Pp 1 CHAPTER 1 Basic Concepts CHAPTER 2

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

Basic concepts Chap 1 scientific methods and apa style

§ 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. Mac. Lean, 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 Psychological Buletten : http: //www. apa. org/pubs/journals/bul/ Articles on Meta-Analysis 3

APA WRITING STYLE http: //www. apa. org/pubs/highlights/sp otlight/topic-basic. aspx http: //www. apa. org/pubs/highlights/pe eps/index. aspx

Selecting the right research topic requires critical thinking § 1. Generate Ideas (Theories) § 2. Identify Topic § 3. Develop Research Question § 4. Elaborate Research Question § 5. Describe Expectations 5

Selecting the right research topic requires critical thinking § 5. Describe Expectations: § You describe your expectations. This is the first step towards creating a hypothesis, or a testable prediction of the results. You might try to explain the relationship between two (or more) events or variables. You might also predict how changing one variable might affect another variable. 6

Selecting the right research topic requires critical thinking § Ex. Begin by reviewing your topic and research question below; § Topic: Personality and preference for dogs or cat. § Research Question: Are there differences in personality between people who prefer dogs vs. cats? (descriptive question) 7

steps in research process

SUMMARY OF RESEARCH METHODS

WHAT METHOD TO USE WHEN? © 2009 Pearson Prentice Hall, Salkind.

11

Types of research project § Your dissertation requires a comprehensive investigation that is relevant to the human services field. There is significant latitude on the type of project students may complete. Acceptable projects include: An empirical quantitative study An empirical qualitative study (Case Study) A thorough literature review with a meta-analysis A newly developed and needed psychometric assessment. § A translation and norming of an existing and important instrument (use of secondary data). § An experimental single-case design study (quantitative) § § 12

Types of research project § Evaluation and analysis of an existing data set. § This type of study requires a relevant introduction and literature review on a selected topic for which there is existing data. It summarizes archival data derived from past research, using appropriate statistical analyses to draw overall conclusions, interpret and discuss findings. 13

Reliability and validity of research 14

What is Statistics? §Set of methods and rules for ORGANIZING SUMMARIZING, and INTERPRETING information (data) 15

Population Sample 16

17

Population Sample 18

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) 19

Sample §The people chosen for a study are its subjects or participants, collectively called a sample. §The sample must be representative. 20

21

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 (idea). Ex. Next slide 22

When and how should the construction and development of Theory take place in the research process? § 1. Deductive Reasoning (Theory Comes First) : State a clear hypothesis in advance then, proceed with your analytical task to measure/match up your data against it. 23

When and how should the construction and development of Theory take place in the research process? § 2. Inductive Reasoning (Theory Comes Last): You will develop your theory through data generation/observations/interviews and analysis then, you will develop explanations which appear to fit them. For observations/interviews use qualitative research. Glaser and Straus (1967) call this theory generating and the next ‘Grounded Theory’ or ‘Constant Comparative Method. ’ 24

When and how should the construction and development of Theory take place in the research process? § 3. Theory, data generation and data analysis are developed simultaneously in a dialectical process: Device a method for moving back and forth between data analysis and the process of explanation/theory construction. For observations/interviews use qualitative research. Glaser and Straus (1967) call this theory generating and inductive reasoning “Grounded Theory or Constant Comparative Method. ” Blaikie’s characterization of ‘abductive reasoning’ and ‘retroductive reasoning’ also match this theory. 25

§Hypothesis theory question knowledge information 26

Ex. The Effects Of TV Violence On Children Or, The relationship between tv VIOLENCE and aggression in children § Question: § DOES Tv violence CAUSE aggression in children? or § DOES tv violence related to aggression in children? § Running head: TV Violence and Children § Theory: Tv violence may CAUSE aggression in children or tv VIOLENCE may be related to aggression in children 27

