Psych Lab Midterm

Ordinal scale

-An Ordinal Scale is used to measure things by ranking and doesn't necessarily imply equal distances between the rankings. -Medals in the Olympics (Gold, Silver, Bronze) and socioeconomic status (upper, middle, lower) are examples of ordinal scales.

Protoscience

-Protoscience is sometimes distinguished from pseudoscience by a willingness to be changed through new evidence -Some protosciences go on to become an accepted part of mainstream science.

Qualitative theory

-Qualitative researchers aim to gather an in-depth understanding of human behavior and the reasons that govern such behavior. -The qualitative method investigates the why and how of decision making, not just what, where, when. Hence, smaller but focused samples are more often used than large samples.

demand characteristics

-Sometimes during an experiment, a participant might pick up on some clue or bias from the researcher, the situation, or something about the experiment that gives the participant and idea of what type of response the researcher is looking for. -this doesn't mean that the participant is right, just that something makes them act in a way they think is what the researcher wants and not necessarily in their normal manner.

theory

-a well-confirmed type of explanation of nature, made in a way consistent with scientific method, and fulfilling the criteria required by modern science. -Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,

predictive validity

-is the extent to which performance on a test is related to later performance that the test was designed to predict. -For example, the SAT test is taken by high school students to predict their future performance in college (namely, their college GPA). If students who scored high on the SAT tend to have high GPAs in college, then we can say that the SAT has good predictive validity

Quantitative theory

-is the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. -The objective of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena.

Criterion Validity

-measures the degree to which the test scores measuring one test criterion is consistent with other criterion being measured. -For instance, if an achievement test is aimed at measuring a normal 5th grader's achievement, it is important that both the language skills and mathematics tests are properly calibrated for an average 5th grader. -A typical way to achieve this is the extent to which a score on a personality test can predict future performance or behavior.

content validity

-refers to how well a test measures the behavior for which it is intended. -For example, let's say your teacher gives you a psychology test on the psychological principles of sleep. The purpose of this test is to measure your knowledge or mastery of the psychological priniciples of sleep, right?

Construct Validity

-refers to the ability of a measurement tool (e.g., a survey, test, etc) to actually measure the psychological concept being studied. In other words, does it properly measure what it's supposed to measure? -or example, if we want to know our height we would use a tape measure and not a bathroom scale because all height measurements are expressed in inches and not in pounds. -THEORY BASED

discussion section

1. Function: The function of the Discussion is to interpret your results in light of what was already known about the subject of the investigation, and to explain our new understanding of the problem after taking your results into consideration. The Discussion will always connect to the Introduction by way of the question(s) or hypotheses you posed and the literature you cited, but it does not simply repeat or rearrange the Introduction. Instead, it tells how your study has moved us forward from the place you left us at the end of the Introduction. Fundamental questions to answer here include: Do your results provide answers to your testable hypotheses? If so, how do you interpret your findings? Do your findings agree with what others have shown? If not, do they suggest an alternative explanation or perhaps a unforseen design flaw in your experiment (or theirs?) Given your conclusions, what is our new understanding of the problem you investigated and outlined in the Introduction? If warranted, what would be the next step in your study, e.g., what experiments would you do next? 2. Style: Use the active voice whenever possible in this section. Watch out for wordy phrases; be concise and make your points clearly. Use of the first person is okay, but too much use of the first person may actually distract the reader from the main points. 3. Approach: Organize the Discussion to address each of the experiments or studies for which you presented results; discuss each in the same sequence as presented in the Results, providing your interpretation of what they mean in the larger context of the problem. Do not waste entire sentences restating your results; if you need to remind the reader of the result to be discussed, use "bridge sentences" that relate the result to the interpretation:

Nominal scale

A Nominal Scale is a measurement scale that identifies things using a word. Also known as a qualitative scale, items are usually organized by their category or name. -Gender and Ethnicity.

bar graph

A bar graph is a chart that uses either horizontal or vertical bars to show comparisons among categories. One axis of the chart shows the specific categories being compared, and the other axis represents a discrete value.

split-half reliability

A measure of consistency where a test is split in two and the scores for each half of the test is compared with one another. If the test is consistent it leads the experimenter to believe that it is most likely measuring the same thing.

abstract

An abstract summarizes, in one paragraph (usually), the major aspects of the entire paper in the following prescribed sequence:

Scientific explanation

An intelligible account of why something happens. On a covering law model, the scientific explanation of an event has the form of an argument whose conclusion is the event to be explained and whose premises include both antecedent circumstances and one or more hypotheses.

