You are required to complete three (and 4t for extra credit) SPSS Lab projects for this course. Detailed instructions are provided for each:

Note: For each of the three SPSS lab projects you must: 

• Design and conduct a simulated research project that tests a hypothesis(es) derived from a psychological theory
• Each lab Project is limited to 3 single spaced pages (about 1500 words)
  (not including figures and tables and a reference page).
• Include all APA sections (Abstract, Introduction, Method/Procedures, Results, Discussion, References)
•  Abstract must be less than 100 words.   
• Results must indicate for hypothesized significant relationships, p levels between <.05 and > .001)
   NOTE ********
• Use Academic Style tables when you import tables and figures into a word processing document. 
• Include the exact probability of statistics tests in tables.
• Use APA style for numbering and titles for tables and figures,
• Use italics for statistical symbols.
• Provide a narrative explanation of the findings in the Discussion section, and discuss limitations.
• DO NOT  include a printout of the actual data from SPSS.
• NOTE: USE SINGLE SPACE (3 pages) FOR SUBMISSION OF REPORTS TO Sakai!!!!

Lab #1 Hypothetical Relationships

Lab #2 Multivariate Regression

Lab #3 One-way ANOVA  

 Lab #4 Two-way ANOVA  (optional for extra credit)

Purpose: To demonstrate the strength (magnitude) and direction (positive, negative) of the relationships among variables relevant to a psychological theory.

Instructions:

1. Use an existing psychological theory that explains the relationships between four CONTINUOUS variables (i.e. interval or ratio scale level) that are relevant to your theoretical framework and that you plan to use in your projects. At least one should be an outcome variable related to your hypothesis (Use A for this one, which will be your primary Dependent variable): Name them:  A, B, C, and D and provide Variable and Value Labels for each.
2.  Make sure the data for the four variables have a wide enough range of about 1 to 50 (for variables you create) and are normally distributed. This means they should NOT exceed the parameters for kurtosis and skewness. If you use an existing measure or construct, use that scale.
3. Create a data set that will support the first two hypotheses for significant positive and negative relationships

     Less than p < .05 but greater than p > .001:

Results must indicate for hypothesized significant relationships, p levels between <.05 and > .001)

H1. a positive significant relationship between A and B (p <.05 but greater than p> .001)
H2. a negative significant relationship between A and C (p <.05 but greater than p> .001)
H3. a non-significant relationship (near zero) between A and D. (p > .05)

 -> SPSS procedures:

1. Create and input data to an SPSS file for data analysis N = 30 (four observations A,B,C,D for 30 subjects).
2. Perform statistical analyses to show descriptives for all four variables, and the correlations among them (see below):
3. Be sure to include a narrative explanation of your results indicating how they fit (or follow from) your theory.

• Table 1: Report Descriptives for all four vars:  (SPSS: Analyze>Descriptive statistics> Descriptives for all four variables 
• Table 2: Report Pearson Correlations among all four variables (SPSS: Analyze>Correlate>Bivariate)
• Figures 1-3: Plot the variables for three hypotheses (SPSS:  Graphs>Legacy>Scatterplot/Dot; choose "simple").
  
      Figure 1 (A&B), define Y axis as A, X as B;
      Figure 2 (A&C, define  Y axis as A, X axis as C;
      Figure 3 (A&D), define Y axis as A, X axis as D.

Import your tables and figure (graph) into Word document using Academic Style tables ("table looks").

Note : I recommend that you delete the title in the SPSS table and place the table number and title outside the imported SPSS table.

Explain how the four variables are related with regard to the direction and strength. If the hypothesized relationship is counterintuitive, be sure to explain why.

Example:

VARIABLE:   VARIABLE LABELS
 A                     Productivity   (outcome or DV of interest)
 B                     Job satisfaction
 C                    Annual days late (to work)
 D                    Extraversion (personality)

Purpose: To demonstrate how each of three continuous predictor variables explain variance in a criterion or outcome variable, (i.e. Dependent Variable)

Instructions:

(1) Using YOUR proposal topic and the four variables from Lab 1 (or you many change the names if you like), hypothesize in the introduction how each of the three variables will contribute (account for variance) in your outcome, (or Dependent variable).

