Seminar 2: Correlation Analysis using SPSS

SESSION OBJECTIVES

  • The focus of this session is to:

    • Teach how to complete a correlation in SPSS.

    • Explain how to write up your correlation results.

RUNNING A CORRELATION

  • The data collection will utilize two scales:

    • Honesty-Humility: A subscale derived from the HEXACO questionnaire (Lee & Ashton, 2004).

    • Morality: A subscale from the Temperament and Character Inventory (Cloninger et al., 1994).

  • Formulating an expectation regarding the relationship:

    • Consider whether it will be a Positive or Negative correlation.

    • The proposed hypothesis is:

    • There will be a positive relationship between morality and honesty-humility.

    • Definition of Hypothesis:

    • A hypothesis is a prediction made at the start of a quantitative study, stating expected findings usually based on previous literature.

HONESTY-HUMILITY SCALE

  • Questionnaire items rated on a scale of 1-5:

    • 1 = Strongly Disagree

    • 2 = Disagree

    • 3 = Neutral

    • 4 = Agree

    • 5 = Strongly Agree

  • Sample questions include:

    1. I wouldn't use flattery to get a raise or promotion at work, even if I thought it would succeed.

    2. If I knew that I could never get caught, I would be willing to steal a million dollars.

    3. Having a lot of money is not especially important to me.

    4. I think that I am entitled to more respect than the average person.

    5. If I want something from someone, I will laugh at that person's worst jokes.

    6. I would never accept a bribe, even if it were very large.

    7. I would get a lot of pleasure from owning expensive luxury goods.

    8. I want people to know that I am an important person of high status.

    9. I wouldn’t pretend to like someone just to get that person to do favours for me.

    10. I’d be tempted to use counterfeit money, if I were sure I could get away with it.

MORALITY SCALE

  • Questionnaire items rated on a scale of 1-5:

    • 1 = Strongly Disagree

    • 2 = Disagree

    • 3 = Neutral

    • 4 = Agree

    • 5 = Strongly Agree

  • Sample questions include:

    1. I listen to my conscience.

    2. I try to fool others.

    3. I act according to my conscience.

    4. I believe that the end justifies the means.

    5. I like harmony in my life.

    6. I misuse power.

    7. I return extra change when a cashier makes a mistake.

    8. I do the opposite of what is asked.

    9. I stand behind my actions.

    10. I care about justice.

SPSS OVERVIEW

  • SPSS (Statistical Package for the Social Sciences) can:

    • Analyze data using inferential statistics.

    • Produce a wide range of descriptive statistics, including Mean and Standard Deviation.

  • Importance of input data format:

    • SPSS is stringent in data input.

OPENING SPSS

  • Step-by-step instructions:

    • Open SPSS from the desktop.

    • Create a new data set.

INPUTTING DATA INTO SPSS

VARIABLE VIEW

  • Explanation of view modes:

    • Data View: Displays data.

    • Variable View: Allows managing data properties.

  • Inputs:

    • Naming columns (including participant IDs and response variables).

DATA PARAMETERS IN VARIABLE VIEW

  • Important parameters for each column:

    • Name: Column names to reflect variables.

    • Type: Specifies whether information is numeric/categorical.

    • Decimals: Indicates how many decimal places to display (e.g., 2 for 10.25).

    • Values: Assign numeric codes to written categories (SPSS requires numeric representation).

    • Measure: Essential for statistical analysis; options include:

    • Nominal - Categorical data without meaningful order.

    • Ordinal - Categorical data with meaningful order.

    • Scale - Numerical data.

SCORING THE QUESTIONNAIRES

  • Participants contribute responses under their respective rows.

  • Reverse Coding: Identifies items requiring a reversed scoring approach denoted by ‘R’ in item numbers:

    • Honesty-Humility: 1, 2R, 3, 4R, 5R, 6, 7R, 8R, 9, 10R.

    • Morality: 1, 2R, 3, 4R, 5, 6R, 7, 8R, 9, 10.

  • To score:

    • The score for each subscale is the average of standard and reversed items.

USING SPSS FOR REVERSE SCORING

  • Actions for creating new variables:

    • Select 'Recode into Different Variables' to avoid overwriting original values.

  • Specify the new variables as reversed versions (e.g., HH2R for question 2).

  • Detail on setting original and new value correspondence for reverse scoring:

    • Original 1 becomes new 5, 2 becomes new 4, etc.

COMPUTING AVERAGE SCORES

HONESTY-HUMILITY SCORE

  • Calculate honesty-humility average:

    • Average of questions 1, 3, 6, 9 and reversed questions 2, 4, 5, 7, 8, 10. Formula:
      extAverageHH=rac(Q1+Q3+Q6+Q9+Q2R+Q4R+Q5R+Q7R+Q8R+Q10R)10ext{Average_HH} = rac{(Q1 + Q3 + Q6 + Q9 + Q2R + Q4R + Q5R + Q7R + Q8R + Q10R)}{10}

MORALITY SCORE

  • Calculate morality average:

    • Average of questions 1, 2R, 3, 4R, 5, 6R, 7, 8R, 9, 10. Formula:
      extAverageM=rac(M1+M2R+M3+M4R+M5+M6R+M7+M8R+M9+M10)10ext{Average_M} = rac{(M1 + M2R + M3 + M4R + M5 + M6R + M7 + M8R + M9 + M10)}{10}

TESTING HYPOTHESIS

  • Running the correlation test:

    • Reiterate hypothesis:

    • Expect a positive relationship between morality and honesty-humility.

ASSUMPTIONS FOR CORRELATION

  • Criteria for data suitability:

    • Data must be continuous.

    • The relationship must be linear.

    • No significant outliers should exist.

    • Data should be normally distributed.

    • SPSS can aid in testing these assumptions via manual provided.

PERFORMING CORRELATION IN SPSS

BIVARIATE CORRELATIONS

  • Paths to executing correlation:

    • Navigate via Analyze → Correlate → Bivariate.

    • Choose Pearson for parametric correlation; select one-tailed for directional hypothesis.

GENERATING OUTPUT CORRELATION

  • The output includes:

    • Descriptive statistics (means and standard deviations).

    • Correlation coefficients with significance levels.

  • Example output content:

    • Honesty-Humility Mean: 3.1 (SD: 0.46188)

    • Morality Mean: 2.93 (SD: 0.38312)

    • Correlation coefficient: r = -0.107, indicating a weak negative relationship.

    • Significance value: p = 0.385; indicating no statistical significance as it exceeds .05.

WRITING RESULTS

INTERPRETATION STEPS

  1. Descriptive statistics interpretation:

    • Examine means and determine average satisfaction on scales (both hover around midpoint).

  2. Graph inclusion:

    • Visual representation recommended through Scatter/Dot plots.

    • Follow steps to add a best-fit line and manipulate graphical elements.

  3. Correlation interpretation:

    • Access correlation coefficient, significance levels, and effect size.

    • Final correlation phrase: reports non-significant, weak, negative correlation (e.g., r = -0.107, p = 0.385, R2 = 0.01).

CONCLUDING REMARKS

  • Summarized session objectives, ensuring students grasped the methodology to run correlations in SPSS and articulate the results effectively.