Research and Statistics “the best way to attain one’s goals is by focusing on the means, not the end”

Consider This Social Question: Do Women Prefer Men With A More Masculine Or Feminine Face?

How Do We Find A Solution To Our ? DEFINE the variables we’ll measure Formulate the HYPOTHESIS Determine the POPULATION & SAMPLE to be tested Determine the RESEARCH METHOD to use Summarize the DATA Determine appropriate STATISTICAL MEASURE to analyze data Synthesize RESULTS

1. DEFINE the variables Variables are specifically defined according to the needs of the researcher Operational Definition – Specific description of the variables to be measured Helps researchers determine whether their research goals were achieved Masculine Face  Chiseled jaw Scruffy, stubbly hair on face Thick eyebrows Feminine Face  Prominent cheek bones and dimples Plucked eyebrows Long, curly eyelashes

2. Formulate the HYPOTHESIS A hypothesis is an educated guess at a solution to the research question Null Hypothesis (H0) – A hypothesis that can only be accepted as being false Women do not prefer men with a more masculine face Alternate Hypothesis (H1 or Ha) – The opposite of the null hypothesis, in lieu of a competing proposal Women prefer men with a more masculine face

3. Determine the POPULATION & SAMPLE All research is done to describe a certain population, though a sample of the population must be studied because populations can be so large Population All subjects to be studied in a population Populations have Parameters – a numerical measurement describing a population’s characteristic Sample A sub-collection of the population that is being studied Statistics – is a measurement describing some characteristic of a sample

Random Sampling Selection where each member of a specified population has an equal chance of being selected Simple Random Sample – every possible sample of size n has the same chance of being chosen

Systematic Sampling Selection of every Kth element in the population i.e. every 3rd person in the population was chosen

4. Determine the RESEARCH METHOD to use There are many research methods to choose from, each of which have their specific advantages and disadvantages Be Aware of… Researcher Bias – researcher loses objectivity due to personal involvement and skews the results because of their own desired outcome Confirmation Bias – researcher tends to confirm rather than refute actual results in order to confirm existing beliefs

Naturalistic Observation Field Study Behavior in natural environment No manipulation of variables ADVANTAGES  Generalizability DISADVANTAGES  Subject Reactivity Observer Bias No control over variables Confounding variables

Case Study Intense study of one individual over time ADVANTAGES  Detailed, historical Does not require a large sample pool Rare or unusual studies DISADVANTAGES  Limited generalizability Reliability is limited

Survey, Interview, Questionnaire Sample of behaviors or beliefs Standardized set of questions in verbal or written form ADVANTAGES  Quick, easy, cheap DISADVANTAGES  Limited generalizability Honesty is in question Research bias possibility

Longitudinal Study Same subjects are repeatedly tested over a long period of time ADVANTAGES  Stability Good for developmental studies Small sample pool DISADVANTAGES  Too much time and money Lost or deceased subjects – age history confound Tests become familiar over time, less effective

Cross-Sectional Study Different subgroups of a population are compared at the same point in time Subgroups vs. time ADVANTAGES  More time and money efficient Good for developmental studies DISADVANTAGES  Limited generalizability between age groups Cohort Effect – sociocultural context can influence outcomes

Correlational Study Determines relationships between variables by measuring them, NOT manipulating them i.e. Is there a correlation between blood pressure and cholesterol level? Correlation DOES NOT equal causation!!! Correlation Coefficient (r) – shows strength of the relationship on a scale from +1.00 to -1.00 Illusory Correlations – perception of a relationship where none actually exists

Positive Correlation- both variables increase or decrease in the same direction; rho (r) = +1

Negative Correlation- relationship increases or decreases in opposite directions; rho (r) = -1

No Correlation- shows no relationship between variables; r = 0

Experiment Study of cause and effect relationships through the manipulation of variables Cause - the tool used to elicit a response Effect – the elicited or expected response Control Group – the group that is not manipulated; experimental group is compared to Experimental Group – the group which manipulation is performed upon

Independent Variable (IV) – the one variable that is manipulated Experimental variable, manipulated variable Dependent Variable (DV) – suspected outcome after manipulation of the IV Controlled variable, respondent variable Extraneous Variables – potential factors that could likely influence the DV Confounding Variables – a factor other than the IV that does influence the DV

A researcher is interested in how the activity levels of 4-yr olds is affected by viewing an exciting 30min video of Teenage Mutant Ninja Turtles versus a 30min calming video of Barney IV: DV: EG: CG: The therapist wants to test a new drug designed to increase the ability of teenagers with ADHD to take accurate notes in class IV: DV: EG: CG:

