BNAD 276 Study Guide - Fall 2018, Comprehensive Midterm Notes - Standard Deviation, Square Root, Confidence Interval
12 Oct 2018
School
Department
Course
Professor
BNAD 276
MIDTERM EXAM
STUDY GUIDE
Fall 2018
BNAD 276 NOTE 1
(Lecture notes)
Demonstrations
• Our prior knowledge will influence our memories - inserting what was never there.
• Our interests will influence what we see - making invisible what is right in front of us.
• Our recent experiences will influence what we see - making one interpretation much
more likely.
• Our current environment will influence what we see - making images meaningful.
• How we interpret social interactions and business problems are similarly vulnerable to
bias.
Why study stats?
• Biases can impede or improve our decision making.
• Our vulnerability to biases and illusions in social settings and in even the most basic daily
experiences (We are vulnerable to biases).
• Statistics and research methods allow us to try to “take into account” our natural
tendencies for specific kinds of biases.
An operational definition
• Definition of a construct or characteristic in terms of how it is measured specifically for a
particular context.
How do we measure mental processes?
• “Constructs” represent relatively abstract concepts. For example, memory, happiness,
satisfaction, humor.
• “Operational definitions” define how constructs are measured.
• “Measurements” assess observable characteristics or behaviors resulting in a reduction of
uncertainty.
• Data analyses try to describe, predict and explain measurements of behaviors (or
characteristics).
Evaluating operational definitions: Validity & Reliability
• Validity: the extent to which a test measures what it intends to measure.
• Reliability: the extent to which a test yields consistent results.
• Validity is a measure of the meaning of the scores.
• Reliability is a measure of consistency (or precision).
Independent & Dependent variable
• Dependent variable: the variable being measured by investigator. The data that is being
recorded. What are you measuring?
• Independent variable: the factor that is being manipulated (or compared) by the
experimenter. How do your groups differ?
(Textbook notes)
Descriptive statistics & Inferential statistics
• Descriptive statistics: refers to the summary of important aspects of a data set, such as
collecting data, organizing the data.
• Inferential statistics: refers to drawing conclusions about a large set of data - called a
population - based on a small set of sample data.
The need for sampling
• Obtaining information on the entire population is expensive (impractical).
• It is impossible to examine every member of the population.
Types of data
• Cross-sectioal data: data collected by recording a characteristic of many subjects at the
same point in time.
• Time series data: refers to data collected by recording a characteristic of a subject over
several time periods.
Types of variable
• Qualitative variable: we use labels or names to identify the distinguishing characteristic
Document Summary
An operational definition: definition of a construct or characteristic in terms of how it is measured specifically for a particular context. How do we measure mental processes: constructs represent relatively abstract concepts. For example, memory, happiness, satisfaction, humor: operational definitions define how constructs are measured, measurements assess observable characteristics or behaviors resulting in a reduction of uncertainty, data analyses try to describe, predict and explain measurements of behaviors (or characteristics). Independent & dependent variable: dependent variable: the variable being measured by investigator. Independent variable: the factor that is being manipulated (or compared) by the experimenter. Descriptive statistics & inferential statistics: descriptive statistics: refers to the summary of important aspects of a data set, such as collecting data, organizing the data. Inferential statistics: refers to drawing conclusions about a large set of data - called a population - based on a small set of sample data. The need for sampling: obtaining information on the entire population is expensive (impractical).