Formulating a hypothesis § Ex. The Effects Of TV Violence On Children § Operational Definitions of Variables § Instruments used § Accuracy of the Instruments- (next slide) determined by Variance, Reliability and Validity § Data Collection § Use of Statistics § Hypothesis should be clear, concise and reasonable. 28

29

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). 30

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! 31

OPERATIONAL DEFINATIONS § Understanding the scientific process § http: //undsci. berkeley. edu/article/0_0_0/howscienceworks_02 32

33

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. 34

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 35

36

37

Hypothesis is a Research Topic § “High Cholesterol Can Cause Heart Attack” Experimental Research 39

Hypothesis is a Research Topic § “Heart Attack is Related to High Cholesterol” Correlational Research 40

Hypothesis is a Research Topic § “A Causal Relationship Study of The effect of High Cholesterol on Heart Attack” SEM 41

Hypothesis is a Research Topic §A META ANALYTIC STUDY of Heart Attack and High Cholesterol 42

Hypothesis is a Research Topic Study of Heart Attack and High Cholesterol: A Meta Analysis 43

Hypothesis is a Research Topic §It is better to look for the related literature at the same time you are formulating your hypothesis. The relevant research will guide you to select the type of the research which is best for your research. 44

Key Terms q Measurement: Quantifying an observable behavior or when quantitative value is given to a behavior 45

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 (ex. The effect of hunger on learning) § Discrete Numbers: 1, 2 3, 17, 123 § Continues Numbers: 2. 6, 3. 5, 1. 7 § Discrete Variables § Intervening Variables § Confounding Variables 46

CONTINUOUS VERSUS DISCRETE VARIABLES § Discrete variables (categorical) § Values are defined by category boundaries § E. g. , gender § Continuous variables § Values can range along continuum § E. g. , height a 47

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 48

heavy drinkers die at a younger age 49

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 50

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. 51

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 52

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. 53

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 postsecondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth.

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 55

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 56

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). attack 5 7

The Role of Statistics in Quantitative Research

Statistics §Descriptive §VS §Inferential 59

Descriptive Statistics § Descriptive Stats Describes the distribution of scores and values by using Measures of Central Tendency(Mean, Median, Mode), Variability (Standard Deviation, Variance), frequencies, percentage, etc. § Can be Qualitative or Quantitative research. § Three kinds of Descriptive Research: § 1. Case Study Qualitative Research § 2. Survey (using questioner) Qualitative or Quantitative Research or mixed § 3. Observational Studies Qualitative or Quantitative Research or Mixed 60

Descriptive & Inferential Statistics § Inferential Infer or draw a conclusion from a sample. by using statistical procedures such as Correlation, Regression, t-test, ANOVA, etc. Research ex. Association between Anger and Depression 61

Descriptive & Inferential Statistics §Statistical inference is the process of deducing/concluding properties of an underlying probability distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. 62

Descriptive & Inferential Statistics §statistical inference aims at learning characteristics of the population from a sample; the population characteristics are parameters and sample characteristics are statistics. 63

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. 64

Scales of Measurement (NOIR) Nominal Scale Qualities Assignment of labels Example What You Can Say Gender— Each (male or observation female) belongs Preference— in its (like or dislike) own Voting record category —(for or against) What You Can’t Say An observation represents “more” or “less” than another observation 65

ORDINAL SCALE Qualities Assignment of values along some underlying dimension (order) Example Rank in college Order of finishing a race What You Can Say One observation is ranked above or below another. What You Can’t Say The amount of one variable is more or less than another 66

INTERVAL SCALE Qualities Equal distances between points “arbitrary zero” Example Number of words spelled correctly on Intelligence test scores Temperature What You Can Say What You Can’t Say 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 67

68

RATIO SCALE Qualities Meaningful and nonarbitrary zero Absolute zero Example Age Weight Time? What You Can Say One value is twice as much as another or no quantity of that variable can exist What You Can’t Say Not much! 69

