☆ Nussbaum - PSYB01 - Stats & Research Methods Lec Compilation

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Department
Psychology
Course
PSYB01H3
Professor
David Nussbaum
Semester
Fall

Description
Research Methods Lecture 2: Quick Stats Review What size should armour be? Contract with tribe’s chief, who will pay you. Armour price related to Armour size. - Measured various people in army, and come up with some of describing what typical size of armour was, because that is what you are going to build and get paid for. - (Height in cm) ------ 160 162 164 166 ------ - Measured these 4 people; variability present; looking for what is typical? - Calculate mean: 163 => average is 163cm Normal curve: - 163 in middle Problem 1 - Alternative distribution ------ 130 130 190 190 380 + 260 = 640/4 = 160 => average is 160cm ------ - Want to manufacture suits of armour and made at all 160cm; not good because too big for 190, too small for 130 individuals - Mean calculation can work with normal distribution well, but not as well with others - Normal curve distributed compared to two normal curves side by side and have our point in the midst between them - Instead of mean, can count what is most frequent value that shows up (mode – two modes exist here: 130, and 190) – Bimodal distribution, but uses notion of what is most typical scores area Problem 2 – 1,000,000 90,000 70,000 50,000 20,000 0 ------------------------------------------- - Outlier: 1,000,000 - Resolution: try using median - W.r.t. descriptive statistics: mean, mode and median are main ones for describing central tendency - Helpful depending on what the question is, and what shape of distribution is. - E.g. 3 different distributions A- One normal curve (e.g. in height, weight ,IQ (mean = 100)) B- One very thin curve with spike at 100 C- One a very low flatter hill ^- Not the same distribution, despite mean, mode and median the same - So how can one describe what is different about these distributions in quantitative terms? - What differs between them: variability B- a lot of 100s, but other values are very, very low - Look here at: What is average distance from central tendency? 10 12 14 16 Mean = 13 - If substract 13 from each value and then square 10 -13 12 – 13 14 – 13 16 – 13 = -3 -1 -1 -3 Mean deviation from mean of 0 Square the values  9 1 1 9 = 20 – variance Square root the variance  standard deviation: \sqrt{20} = 4.5 (1) Subtract all values from mean, (2) Square all values, (3) Add them up: Variance, (4) Square root: Standard deviation - Mean IQ: 100; standard deviation is 85, 115 - E.g. Measure the IQ  need one number that tells not only what their IQ is, but where they fall on this scale w.r.t standard deviation; one way: - 115: one standard deviation above the mean (+1) - 85: one standard deviation below the mean (-1) - z-scores ^- - Have z-score on VAR1, and another on VAR2 - Correlation coefficient is ratio of z-scores Key Statistical Concepts from “Quick Stats Review” Mean Mode Median Variance Standard deviation z-scores Lecture 3: EEG, QEEG & EP Biological Substrate being Captured - It is through axon conduction and synaptic transmission that neurons convey and process information - Excitatory electrochemical events linked to PSPs spread from neural tissue of origin to through brain tissue, meninges and skull. - Each electrode detects electrocellular activity of about 10 billion cortical neurons (Yikes!) - As the resultant diffuse electrical currents move from the brain and meninges through the skull, they are furthered scattered as they pass through the skull. There, they activate surface electrodes placed on the skull - EEG reflects activity of neurons close to electrodes; unable to detect deep structures (e.g. hypothalamus) Locating the electrodes - To locate the exact electrode position, EEG uses 4 anatomical landmarks from which measurements can be taken. 1. Nasion: indention between the forehead and nose 2. Inion: ridge that can be felt at the mid-line of the skull’s back 3. Over the occipital area 4. Preauricular points: indentations just above the cartilage that covers the external ear openings - 10-20 system: The electrode locations and distances between the electrodes are defined as 10% or 20% of these anatomical distances - The typical electrode placements for clinical investigation - Electrode locations denoted by brain region: F = frontal lobe T = temporal lobe C = central lobe P = parietal lobe O = occipital lobe A = auricular (ear) Basic Frequency Bands – Associated Amplitudes, Functional States and EEG Frequencies - Hz: frequency; microvolts: amplitude - Frequencies historically divided into 5: DELTA THETA ALPHA BETA GAMMA * * * ** - 0-4 Hz - 4-8 Hz - 8-13 Hz 13-32 microvolts 32-64 - 20-200 - 20-100 - 20-60 (i.e. microvolts microvolts microvolts microvolts Beta-1: 13-21 - 2-4cps - 4-7cps - 8-11cps Beta-2: 22-32) - Deep - - Relaxed - Engaged/very active brain sleep Drowsiness but alert - If very fast, then excited/urgency/emergency * Slower waveform activity (fewer cycles per second)  indicates lowered blood flow and fuel (glucose) in that brain area ** Faster activity shows increased brain activity - Types of brain electrical activity also reflect the level of arousal and associated functions of the person - EEG wave forms analyzed according to time periods = epochs Order: Delta < Theta < Alpha < Beta (1 < 2) < Gamma Typical Clinical Purposes of EEG 1. Epilepsy diagnosis to determine occurrence of which seizure type 2. Check for problems of loss of consciousness, dementia 3. Help determine probability of recovery after change in consciousness 4. Determine if comatosed patient is brain-dead 5. Study sleep disorders (e.g. narcolepsy) 6. Monitor brain activity while administrating general anesthesia during brain surgery 7. Help determine the existence of physical problem (e.g. problem in brain, spinal cord, nervous system) or mental health problem Epilepsy - EEG commonly used to confirm epilepsy suspicion - May require further tests (e.g. CT, MRI, PET) to get more specific information as to what is cause and location of occurrence in brain EEG: Research Applications (1) DV = EEG wave amplitude and patterns - Differentiate based on specific task parameters & stimulus - Assists in diagnosing or linking to particular symptoms (2) EEG signals may confirm or exclude ischemic strokes of lacunar and posterior circular conditions (Sheroajpanday et al, 2011). (3) EEG is an effective inverse index for detecting cerebral activity in period between response for one stimulus and the next in Stroop task (Compton et al, 2011). - Found differences in alpha power during intertrial intervals  support for right and left hemisphere and their role in behavioural activation and behavioural inhibition (4) Right-left desynchronization magnitude for action perception & production differs based on developmental stage; infants have smaller magnitudes vs. adults, or older children (Marshall et al, 2011). EEG – STRENGTHS AND LIMITATIONS Strengths Limitations 1. Direct instead of indirect 1. Wave frequency varies based on age; lower in epileptic abnormality children & in 60+ 2. Cost: low 2. Although cortical dysfunction detected, 3. Morbidity: low etiology disclosure rarely 4. Portable 3. Sensitivity and specificity: relatively low 5. Summary of on-going “real- 4. Prone to electrical and physiologic artifacts time” states 5. Influenced by state of alertness & drugs 6. Safely used on various 6. Small/deep lesions may not produce EEG patients; includes infants abnormality 7. Detects ms level changes, 7. Limited time sampling (for routine EEG) and which’re crucial taking into spatial sampl
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