1. Pp # 1CHAPTER 1 Basic Concepts CHAPTER 2 Describing and Exploring Data Part A

2. scientific methods and apa style
Basic concepts
scientific methods and apa style

3. Behavioral Neuroscience
"The Contribution of Medial Prefrontal Cortical Regions to Conditioned Inhibition" by Heidi C. Meyer and David J. Bucci
Journal of Comparative Psychology
"Dogs (Canis familiaris) Account for Body Orientation but Not Visual Barriers When Responding to Pointing Gestures" by Evan L. MacLean, Christopher Krupeneye, and Brian Hare
Journal of Experimental Psychology: Animal Learning and Cognition
"Stress Increases Cue-Triggered "Wanting" for Sweet Reward in Humans" by Eva Pool, Tobias Brosch, Sylvain Delplanque, and David Sander
Journal of Experimental Psychology: General
"Searching for Explanations: How the Internet Inflates Estimates of Internal Knowledge" by Matthew Fisher, Mariel K. Goddu, and Frank C. Keil
Journal of Experimental Psychology: Human Perception and Performance
"What Can 1 Billion Trials Tell Us About Visual Search?" by Stephen R. Mitroff, Adam T. Biggs, Stephen H. Adamo, Emma Wu Dowd, Jonathan Winkle, and Kait Clark
Journal of Experimental Psychology: Learning, Memory, and Cognition
"The Tip-of-the-Tongue Heuristic: How Tip-of-the-Tongue States Confer Perceptibility on Inaccessible Words" by Anne M. Cleary and Alexander B. Claxton

4. APA WRITING STYLE

6. Reliability and validity of research

7. What is Statistics?
Set of methods and rules for ORGANIZING SUMMARIZING, and INTERPRETING information

8. Population Sample

10. Population Sample

11. Population and Sample
Population:
Population is the set of all individuals of interest for a particular study. Measurements related to Population are PARAMETERS (i.e., µ, σ)
Sample:
Sample is a set of individuals selected from a population. Measurements related to sample are STATISTICS (i.e., M, S)

12. Sample
The people chosen for a study are its subjects or participants, collectively called a sample.
The sample must be representative.

13. Hypothesis educated guess/statement
Selecting a Problem to investigate or a Research Topic
The root of Hypothesis is a question, which implemented in a theory.
Hypothesis should be clear, concise and reasonable

14. Formulating a hypothesis
Ex. The Effects Of TV Violence On Children (next slide)
Operational Definitions of Variables
Instruments
Accuracy of the Instruments
determined by Variance,
Reliability and Validity
Data Collection
Use of Statistics

15. Ex. The Effects Of TV Violence On Children
Or, The relationship between tv VIOLENCE and children violence
Question:
DOES Tv violence CAUSE children VIOLENCE? or
DOES tv violence related to children VIOLENCE?
Running head: TV Violence and Children
Theory: Tv violence may CAUSE children VIOLENCE or tv VIOLENCE may be related to children violence

17. OPERATIONAL DEFINATIONS
An operational definition is how we (the researcher) decide to measure the variables in our study
(variable = anything that can be measured).
There are usually hundreds of ways to measure a DV (e.g. behavior).

18. OPERATIONAL DEFINATIONS
Practice: How will you operationally define the following four items
Self-esteem, shyness, Love, Memory Loss
Hint: To operationally define the IV, you have to figure out how will you measure the IV. There is no one right answer. There are LOTS of ways to measure these items!

19. OPERATIONAL DEFINATIONS
Understanding the scientific process

21. Merriam Webster Dictionary and Thesaurus Definition of Short-Sighted
1. Near sighted or Myopia
2. Lacking Foresight
3. Lacking the power of foreseeing
4. Inability to look forward
My Operational Definition:
5. person who is able to see near things more clearly than distant ones, needs to wear corrected eyeglasses prescribed (measured) by Ophthalmologist.