A researcher is interested in how the activity levels of 4-yr olds is affected by viewing an exciting 30min video of Teenage Mutant Ninja Turtles versus a 30min calming video of Barney IV: viewing 30min video of TMNT DV: activity level of 4yr olds EG: 4yr olds watching TMNT CG: 4yr olds watching Barney The therapist wants to test a new drug designed to increase the ability of teenagers with ADHD to take accurate notes in class IV: teens taking a drug DV: ability to take accurate notes EG: teens taking the drug CG: teens not taking the drug

The Relationship Between Variables Qualitative  CATEGORIES and WORDS Nominal, ordinal Quantitative  NUMBERS Interval, ratio Reliability  (IS THE TEST CONSISTENT?) Replicable – do the original methods produce the same results over and over again? measures magnitude of the relationship (size) Predicts one variable based on the other Validity  (DOES IT MEASURE WHAT IT IS SUPPOSED TO?) Asks whether the study actually measures what was intended? Measures truthfulness of the study How well does the sample represent the population? Ethical  Standards of the APA – American Psychiatric Association

Psychometrics Reliability  is the test consistent? Split-Half reliability  if a test is split in 2 parts and the scores on each part are consistent with each other, the test is considered reliable because they’re both measuring the same thing

How Do We Measure Variables? NOMINAL LEVEL OFMEASUREMENT Qualitative; names, levels, categories; cannot be ranked Survey responses (i.e. yes, no, undecided) Religious preference, gender, race, colors ORDINAL LEVEL OF MEASUREMENT Qualitative; rank but not how much difference between values A,B,C,D,F; 1st, 2nd, 3rd Good/Bad; Nice/Naughty; Short/Tall Socioeconomic Status INTERVAL LEVEL OF MEASUREMENT Quantitative; rank with difference between values; no absolute zero The year 1000, 2000, 1776, and 1492 Temperature; measurement of inches on a ruler RATIO LEVEL OF MEASUREMENT Quantitative; absolute zero; ratio between values possible Price of college textbooks; distance travelled NOIR levels of measurement

Are the variables shown below NOMINAL, ORDINAL, INTERVAL or RATIO? PRACTICE Are the variables shown below NOMINAL, ORDINAL, INTERVAL or RATIO? Average, above average, below average Nicotine’s content in cigarettes Today’s temperature in Celsius Colors of M&M candies Exam Scores

ANSWERS Average, above average, below average ORDINAL Nicotine’s content in cigarettes RATIO 3. Today’s temperature in Celsius INTERVAL 4. Colors of M&M candies NOMINAL 5. Exam Scores

Field Experiment – experiment conducted outside of the laboratory Quasi-Experiment- a controlled experimental design that does not ensure random assignment of the participants to the experimental and control groups Double-Blind Experiment – study during which neither the participants or researchers know which group any subject belongs; allows experimenters to check actual effects Placebo Effect – experimental results caused by expectations alone

5. Summarize the DATA Histogram Frequency Polygon Pie Chart Graphs, charts and tables are ways to summarize the most important aspects of your data pictorially Histogram Frequency Polygon Pie Chart

6. Determine the appropriate STATISTICAL MEASURE Statistical measures help us to process the data we have collected, and summarize it into quick and clear results MEASURES OF CENTRAL TENDENCY MEAN (AVERAGE) The number obtained by adding the values and dividing the total by the number of values MEDIAN The true middle value when the original data values are arranged in order MODE The score that occurs most frequently; denoted by M; can be used with nominal data Bimodal; multimodal; no mode a factor other than the IV that does influence the DV

What is the mean, median, and mode for each number set? 5 5 5 3 1 5 1 4 3 5 1 2 2 2 3 4 5 6 6 6 9 1 2 3 6 7 8 9 10

Standard Deviation Best way to show variability when the distribution of data is normal The square root of the variance… average distance from the mean Variance= information under the radical If a researcher concludes the difference between two variables is statistically significant after conducting a t-test, p =.05 Statistical Significance = confidence that our results are not due to chance alone.

Normal Distribution data is symmetrical; mean=median=mode

Outliers are towards the positive Outliers are towards the negative Skewed Distribution data extends more in the direction of outlying scores; “skewness is fewness” Outliers are towards the positive Outliers are towards the negative

Questions You Can Attempt To Answer in Your Conclusion 7. Synthesize RESULTS Results and conclusion sections are used to summarize, in words, the information found in the data and statistics sections Questions You Can Attempt To Answer in Your Conclusion Was my hypothesis correct? Why or why not? What are some real-life applications to my controlled experiment? What do my graphs and data tables say? What are some other studies that have been done on, or related to, my subject? How generalizable are my results? Was there any experimenter bias? Was this study ethical? Could this study have been improved in any way?