Introduction to classical and Modern Test Theory

Introduction to classical and Modern Test Theory

Pp # 1CHAPTER 1 Basic Concepts CHAPTER 2 Describing and Exploring Data Part A 1 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. Claxton3 APA WRITING STYLE otlight/topic-basic.aspx eps/index.aspx

5 Reliability and validity of research 6 What is Statistics? Set of methods and rules for ORGANIZING SUMMARIZING, and INTERPRETING information 7

Population Sample 8 9 Population Sample 10

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., , ) Sample: Sample

is a set of individuals selected from a population. Measurements related to sample are STATISTICS (i.e., M, S) 11 Sample The people chosen for a study are its subjects or participants, collectively called a sample. The sample must be

representative. 12 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 13 Formulating a hypothesis

Ex. The Effects Of TV Violence On Children Operational Definitions of Variables Instruments

Accuracy of the Instruments (next slide) determined by Variance, Reliability and Validity Data Collection Use of Statistics 14

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 15 16 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). 17 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! 18 OPERATIONAL DEFINATIONS Understanding the scientific process 19 20 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. 21 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 StanfordBinet 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 22 23 24 Hypothesis is a Research Topic High

Cholesterol Can Cause Heart Attack Experimental Research 26 Hypothesis is a Research Topic Heart Attack is Related to High Cholesterol Correlational Research 27

Hypothesis is a Research Topic A Causal Relationship Study of The effect of High Cholesterol on Heart Attack SEM 28 Hypothesis is a Research Topic A META ANALYTIC STUDY of Heart

Attack and High Cholesterol 29 Hypothesis is a Research Topic Study of Heart Attack and High Cholesterol: A Meta Analysis 30 Key Terms

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

Variable: Any characteristic of a person, object or event that can change (vary). Independent Dependent Variable, IV (manipulate) Variable, DV (measure) Constant Discrete Numbers: 1, 2 3, 17, 123

Continues Numbers: 2.6, 3.5, 1.7 Confounding Intervening Variable Variables 32

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

33 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 Buthow a variable is measured can determine the level of precision 34 heavy drinkers die at a younger age 35 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 36 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. 37 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 38 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. 39

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 Scientifi Research Reliability - Dependability, replicability Validity True; It is what we say it is Internal - Within the study External - Generalizable to the larger world

41 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 42 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 4 3 Statistics

Descriptive VS Inferential 45 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. 46 Descriptive & Inferential Statistics Scales of Measurement

Frequency Distributions and Graphs Measures of Central Tendency Standard Deviations and Variances

Z Score t-Statistic Correlations Regressionsetc.

47 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 own dislike) categor Voting record(for y or

against) What You Cant Say An observati on represent s more or less than another observati

on 48 ORDINAL SCALE Qualities Assignmen t of values along some underlying dimension (order)

Example Rank in college Order of finishing a race What You Can Say One observatio n is

ranked above or below another. What You Cant Say The amount of one variable is more

or less than another 49 INTERVAL SCALE Qualities Equal distances between points Example

Number of words spelled arbitrary correctly on Intelligence zero test scores Temperatur e What You Can Say

What You Cant Say One score differs from another on some measure that has equally appearin g

intervals The amount of difference is an exact representatio n of differences of the variable being studied

50 51 RATIO SCALE Qualities Meaningful Example Age and non- Weight arbitrary

Time? zero Absolute zero What You Can Say One value is twice as much as another or no

quantity of that variable can exist What You Cant Say Not much! 52 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 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 53 Test your knowledge Test scores are which scale of measurement? Nominal

Ordinal Interval Ratio 54

Frequency Distributions and Graphs Bar 55 Frequency Distributions and Graphs Histogram 56 Histogram of Test Scores

57 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 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 Polygon 60

Frequency Distributions and Graphs 61 62 63 64 65 66

67 68 Mesokurtic, Normal, Platykurtic, Leptokurtic, 69 Kernel Density Distribution Blue=Normal Distribution 70

Frequency Distributions 71 Frequency Distributions Frequency Distributions()is the number of frequencies, Or when a score repeat itself in a group of scores.

72 Frequency Distributions Frequency Distributions () 2, 4, 3, 2, 5, 3, 6, 1, 1, 3, 5, 2, 4, 2 =N=14=N=14 =/N Proportion %=P x 100 ==N=14X/=N=14 mean for frequency distribution only

73 Frequency Distribution Table X f fX P=f/n %= px100 1

2 2 2/14=.014 14% 14% 2 4

8 4/14=0.29 29% 43% 3 3 9

3/14=0.21 21% 64% Cumulative % Frequency Distributions Frequency fX

Distributions () X f =/=N=14 %=P x 100 Cum% 1 2 2

4 3 3 4 2 8 2/14=0.14

14% 5 2 10 2/14=0.14 14% 6

1 6 1/14=.07 7% Data can be breakdown into smaller intervals 75 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% 76 77 Frequency Distributions X=2, f=4, N=14

=/N P=4/14=.29 %=P x 100= 29% X=3, f=4, N=14

P=3/14=.21 %= 21% 78 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). 79 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 70s and 80s). 80 81 82

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). 83

Stem plot Data can be breakdown into smaller intervals 84 85 S tatistical P

ackage for the S S ocial ciences 86 Frequency Distributions Frequency Distributions () 2, 4, 3, 2, 5, 3, 6, 1, 1, 3, 5, 2, 4, 2

87 88 89 90

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