22. The American Heritage Dictionary
Definition of Intelligent
1. Having or indicating a high or satisfactory degree of intelligence and mental capacity
My Operational Definition of Intelligent:
2. Revealing or reflecting good judgment or sound thought : skillful
And is measured by the IQ score from the Stanford-Binet V IQ Test ( in the Method section of the research paper we write about the reliability and validity of this instrument). You may select other IQ tests i.e., WAIS or WISC

26. Hypothesis is a Research Topic
“High Cholesterol Can Cause Heart Attack” Experimental Research

27. Hypothesis is a Research Topic
“Heart Attack is Related to High Cholesterol” Correlational Research

28. Hypothesis is a Research Topic
“A Causal Relationship Study of The effect of High Cholesterol on Heart Attack” SEM

29. Hypothesis is a Research Topic
A META ANALYTIC STUDY of Heart Attack and High Cholesterol

30. Hypothesis is a Research Topic
Study of Heart Attack and High Cholesterol: A Meta Analysis

31. Key Terms
Measurement:
Quantifying an observable behavior or when quantitative value is given to a behavior

32. Key Terms/Concepts
Variable: Any characteristic of a person, object or event that can change (vary).
Independent Variable, IV (manipulate)
Dependent Variable, DV (measure)
Constant
Discrete Numbers: 1, 2 3, 17, 123
Continues Numbers: 2.6, 3.5, 1.7
Confounding Variable
Intervening Variables

33. CONTINUOUS VERSUS DISCRETE VARIABLES
Discrete variables (categorical)
Values are defined by category boundaries
E.g., gender
Continuous variables
Values can range along a continuum
E.g., height

34. But—how a variable is measured can determine the level of precision
WHAT IS ALL THE FUSS?
Measurement should be as precise as possible. The precisions of your measurement tools will determine the precession of your research..
In psychology, most variables are probably measured at the nominal or ordinal level
But—how a variable is measured can determine the level of precision

35. heavy drinkers die at a younger age

36. Confounding Variables
Confounding variables are variables that the researcher failed to control, or eliminate, damaging the internal validity of an experiment. Also known as a third variable or a mediator variable, can adversely affect the relation between the independent variable and dependent variable.
Ex. Next

37. Confounding Variables
Ex: A research group might design a study to determine if heavy drinkers die at a younger age. Heavy drinkers may be more likely to smoke, or eat junk food, all of which could be factors in reducing longevity. A third variable may have adversely influenced the results.

38. Intervening Variables
A variable that explains a relation or provides a causal link between other variables.
Also called “Mediating Variable” or “intermediary variable.”
Ex. Association between income and longevity Next slide

39. Intervening Variables
Ex: The statistical association between income and longevity needs to be explained because just having money does not make one live longer.
Other variables intervene between money and long life. People with high incomes tend to have better medical care than those with low incomes.
Medical care is an intervening variable. It mediates the relation between income and longevity.

40. extraneous variables
These variables are undesirable because they add error to an experiment. A major goal in research design is to decrease or control the influence of extraneous variables as much as possible.
Ex. In a study examining the effect of post-secondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth.

41. The Fidelity of Scientific Research
Reliability - Dependability, replicability
Validity – “True”; It is what we say it is
Internal - Within the study
External - Generalizable
to the larger world

42. External & Internal Validity
External validity addresses the ability to generalize your study to other people and other situations. Ex. Correlational studies. The association between stress and depression

43. Internal Validity
Internal validity addresses the "true" causes of the outcomes that you observed in your study. Strong internal validity means that you not only have reliable measures of your independent and dependent variables But a strong justification that causally links your independent variables to your dependent variables (Ex. Experimental studies. The affect of stress on heart attack).

44. in Quantitative Research
The Role of Statistics
in Quantitative Research

45. Statistics
Descriptive VS
Inferential

46. Descriptive & Inferential Statistics
Descriptive Stats Describes the distribution of scores and values by using Mean, Median, Mode, Standard Deviation, Variance, Covariance, etc.
Inferential
Infer or draw a conclusion from a sample.
by using statistical procedures such as Correlation, Regression, t-test, ANOVA, etc.

47. Descriptive & Inferential Statistics
Scales of Measurement
Frequency Distributions and Graphs
Measures of Central Tendency
Standard Deviations and Variances
Z Score
t-Statistic
Correlations
Regressions………etc.

48. Scales of Measurement (NOIR)
Nominal Scale
Qualities
Example
What You Can Say
What You Can’t Say
Assignment of labels
Gender—
(male or
female)
Preference—
(like or dislike)
Voting record—(for or
against)
Each observation belongs in its own category
An observation represents “more” or “less” than another observation

49. ORDINAL SCALE Rank in college Order of finishing a race
Qualities
Example
What You Can Say
What You Can’t Say
Assignment of values along some underlying dimension (order)
Rank in college
Order of finishing a race
One observation is ranked above or below another.
The amount of one variable is more or less than another

50. INTERVAL SCALE Number of words spelled correctly on
Qualities
Example
What You Can Say
What You Can’t Say
Equal distances between points
“arbitrary zero”
Number of words spelled correctly on
Intelligence test scores
Temperature
One score differs from another on some measure that has equally appearing intervals
The amount of difference is an exact representation of differences of the variable being studied

52. RATIO SCALE Age Weight Time? Absolute zero
Qualities
Example
What You Can Say
What You Can’t Say
Meaningful and non-arbitrary zero
Absolute zero
Age
Weight
Time?
One value is twice as much as another or no quantity of that variable can exist
Not much!