Analysis of variance (ANOVA)

Analysis of variance is a statistical test to determine if all sample groups in a study are affected by the same factors, and if they are affected to the same degree. The groups are kept separate and tests are done independently on each group, but the results are then compared. -We use this when more than 2 groups -We use when have more than 1 IV interaction

Planned and unplanned comparison

Comparisons that the researcher intended to make before they collected the data are referred to as planned comparisons. Comparisons that the researcher decides to make after they get the data are referred to as unplanned comparisons.

Confirmational strategy

Confirming hypothesis

degrees of freedom

Degrees of Freedom is a number used in statistical analysis to indicate how many ways the obtained results could have been found through random sampling.

Validity

If the test does indeed measure what it is intended to measure, then we can say that the test is valid (or has validity) -validity refers to whether a study is able to scientifically answer the questions it is intended to answer. -Internal validity: within experiment; One of the keys to understanding internal validity (IV) is the recognition that when it is associated with experimental research it refers both to how well the study was run (research design, operational definitions used, how variables were measured, what was/wasn't measured, etc.), and how confidently one can conclude that the change in the dependent variable was produced solely by the independent variable and not extraneous ones. -External: generalizability

scientist

In a more restricted sense, a scientist may refer to an individual who uses the scientific method.

Inferential statistics

Inferential statistics provide ways of testing the reliability of the findings of a study and "inferring" characteristics from a small group of participants or people (your sample) onto much larger groups of people (the population). inferential let you say what the data mean.

parallel-forms reliability (Inter-method reliability)

Inter-method reliability assesses the degree to which test scores are consistent when there is a variation in the methods or instruments used. -This allows inter-rater reliability to be ruled out. When dealing with forms, it may be termed parallel-forms reliability.

pearson correlation

Is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation.

normal distribution

Often referred to as "bell curves" (because the shape looks like a bell) it tracks rare occurrences of a trait on both the high and low ends of the "curve" with the majority of occurrences appearing in the middle section of the curve.

Range, Interquartile, Variance, SD

Range is the difference between the highest and lowest scores in a data set and is the simplest measure of spread. Interquartile range is a statistical measurement that defines the middle range of numbers in a distribution and can be used to approximate the most common scores. -IQ = Q3-Q1 Variance is a measure of how much values in a data set differ from the mean. Do scores tend to center around the mean or are they spread out? Standard Deviation is a measure of variation (or variability) that indicates the typical distance between the scores of a distribution and the mean.

Statistical power

Statistical power is the likelihood that a test will be able to to detect an effect (during a research study) when one truly exists. Instead, we test the opposite of our hypothesis, called the null hypothesis, by looking for enough evidence to say that it is false, and we should reject it. In rejecting the null hypothesis, we are in effect saying that our hypothesis is true. In other words, Statistical Power is the probability of correctly rejecting the null hypothesis when it is in fact false (meaning, the original hypothesis is true).

introduction

The function of the Introduction is to: Establish the context of the work being reported. This is accomplished by discussing the relevant primary research literature (with citations) and summarizing our current understanding of the problem you are investigating; State the purpose of the work in the form of the hypothesis, question, or problem you investigated; and, Briefly explain your rationale and approach and, whenever possible, the possible outcomes your study can reveal.

t-test for independent samples

The independent samples t-test is used when two separate sets of independent and identically distributed samples are obtained, one from each of the two populations being compared. For example, suppose we are evaluating the effect of a medical treatment, and we enroll 100 subjects into our study, then randomly assign 50 subjects to the treatment group and 50 subjects to the control group.

