©2005, Pearson Education/Prentice Hall CHAPTER 4 Data: Measurement and Analysis

©2005, Pearson Education/Prentice Hall What to Measure? The types of things you can measure in psychology are endless. Deciding on what and how often to measure can sometimes be a confusing matter. One choice you have to make is whether to measure overt behavior or a covert process.

©2005, Pearson Education/Prentice Hall Overt Behavior Overt behaviors are those that can be viewed or directly assessed. There are 2 types of overt behavior: –Verbal (use of language, e.g., words) –Motor (body movement, e.g., running speed). Common measures associated with overt behavior include: –Performance speed –Trials –Reaction time

©2005, Pearson Education/Prentice Hall Covert Behavior Covert behaviors are those that are not directly observable. –Some examples include feelings, current physical states, and attitudes. –If we can’t directly observe covert behavior then how do we measure it? We can measure covert behaviors directly with machines (e.g., heart rate monitor) or indirectly (e.g., self-reports).

©2005, Pearson Education/Prentice Hall Direct and Indirect Measures of Covert Behavior Direct techniques can include: –EEG (cortical brain activity) –GSR (skin conductivity) –PET and MRI (brain imaging). Indirect techniques can include: –Surveys –Questionnaires

©2005, Pearson Education/Prentice Hall Surveys and Questionnaires Surveys and questionnaires are important methods psychologists use to gather information about covert behavior. They generally include some of the following characteristics: –Closed-ended items (participants are restricted to a set of fixed responses, e.g., true/false, multiple choice questions.) –Open-ended items (participants are free from response restriction, e.g., What do you think about serial killers?).

©2005, Pearson Education/Prentice Hall Closed-ended items are generally superior to open-ended items because they are much easier to score (remember this when you do your Honors Thesis). There are many useful ways to present close- ended items to participants each with their own set of pros and cons: –Point scales –Likert scales –Segmented rating scales –Numerical rating scales Types of Closed-Ended Items

©2005, Pearson Education/Prentice Hall Before you start, check the thousands of published psychological survey and tests to see if one already exists on your topic. –If not, then you need to create one. The following steps will help you: Composing a Survey

©2005, Pearson Education/Prentice Hall Composing a Survey 1.Describe, in detail, your measure and refer to this description often. 2.Decide on who you will measure. Why? 3.Design items to gather demographic data. 4.Design numerous items only on the topic you are interested in – do not try to measure everything. 5.Rework your items – e.g., make them as simple and straightforward as possible. Avoid jargon, technical terms, negative wording, and double- barreled items. 6.If using closed-ended items, be sure your response sets cover the complete range of possible answers in equal increments. Why? And be sure you response sets match the question. Why? 7.Decide on the statistics you are likely to use. 8.Pretest the survey on a group of people who will not be in your actual study. Tell them you want constructive criticism. 9.Revise the test and make it look professional. 10.Now the fun part begins – giving the survey and deciding if your survey is reliable and valid. This comes in later chapters.

©2005, Pearson Education/Prentice Hall Other Types of Data: Remnant Remnant data refers to the use of existing remains, products, or evidence of behavior to infer or explain past events. Some examples include: –Physical Traces E.g., Graffiti, garbage. –Archival Data E.g., birth weights and rates, weather reports and crime.

©2005, Pearson Education/Prentice Hall How to Identify Pseudoscience? The “Trappings” of Science –Pseudoscience tries to be like real science. –Pseudoscience makes predictions about phenomenon, but rarely tests them. Data is Often Based on Testimonials –Data like this can be easily manipulated. Evasion of Disproof –Explanations given by the pseudoscience to account for data that disprove the pseudoscience are difficult/impossible to test (e.g., the phenomenon can’t be measure by conventional means)

©2005, Pearson Education/Prentice Hall Naturally Occurring Behavior Sometimes psychologists want to study ongoing or naturally occurring behavior. This is unique because researchers not only have to be concerned about what they are observing, but also when and where the behavioral observations will be made. To deal with this, psychologists often employ: –Time sampling technique Taking samples of behavior only at certain times. –Event or Situation sampling technique Taking samples only in a predetermined situation.