LEVELS OF MEASUREMENT Level of Measurement Example Quality of Level Ratio Rachael is 5’. 10” and Gregory Absolute zero is 5’. 5” Interval Rachael is 5” taller than Gregory An inch is an inch Ordinal Rachael is taller than Gregory Greater than Nominal Rachael is tall Rachael is 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 70

Test your knowledge § Test scores are which scale of measurement? § A. Nominal § B. Ordinal § C. Interval § D. Ratio 71

Frequency Distributions and Graphs Bar 72

Frequency Distributions and Graphs Histogram 73

Histogram of Test Scores 74

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 75

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 76

Polygon 77

Frequency Distributions and Graphs 78

79

80

81

82

83

84

85

Mesokurtic, Normal, Platykurtic, Leptokurtic, 86

Descriptive Statistics Measures of Central Tendency 87

Measures of Central Tendency § Mean---- Interval or Ratio scale Polygon § The sum of the values divided by the number of values--often called the "average. " μ=ΣX/N § Add all of the values together. Divide by the number of values to obtain the mean. § Example: X 7 12 24 20 19 ? ? 88

Descriptive Statistics The Sample Mean is: M or (x ) and μ=Population Mean μ=ΣX/N= 82/5=16. 4 (7 + 12 + 24 + 20 + 19) / 5 = 16. 4. 89

The Characteristics of Mean § 1. Changing a score in a distribution will change the mean § 2. Introducing or removing a score from the distribution will change the mean § 3. Adding or subtracting a constant from each score will change the mean § 4. Multiplying or dividing each score by a constant will change the mean § 5. Adding a score which is same as the mean will not change the mean 90

Measures of Central Tendency § Median Middle Ordinal Scale Bar/Histogram § Divides the values into two equal halves, with half of the values being lower than the median and half higher than the median. § Sort the values into ascending order. § If you have an odd number of values, the median is the middle value. § If you have an even number of values, the median is the arithmetic mean (see above) of the two middle values. § Example: The median of the same five numbers (7, 12, 24, 20, 19) is ? ? ? . 91

Measures of Central Tendency § The median is 19. § Mode Nominal Scale Bar/Histogram § The most frequently-occurring value (or values). § Calculate the frequencies for all of the values in the data. § The mode is the value (or values) with the highest frequency. § Example: For individuals having the following ages -- 18, 19, 20, 20, 21, and 23, the mode is ? ? 92

CHARACTERISTICS OF MODE § Nominal Scale § Discrete Variable § Describing Shape 93

WHEN TO USE WHICH MEASURE Measure of Central Tendency Level of Measurement Use When Examples Mode Nominal Data are categorical Eye color, party affiliation Median Ordinal Data include Rank in class, extreme scores birth order, income Mean Interval and ratio You can, and the data fit Speed of response, age in years

§Frequency Distributions 95

Frequency Distributions §Frequency Distributions(ƒ)is the number of frequencies, Or when a score repeat itself in a group of scores. 96

Frequency Distributions §Frequency Distributions (ƒ) 2, 4, 3, 2, 5, 3, 6, 1, 1, 3, 5, 2, 4, 2 Σƒ=n=14 Ρ=ƒ/n Proportion %=P x 100 μ=ΣƒX/Σƒ mean for 97 frequency distribution only

Frequency Distribution Table X f f. X P=f/n %= px 100 Cumulative %

Frequency Distribution Table Cumulative % X f f. X P=f/n %= px 100 1 2 2 2/14=. 14 14% 2 4 8 4/14=0. 29 29% 43% 3 3 9 3/14=0. 21 21% 64%

Frequency Distribution Table Cumulative % X f f. X P=f/n %= px 100 4 2 8 2/14=. 14 14% 78% 5 2 10 2/14=0. 14 14% 92% 6 1/14=0. 07 7% 99% or 100%

Frequency Distributions § μ=ΣƒX/Σƒ § 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% 101

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. 2 x 100: 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% 102

103

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). 104

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-andleaf display is often called a Stemplot (popular in 70 s and 80 s). 105

106

107

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. 25 -26 text or next slide). 108