Lab 11: Results Lab 11: Overview  Lab 10 Review  Lab 11 Results Section Overview  Pass back papers/Pre-Labs Lab 10: Method  Participants  Make sure you include the total number of participants  Categorical Descriptives : Needed to include both the frequency (#)and the valid percent (%) after each group (e.g., gender) → # goes in text, % goes in parentheses  Continuous Descriptives: Include units when you are talking about means (e.g., 30 years old)  Needed to describe 4 demographic descriptives  Space before and after equal sign: (SD_=_1.29) Method  Don’t use M or N within the text → these go in parentheses  YES → The average age was relatively young (M = 20.01, SD = 2.36)  YES → The average age was 20.01 years old (SD = 2.36)  WRONG → The average age was M = 20.01 (SD=2.36)  YES → The sample size was large (N = 150)  YES → There were 150 total participants  WRONG →There were N = 150 participants Method   Materials  Describe DV scale: # items, sample item, response options, and alpha (with correct citation)  Describe remainder of items (opinion and demographic): # items, sample item, response options Procedure  Needed to say Qualtrics, how long you had it active for, how you recruited participants, and something that indicated you created the survey you were sending out. Confusions  Feedback: Confusion with reverse coding  Purpose of reverse coding: To avoid repetitive responses and to “switch things up” scales sometimes use both positively and negatively worded statements  However, when we do data analysis, we want to ensure that higher scores indicate higher levels of the DV  I am happy with my appearance → more agreement = higher self-esteem  I am self-conscious about how I look → more agreement = lower self-esteem  We want all our statements to indicate higher levels of the DV, if we reverse code the second statement, it now corresponds to higher selfesteem  More agreement becomes less agreement and we now have consistent codes where all statements indicate higher self-esteem General  I try to go through lectures relatively fast to give students time to work on assignments, will try to slow down  Raise hand if you have questions during lecture  Happy to answer questions after lecture  Data analysis and APA style take practice, it’s ok if you don’t understand it right away ☺  Use your resources and ask questions Lab 12: Results Results - Big Picture  This is the section where you report your statistics and let the reader know whether your hypotheses were supported or not.  The primary purpose is to report your statistics  No big picture interpretation – that goes in your discussion  State what the numbers mean IN WORDS! Example: Finding → “There was significantly more rainfall in San Diego in 2017 than in 2016.” What is means → “The drought in San Diego may be coming to an end” Lab 11: Results  Similar to the previous results section but…  You’re running two analyses  Essentially you’re doing two of everything  One heading, Results, BOLD and CENTERED.  Outline:  Hypotheses  Group Difference and Correlational Results  Hypotheses supported/not supported  There are detailed instructions for all of the SPSS related steps in your lab manual and on the following slides  Follow along step by step with your lab manual and the PowerPoint slides Before you run Analyses  Make sure you are using the ”cleaned up” SPSS data with recoded DV scale (if applicable) – whoever saved last week should send it to their group members if it was done as a group  IMPORTANT: Check to make sure the Variable ‘Type’ and ‘Measure’ are set appropriately   Go to ‘Variable view’  Check the ‘Type’ column for the the items you are interested in: They should be set to ‘Numeric’  IMPORTANT: Check the ‘Measure’ column for the items you are interested in:  A Group item (categorical) should be set to ‘Nominal’  A Continuous item should be set to ‘Scale’ Create an overall score for your DV scale (this is NOT the same as alpha)  Take note of how to combine your scale items together. This is located on the scales you were given on blackboard  Can find this info under “scoring”  We will need to create a new variable Making an Overall DV Score Making an Overall DV Score REMEMBER: If you had to reverse code items last week, you must use those again when creating your overall DV score!!! The total number of items being summed should equal the total number in your DV scale Making an Overall DV Score Making an Overall DV Score  You will use your newly created overall DV score to test your hypotheses  Do NOT use any individual items from the scale Recoding Data Recoding Data  Some of you will need to recode your data.  Recode before testing your hypotheses otherwise your results will be incorrect  If you’re doing an age split, ethnicity group split, or if one of us has told you that you have to please pay attention.  Examples:  Group 1: Ages 29 and below; Group 2: Ages 30 and above  Group 1: Whites; Group 2: Non-Whites  Group 1: Underclassmen; Group 2: Upperclassmen Recoding Your Data Recoding Your Data Recoding Your Data Recoding Your Data Recoding Data Recoding Data All ethnicities that are not white get recoded into one number Hypotheses Hypotheses  Start by restating your hypotheses  These can be exactly the same as you’ve been using, unless we made changes to your last lab.  It was hypothesized that… and…It was also hypothesized that…  Remember your outcome variable (DV), should be the overall DV score you just made. This stays the same from your group difference and correlational hypotheses.  E.g. Self-Esteem, Life Satisfaction, etc. Group Difference Hypothesis TWO GROUPS → T-TEST Group Difference Hypothesis: Two Groups If you are comparing two groups only:  For this hypothesis you’re testing whether or not there is a difference in your DV (outcome) between levels of your two groups   A significant finding indicates that there is a significant difference between the levels of your two groups on your DV.   