Mean, Median, Mode

The mean tells us the average value or score; the median tells us the midpoint in the range of values; the mode tells us the most common value in the data set.

scientific method

The overall process of the scientific method involves making conjectures ( hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions. -Hypothesis must be falsifiable

Parsimonious explanation

The simplest expression of scientific truth; where 2 theories exist to explain a similar phenomenon, the one making the fewest assumptions should prevail—i.e., it should be no more complicated than necessary.

t-test

The t-test is a statistical test that is used to determine if there is a significant difference between the mean or average scores of two groups. First, it determines if the means are sufficiently different from each other to say that they belong to two distinct groups. Second, the T-Test also takes into account the variability in scores of the two groups. This is called the standard error,

effect size

This is a statistical term that refers to the size of a relationship between two variable. Sometimes effect size is known as treatment effect because it is often used when dealing with therapeutic intervantions (ie., this treatment is shown to be more effective than another at treating a specific disorder).

face validity

This is a very basic form of validity in which you determine if a measure appears (on the face of it) to measure what it is supposed to measure. In other words, does the measure "appear" to measure what it is supposed to measure?

double blind

This is one type of experimental procedure in which both the patient and the staff are ignorant (blind) as to the condition (or group) that the participant is in. This type of design is commonly used in drug evaluation studies, and is used to prevent the researchers from acting differently to people in one group, or from giving the participant any information that could make them act and/or behave unnaturally.

scatter plot

a scatterplot is a visual representation of the relationships or associations between two numerical variables, which are represented as points (or dots), each plotted at a horizontal axis (y-axis) and vertical axis (y-axis).

variable

a variable is any factor that can be controlled, changed, or measured in an experiment -IV: variable that can be changed in the experiment -DV: variable that is measured or observed.

outlier

an outlier is a distribution point that is much further away from any other distribution points. Outliers can skew measurements so that the results are not representative of the actual numbers.

self-reported measures

are any methods of data collection that rely on the participant to report his or her own behaviors, thoughts, or feelings. The advantage of this method is that the researcher can obtain information that is not easily observable, but the disadvantage is that participants' report may not be accurate or reliable.

Expectancy Effects

are the results that an experimental researcher or observer generates when they have somehow, usually subtly and subconsciously, communicated the expected results to the participant.

Descriptive Statistics

are used by researchers to summarize and "describe" data found during research. Typically researchers deal with lots of data and descriptive statistics provide a way for the researchers to summarize the main properties of a large group of data into just a few numbers. -ex: mean, range, sd, etc.

Test-retest reliability

assesses the degree to which test scores are consistent from one test administration to the next. Measurements are gathered from a single rater who uses the same methods or instruments and the same testing conditions.

Measures of spread

describe how similar or varied the set of observed values are for a particular variable (data item). Measures of spread include the range, quartiles and the interquartile range, variance and standard deviation.

histogram

histogram is very similar to a bar graph in which each bar represents some class or element (for example, a score on an IQ test). The primary difference between a bar graph and a histogram is that the bars in the histogram actually touch each other to show that there are no gaps in between the classes.

common sense explanation

is a basic ability to perceive, understand, and judge things, which is shared by ("common to") nearly all people, and can be reasonably expected of nearly all people without any need for debate

Pseudoscience

is a claim, belief or practice which is falsely presented as scientific, but does not adhere to a valid scientific method, cannot be reliably tested,

five-number summary

is a descriptive statistic that provides information about a set of observations. It consists of the five most important sample percentiles: the sample minimum (smallest observation) the lower quartile or first quartile the median (middle value) the upper quartile or third quartile the sample maximum (largest observation)

applied research

is a form of systematic inquiry involving the practical application of science. It accesses and uses some part of the research communities' (the academia's) accumulated theories, knowledge, methods, and techniques, for a specific, often state-, business-, or client-driven purpose.