53. LEVELS OF MEASUREMENT
Level of Measurement
Example
Quality of Level
Ratio
Rachael is 5’ 10” and Gregory is 5’ 5”
Absolute zero
Interval
Rachael is 5” taller than Gregory
An inch is an inch is an inch
Ordinal
Rachael is taller than Gregory
Greater than
Nominal
Rachael is tall and Gregory is short
Different from
Variables are measured at one of these four levels
Qualities of one level are characteristic of the next level up
The more precise (higher) the level of measurement, the more accurate is the measurement process

54. Test your knowledge Test scores are which scale of measurement?
Nominal
Ordinal
Interval
Ratio

55. Frequency Distributions and Graphs Bar

56. Frequency Distributions and Graphs Histogram

57. Histogram of Test Scores

58. Quiz
Frequency distributions of test scores are frequently illustrated by which kind of graph?
a. a histogram
b. a scatterplot
c. a pie chart
d. a bar graph

59. Quiz
Frequency distributions of test scores are frequently illustrated by which kind of graph?
*a. a histogram
b. a scatterplot
c. a pie chart
d. a bar graph

60. Polygon

61. Frequency Distributions and Graphs

69. Mesokurtic, Normal, Platykurtic, Leptokurtic,

70. Kernel Density Distribution Blue=Normal Distribution

71. Frequency Distributions

72. Frequency Distributions
Frequency Distributions(ƒ)is the number of frequencies,
Or when a score repeat itself in a group of scores.

73. Frequency Distributions
2, 4, 3, 2, 5, 3, 6, 1, 1, 3, 5, 2, , 2
Σƒ=N=14
Ρ=ƒ/N  Proportion
%=P x μ=ΣƒX/Σƒ mean for frequency distribution only

74. Frequency Distribution TableX f fX
P=f/n %=
px100
Cumulative % 1 2
2/14=.014
14% 4 8
4/14=0.29
29%
43% 3 9
3/14=0.21
21%
64%

75. Frequency Distributions
X f fX Ρ=ƒ/Σƒ %=P x 100 Cum%
/14= %
/14= %
/14= %
Data can be breakdown into smaller intervals

76. How do you Calculate Cumulative Percent ?
Add each new individual percent to the running tally of the percentages that came before it.
For example, if your dataset consisted of the four numbers: 100, 200, 150, 50 then their individual values, expressed as a percent of the total (in this case 500), are 20%, 40%, 30% and 10%.
The cumulative percent would be:1.Proportion 2.percentage
100/500=0.2x100: 20%
200: (i.e. 20% from the step before + 40%)= 60%
150: (i.e. 60% from the step before + 30%)= 90%
50: (i.e. 90% from the step before + 10%) = 100%

78. Frequency Distributions
X=2, f=4, N=14
Ρ=ƒ/N
P=4/14=.29
%=P x 100= 29%
X=3, f=4, N=14
P=3/14=.21
%= 21%

79. Stem-and-Leaf Displays
Stem-and-Leaf Displays is another method for displaying data with at least two significant digits.
Leading Digit are the most significant digits (Stems).
Trailing Digits are the less significant digit (Leaves).

80. Stem-and-Leaf Displays
A stem-and-leaf display is a device for presenting quantitative data in a graphical format, similar to a histogram, to assist in visualizing the shape of a distribution. A stem-and-leaf display is often called a Stemplot (popular in 70s and 80s).

83. Stem-and-Leaf Displays
Can be useful for comparing two different distributions. Such as comparing scores from men and women.
Just like frequency distribution raw data can be breakdown into smaller intervals (see p text or next slide).

84. Data can be breakdown into smaller intervals
Stem plot
Data can be breakdown into smaller intervals

86. Statistical Package for the Social Sciences

87. Frequency Distributions
2, 4, 3, 2, 5, 3, 6, 1, 1, 3, 5, 2, , 2