 (2) Analyze your data set to determine how much each of the three predictors will or will not account for variance in the multiple regression
      ->SPSS procedures:

(1) SPSS: Analyze>Regression>Linear (statistics: check Descriptives, -in addition to Estimates, Model fit)

(2) Import SPSS tables into Word document (APA styles) Results section:

Report: Descriptives (for all four variables) (use as your Table 1)
Report: Model Summary (use as your Table 2)
Report: Coefficients (use as your Table 3)

(3) In your discussion, explain the results in the context of your theory.

Example:

VARIABLE:   VARIABLE LABELS
 A                     Productivity   (outcome or DV of interest)
 B                     Job satisfaction
 C                    Annual days late (to work)
 D                    Extraversion (personality)

Lab #3  One way analysis of variance ANOVA

Purpose:  To demonstrate the effects of how a true Independent Variable may affect scores on a  Dependent Variable.

Instructions: Using your proposal topic and the four variable names you created for the Lab #1 assignment:

(1) Create a new Categorical (nominal) variable that will serve as a TRUE  independent variable (not a participant, i.e. subject variable). Make sure it is one that follows from your theoretical framework.

This variable must have at least two levels (such as High and Low) (e.g. attractive v. unattractive stimulus person).  Name the variable IV (for Independent Variable) and give it  meaningful  Variable label such as "Attractiveness level of stimulus person" and Value labels such as: 1 = High attractiveness; 2= low attractiveness. If your A variale for lab 1 was your primary variable of interest, i.e. outcome variable within your theoretical framework, you may want to use this as your DV in this lab 3.

Now add the new IV values by "assigning" participants to groups (conditions) by entering the new values (1, 2, For n =15 in each group condition/level).

(2) Then "assign" subjects/participants to groups (IV) in such a way to insure that the two group means will differ significantly on a dependent variable such as one of the variables you created as an outcome, most likely variable A, your primary DV in Lab1 (why not D?).     

NOTE: You must manipulte your data values to ensure that results will support the hypothesis for your primary DV showing that IV groups differ in the predicted direction.
The  probability for rejection must be between p <.05 and >.001.

NOTE: Don't worry about the other predictors. You may ignore them in your analysis.

(3) Provide a short narrative explanation of your results, including how they fit your theory. Be sure to provide labels for variables and values that explain the nature of the variables.

(4) Import your tables and figures in Academic Style (SPSS) into your document report, making sure they conform to APA style for reporting results.

  -> SPSS procedures:

1. Report in narrative form, (SPSS: Analysis: >Frequencies  VARS=IV (n in each condition)
2. Report in Table 1: Descriptives:  (SPSS >Analysis>Descriptives VARS=A B C D; Options: min, max (in addition to means and SD)
3. Report in Table 2: (SPSS: >Anova: >Analysis> Compare means> ONEWAY Anova (DVs = A B C D)    (IV = 1,2)
    (Options: Descriptives) 

(5) In the Discussion, discuss how your hypothesis (es) are supported with the results and how your theory is supported by your results.

Example: Lab 3 One way ANOVA  

VARIABLE:   VARIABLE LABELS
 A                     Productivity
 B                     Job satisfaction
 C                    Annual days late
 D                    Extraversion
 IV                  Autonomy level needed (high v. low)

*** Lab #4 Two-way analysis of variance ***  OPTIONAL for extra credit

Purpose: To demonstrate how two independent variables (factors) interact to produce outcomes different from what would have occurred if either was tested alone. In this lab, as in Lab #2, one of the factors must be a true IV, but the other may be a truly categorical,  non-manipulated Subject Variable, (e.g. gender, race, educational status).

Instructions:  Using your proposal topic, and the four variables and the IV you created for the Lab #2 assignment,

(1) Create a second IV (or two more new independent variables) that will serve as a second factor (FAC2) in a factorial design that 
 that will produce a significant two-way interaction. Each factor should have at least two levels or conditions (e.g. low and high, yes or no).