Ratio scale

is a measurement scale that has a numerical difference and ratios between two items. A ratio scale has a true zero which means when an item equals 0 there is none of that variable. Height, weight and length are all ratio scales.

hypothesis

is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it.

pilot study

is a small scale preliminary study conducted in order to evaluate feasibility, time, cost, adverse events, and effect size (statistical variability) in an attempt to predict an appropriate sample size and improve upon the study design prior to performance of a full-scale research project

science

is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about nature and the universe. This knowledge is determined through the scientific method by experiments and observations, and may take the form of scientific facts, scientific models, or scientific theories.[

basic research

is a systematic study directed toward greater knowledge or understanding of the fundamental aspects of phenomena.[1] Basic research is executed without thought of a practical end goal, without specific applications or products in mind.

frequency distribution

is a table that displays the frequency of various outcomes in a sample. Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample.

line graph

is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. A line chart is often used to visualize a trend in data over intervals of time

measures of center

is a value at the center or middle of a data set. Graphically, the center can be viewed as the "balance point" of the display. Algebraically, the most common ways to find the center are with the mean, median, or mode.

exploratory data analysis

is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task -Ex: bar graph, histogram, scatter plot

concurrent validity

is the extent to which performance on a measurement is related to current performance on a similar, previously established measurement. -Concurrent validity is demonstrated when a test correlates well with a measure that has previously been validated.

reliability

is the overall consistency of a measure. -A measure is said to have a high reliability if it produces similar results under consistent conditions. -For example, measurements of people's height and weight are often extremely reliable.

standard error of the mean

is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population.

confirmation bias

is the tendency to search for, interpret, or recall information in a way that confirms one's beliefs or hypotheses. -People display this bias when they gather or remember information selectively, or when they interpret it in a biased way.

Interval scale

it allows for quantifying the degree of difference between items, but does not measure the ratio of difference between items. -In everyday terms this is the type of differences that are measured by thermometers or calendars.

linear regression

linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X.

experimenter bias

occurs when a researcher unconsciously affects results, data, or a participant in an experiment due to subjective influence. It is difficult for humans to be entirely objective which is not being influenced by personal emotions, desires, or biases. It is very important to consider experimenter bias as a possible issue in any research setting. Steps can be taken to reduce the likelihood of its occurrence such as conducting blind studies and finding non-biased data collectors.

title page

our paper should begin with a Title that succinctly describes the contents of the paper. Use descriptive words that you would associate strongly with the content of your paper: the molecule studied, the organism used or studied, the treatment, the location of a field site, the response measured, etc. A majority of readers will find your paper via electronic database searches and those search engines key on words found in the title.

p-value

p-value is a function of the observed sample results (a statistic) that is used for testing a statistical hypothesis. Before performing the test a threshold value is chosen, called the significance level of the test, traditionally 5% or 1% If the p-value is equal to or smaller than the significance level (α), it suggests that the observed data are inconsistent with the assumption that the null hypothesis is true, and thus that hypothesis must be rejected and the alternative hypothesis is accepted as true.

paired sample t-test

paired samples t-tests typically consist of a sample of matched pairs of similar units, or one group of units that has been tested twice (a "repeated measures" t-test). A typical example of the repeated measures t-test would be where subjects are tested prior to a treatment, say for high blood pressure, and the same subjects are tested again after treatment with a blood-pressure lowering medication.

Disconfirmational strategy

rejecting hypotheses

Single blind

specific research procedure in which the researchers (and those involved in the study) do not tell the participants if they are being given a test treatment or a control treatment. For example, if a participant believed they were in the group that received a sleeping drug, they may report that they are tired because they believe they "should be tired" since they're in the sleeping drug group.

skewed distribution

term that measures "asymmetry" (lack of similarity) in a "bell curve" The "skewness" (percent of difference) statistic measures how great a change there is in the number of trait occurrences on either side of the mid-point of the curve.

accuracy

the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's actual (true) value.

manipulation check

the experimenter changes or adjusts variables in an experiment and randomly assigns subjects to conditions (groups) to see whether or not those changes create susbstantial changes in the results of the experiment.

Type 1 and 2 error

type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is the failure to reject a false null hypothesis (a "false negative").

behavioral measure

when observation of behaviors in a subject is recorded by researchers. This can occur in a field or a lab setting. A problem with this type of measure is that you have to train coders, which are the researchers that are counting the behavior.

physiological measure

which is collecting measurements of body responses n(like heart rate and breathing rate) during varying conditions. -polygraph