E.g. Life satisfaction by gender. This doesn’t tell you direction, only whether or not there is a difference. Look at the group means for directionality. A non-significant finding indicates that there is NO difference between the levels of your two groups on your DV.  The responses to the DV by each group in the IV are statistically the same. Interpreting Output and Reporting Statistics Reporting Statistics   Important: need space before and after equal sign: (M = 2.45, SD = 1.10),  YES (M = 2.45, SD = 1.10),  NO (M=2.45, SD=1.10), Group Difference:  Males reported significantly higher life satisfaction(M = 4.57, SD = 0.34) than females (M = 2.45, SD = 1.10), t(156) = 12.33, p = .037.  Males did not report more or less life satisfaction than females, t(156) = 1.33, p = .875.  Look at your lab manual. It will show you exactly where the numbers are coming from.  If test is nonsignificant, you DO NOT report means, standard deviations, or reference chart BUT still need to report test statistic Group Difference Hypothesis THREE OR FOUR GROUPS → ANOVA Group Difference Hypothesis: three or four  For this hypothesis you’re testing whether or not there is a difference in your DV (outcome) between levels of your 3 or 4 groups  You will either run a t-test (2 groups) or ANOVA(3-4 groups) depending on your hypothesis, but not both   A significant finding indicates that there is a significant difference between at least one of the levels of your 3 or 4 groups on your DV.   E.g. Life satisfaction by major (business, psychology, nursing). This doesn’t tell you direction, only whether or not there is a difference. A non-significant finding indicates that there is NO difference between the levels of your 3 or 4 groups on your DV.  The responses to the DV by each group in the IV are statistically the same. Interpreting Output and Reporting Statistics Reporting Statistics   Group Difference (3 or 4 groups):  Business majors, psychology majors, and nursing majors differ in their levels of job satisfaction, F(2, 128) = 12.33, p = .037.  If directional: Business majors, psychology majors, and nursing majors differ in their levels of job satisfaction, F(2, 128) = 12.33, p = .037. Psychology majors reported higher levels of job satisfaction (M = 9.15, SD = 3.90) compared to business majors (M = 4.15, SD = 2.60) and nursing majors (M = 6.20, SD = 3.21) .  Reference bar chart if significant (...as seen in Figure 1)  Business majors, psychology majors, and nursing majors do not differ in their levels of job satisfaction, F(2, 128) = 1.10, p = .337. Look at your lab manual. It will show you exactly where the numbers are coming from. Correlational Hypothesis Correlational Hypothesis  For this hypothesis you’re testing whether there is a relationship between your correlational variable and your DV (outcome)   A significant finding indicates that there is a correlation between your correlational variable and your DV.   Exercise and Depression Once again this doesn’t tell you direction, only that the two variables are correlated. Look at the sign (+ or -) of the r value to tell whether it’s a positive or negative correlation (see lab manual) A non-significant finding indicates that there is NO relationship (no correlation) between your variables.  The response on one variables does not relate to the responses on the other variable (they’re independent). Interpreting Output and Reporting Statistics Reporting Statistics  Correlation  Life satisfaction was negatively correlated with depression. Participants who reported lower levels of agreement with the statement “I feel depressed on a regular basis” reported significantly higher levels of life satisfaction, r = -.84, p = .041.  Life satisfaction was negatively correlated with depression. Participants who reported higher life satisfaction reported significantly lower levels of depression, r = -.40, p < .001.  There was no significant relationship found between life satisfaction and depression, r = -.13, p = .276.  Be very careful not to mix up p and r values  Lab manual Figures ONLY INCLUDE IF RESULTS ARE SIGNIFICANT Figures  Figures are only required for significant results (p < .05)  You need to include:   Bar graph for group difference hypothesis  Scatterplot for correlational hypothesis. Remember to add a caption line to your graph.  See Writing with Style for correct figure caption format.  Make sure you include a reference to your figure in the results section. (See Figure 1)  So lets walk through what the bar graph looks like... Graph Your Results: Bar Graph Your Results: Bar Chart Graph Your Results: Bar Chart Output Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Lab Tips  Use your lab manual and follow directions on this powerpoint! They are there to help you.  Remember to italicize your statistics (letters, r, t, M, SD)  Be really careful on your formatting of the statistics (SPACING)  Make sure you have your figures, captions, and in text references in the right format.  Be aware; sometimes a result will be significant BUT IN THE OPPOSITE DIRECTION of your hypothesis.   Check this! Your hypothesis will not be supported, but you still need to report significant results Attach your SPSS output to the back to double check your numbers.  Copy Special -> Paste Special or Paste Picture or Screenshot Formatting Tips   This is the format that’s easiest to read:  Hypothesis 1 → Stats 1 → Supported or Not  Hypothesis 2 → Stats 2 → Supported or Not  You don’t need any other headings besides Results, bold centered. Don’t forget about formatting, paragraph spacing, putting spaces before and after the = signs, no one sentence paragraphs. You’ve got this What to turn in: Results: - State Hypotheses (group difference and correlational) - Statistical Results - Supported/Not supported Figures with captions (for significant results) ******Printed Output (for all analyses – cannot grade without this!!!)****** Suggestion: Run all analyses first, then write up results 
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