The new variables should be called FAC2 (for Independent var 2) and if the original IV1 is used it should be relabeled as FAC1. Each should be given Variable labels and Value labels.

You must develop an explanation (or use an existing one) which would logically predict how the variables would operate together to produce the interaction. The example I have used suggests that FAC1 (Autonomy: Low and High need) will interact with FAC2 (Surveillance: Low and High need) to produce effective performance. It is hypothesized that workers with a low need for autonomy will perform better under High Surveillance while those who have a high need for autonomy will perform better when they are not supervised closely (Low Surveillance). 

(2) You must "assign" subjects to groups in such a way to insure that a significant INTERACTION occurs. You don't have to produce a CROSSED interaction but it will be very impressive if you do!

NOTE: You must create data that will support the hypothesis for your crossed interaction on your primary DV  with a probability for rejection between p <.05 and > .001.

(3) You must provide a rationale for the predicted interaction outcome (FAC1 or FAC2). Interaction must be significant for your primary DV (e.g. A) but not necessarily for  others (B and C, etc). What significant differences, if any, would you expect to find for the other DVs? Why?

 Hint: think about the relationships you established among the four variables in Lab 1.

NOTE: If you are using a student version of SPSS, you will not have Multivariate under Analyze/General Linear Model. Therefore, you can run only one DV at a time using Analyze/General Linear Model/Univariate.
Note: Using SPSS in the labs, you can run all DVs under Multivariate procedures.

Hint: it will help to set up a 2 X 2 table for the four cells and plug in "dummy" or hypothetical means in each cell to determine how they will have to vary. It will also help to sort your primary dependent variable before you attempt to assign subjects to conditions.

You can do this by including the SPSS procedure: Data/Sort Cases...Sort BY....

(3) Now add the new variable(s) in the data list and "assign" subjects to groups by entering the new values (1, or 2, for each subject). If you have 30 subjects, you will have to assign 7 to two cells and 8 to the other two cells. Hint: you may find it easier to use the "view value labels" option under View while in Data View (bottom tab) (not in Variable View).

 -> SPSS procedures:

(4) Describe and explain how all four means fit (or follow from) your theory or reasoning.

Lab 4 data definition

VARIABLE  LABELS:
 FAC1           Autonomy
 FAC2           Surveillance

VALUE LABELS:
Fac1                1 Lo need auto    2  High need auto
Fac2                1 Lo need surveil 2  High need surveil

Data analysis for Lab 4 Two way anova (easy as 1,2,3)

Steps:

#1. Create data: After defining your data variables and labels, in data view, choose View [value labels]. This will make it easier to work with changing subject values to fit your hypotheses.

To calculate how your “assigned” Ss are distributed to conditions (cells):

Analyze/Descriptives/Crosstabs

a. Fac1 -> Row
b. Fac2 -> Column
 
#2. Test your hypotheses: To test your hypothesis (no significant main effects, but a 2-way interaction), use a Two-way ANOVA:

Analyze/General Linear Model/Multivariate (for SPSS student version, use /Univariate  and run each DV separately)

a. DVs -> Dependent variable(s)
b. Fac 1 -> Fixed Factor
c. Fac 2-> Fixed Factor

    Choose under: Options
d. Descriptive statistics 
e. Estimates of effect size 
    (move each fac plus the interaction fac1*fac2 over to the "display means for")
f.. Plots: fac1 -> Horizontal; fac 2 -> separate [add]

 #3. Analyze data:

Inspect Cross tab results to ensure that  Ss are (almost) equally distributed into four cells, i.e. 7 or 8 in each.
Inspect  Univariate ANOVAs  to make sure:
1. There are no main effects for either fac1 or fac2
2. There is a significant interaction (probability of rejection between  p < 05 but not less than .001).
3. The means are in the predicted direction.

Explain results in the narrative.

Note: (Important!)  If results are counterintuitive, you must be careful to explain how the theory predicts the findings. 

Import your Academic tables and figures with numbers and labels into your word processing document.