©2005, Pearson Education/Prentice Hall Types of Data The are two main categories of data: 1.Quantitative Is numerical or can easily be converted to numerical form. 2. Qualitative Usually narrative in nature and difficult if not impossible to convert to numerical.

©2005, Pearson Education/Prentice Hall Scales of Measurement Once you have determined the type of data you will generate, you now need to coordinate this with statistics procedures. The first step in doing this is to determine your data’s “scale of measurement”. –There are 4 Different Scales of Measurement 1.Nominal 2.Ordinal 3.Interval 4.Ratio Let’s consider each one.

©2005, Pearson Education/Prentice Hall Nominal Scale Most basic level of measurement. Numbers represent simple qualitative differences in your variables. –E.g., 1 = group_1; 2 = group_2, etc. Numbers are not intended for numerical calculations, but to classify data. General rule: Similar objects or events are assigned similar numbers, and different objects get different numbers. Statistical procedures: –Frequency counts; Chi-Square.

©2005, Pearson Education/Prentice Hall Ordinal Scales Numbers in ordinal scales describe the object or event as they did in nominal scales, but they also assign magnitude in the form of rank or order. Ordinal scales indicate an individual’s or object’s value based on its relationship to others in the group. Thus, the numbers have meaning only within the group. Ordinal scales provide no information about how closely two individuals or objects are related. Thus, you cannot add, subtract, multiple, or divide numbers in an ordinal scale. –You can transform ordinal numbers as long as the original information about rank is preserved. E.g., Olympic medals, rank of professors. Appropriate statistics: percentiles, correlation, Mann-Whitney U.

©2005, Pearson Education/Prentice Hall Interval Scale Numbers are assigned with the assumption that each number represents a point that is an equal distance from the points adjacent to it. An interval scale is thus very much like the number system you are familiar with. Interval scale also have all the properties noted in nominal and ordinal scales. Interval scales do not have true zeros, thus you cannot make ratio comparisons. E.g., Fahrenheit temperature scale, many Likert scales. Statistical procedures: mean, median, mode, standard deviation, correlation, t-test, ANOVA.

©2005, Pearson Education/Prentice Hall Ratio Scale All the properties of the preceding scales, plus a true zero. E.g., Kelvin temperature scale, bathroom scale. Since true zeros are present ratio comparisons can be made (90 kg is exactly 3 times more than 30 kg). Appropriate statistics: mean, median, mode, standard deviation, correlation, ratios, t-tests, ANOVA.

©2005, Pearson Education/Prentice Hall Descriptive Statistics Descriptive stats provide information about the central tendencies of a group of data. Importance terms include: –Mean: the arithmetic average in a data set –Median: the middlemost score in a data set –Mode: the most frequent score in a data set –Variance: the degree to which scores in a data set deviate from the mean. There are various ways to measure variance or variability. Range: measure of variability in a data set Standard Deviation: most commonly used measure of variability.

©2005, Pearson Education/Prentice Hall Inferential Statistics Inferential statistics are mathematical / statistical procedures for determining the probability that the relationships or differences we observe in our data actually occur in the population. Inferential statistics also tell us whether the differences we see in our data occurred by chance or not. There are 2 types of inferential statistics: –Parametric statistics –Nonparametric statistics

©2005, Pearson Education/Prentice Hall Parametric and Nonparametric Statistics Parametric Statistics test hypotheses that are based on data that allow us to estimate parameters (e.g., means and standard deviations). –In other words, parametric statistics are used with interval or ratio data. –E.g. Pearson r, multiple regression, t-test, ANOVA Nonparametric Statistics test hypotheses that do not involve parameters (e.g., when the data are nominal or ordinal, or not normally distributed). –E.g., Spearman rank correlation, Chi-square.

©2005, Pearson Education/Prentice Hall Statistical Significance What do we mean when we say something is statistically significant? –We are simply saying that there is only a small probability that what we found was due solely to chance. –That small “chance” probability goes by various names: Type I Error Alpha level And is often symbolized by an italicized p “p” The alpha level is predetermine before the study begins and in psychology the level is usual set to 5% or –This means that if the results we gather cannot be obtained by chance more that 5 times in 100 random trials we would say that our results are statistically significant. It is unlikely they occurred solely by change. So can we ever be wrong?