekly graded homework and computer lab assignments.
Methods of Evaluation:
- Weekly graded homework and lab assignments.
- Periodic performance-based quizzes on mastery of computation, use and interpretation of statistics.
- Objective mid-term exam covering conceptual mastery, computation of various statistics, and practical application of statistics.
- Final comprehensive exam based on conceptual mastery, computation, and practical application of statistics covered in the course.
Appropriate Texts and Supplies:
Please refer to instructor provided syllabus.
For more information, please contact: Molika Oum at email@example.com or 805.962.8179 x 5172.