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Welcome Back. Learning Objectives: Identify variables in research Describe Relationships btwn Explain why samples used to describe population Explain random sampling and representative samples Distinguish btwn Descriptive & Inferential Stats

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Welcome Back

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  1. Welcome Back Learning Objectives: Identify variables in research Describe Relationships btwn Explain why samples used to describe population Explain random sampling and representative samples Distinguish btwn Descriptive & Inferential Stats Distinguish btwn experimental & correlational study Identify & distinguish scales of measurement

  2. Research Process • Interest in something • “Playing video games leads to violence” • Goal: Discover LAWS OF NATURE • “Somethings” are called Variables • Variables • Independent variables- the “thing” that influences the behavior • Dependent variables- the outcome or result of the independent variable

  3. Independent Vegetables Vitamins Drugs Smiling Examples? Dependent Cancer Immune System Alzheimers Helping or Altruism Examples? Examples of Variables

  4. Relationship between Variables • Relationship: occurs when a chg in one var. is accompanied by a consistent chg. in another var. • Strength: Degree of chg in X is associated with chg in Y • Types of relationships: • Increase, increase • Increase, decrease • Decrease, decrease • Zero

  5. Population-all members of group Parameters-numbers that describe Sample-subset of pop designed to be representative Statistic-numbers that describe Populations & Samples Every student at BC Students in a History class at BC

  6. http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/ • Why? • Cheaper • Practical • Representative

  7. What is “done” to samples? • Describe • Use descriptive statistics to organize & summarize characteristics of data • Example: The average test score was 87% • Infer • Use inferential statistics to decide whether sample data represents a particular relationship in population • Example: Reading the textbook significantly increased test scores.

  8. Characteristics of a Study • Question about characteristic of sample or pop is asked • Design study to answer question • Who -How many -When -What • Conduct study • Correlational • Experimental

  9. Correlational Study • Goal: To determine if relationship btwn two or more var is present • No variables are manipulated or made to occur, they are simply measured • As such cannot infer causality • Can’t say X causes Y • Only X and Y are related

  10. Ice Cream Sales Temperature Example • Researcher’s Question: -Is there a relationship btwn ice cream sales and crime rate? • Design of study: measure sales & crime rates • Yes, a positive, strong relationship is present • Doesn’t mean ice cream causes crime

  11. Experimental Study • Goal: To determine if relationship (causality) exists btwn variables • Variable (indep) are manipulated or changed to see chg in beh (dep var) • Can *infer causality • X causes chg in Y • *Caution: causal statement based on probability • Never says PROVES • Other variables could be responsible for change in dependent variable

  12. Number of Crimes Committed 0 1 2 Scoops of Ice Cream Example • Researcher’s Question: -Does ice cream cause or have an effect on criminal behavior • Design of study: P’s in diff conditions of ice cream (levels of indep var) and measure criminal beh (dep var) • Yes, a probable causal relationship is present • P’s that ate 1 or 2 scoops of ice cream committed more crimes • PROBABILITY

  13. Type of Data or Characteristics of Scores • Type of data or dependent var you’re interested in will determine what statistic you can use • Numbers you record have diff mathematical characteristics • Characteristics of numbers • Levels of measurement • Continuous or discrete

  14. Scales of Measurement • Nominal Scale: scores used for identification or naming. Ex: categories • Ordinal Scale: scores indicate rank or ordering. Ex: relative amount • Interval Scale: scores indicate actual amount. Ex: numbers • **0 doesn’t necessarily mean non • Ratio Scale: scores indicate actual amount Ex: numbers (0 actually means none)

  15. Continuous or Discrete • Continuous: allows fractional amounts (continues btwn whole numbers) • Usually Scale (ratio & interval) • Test score 97.6 • IQ score 145.9 • Discrete: measures only whole numbers • Usually Nominal or Ordinal • Male or Female • Eye color • Can be Scale • Ice cream or no ice cream • No. of crimes 3

  16. Let’s Graph

  17. Questions? • Let’s get Active with a CLE • Homework: Finish Ch.1 & 2 study guid • Review notes & text • Finish Ch. 1 & 2 of study guide • Preview Ch. 3 • Bring • Questions, book, calculator, pencils • Be ready for quiz

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