# Prepared by Sarah Perry Johnson

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Prepared by Sarah Perry Johnson
Sociology 400 Review Prepared by Sarah Perry Johnson

Review of Levels of Measurement
The 4 Levels of Measurement are: Nominal Ordinal Interval Ratio The subsequent slides will allow us to revisit each level of measurement and review the characteristics of each variable.

Discreet Variables

Nominal Variable Nominal (nom=name)
Qualitative No numerical value Discreet Examples: religion, nationality, occupation, etc. Nominal variables are also considered to be categorical.

Ordinal Variables Ordinal (order) Quantitative
In order of cases greater than or less than; a range Discreet Examples: level of conflict (low to high), socioeconomic status (SES), Starbucks sizes, educational attainment, etc.

Continuous Variables

Interval Variables Interval Quantitative
Numerical/integer value; no absolute/fixed “0” point Continuous Example: temperature, depression score (starts at 6, ends at 60)

Ratio Variables Ratio Quantitative
Numerical/integer value; has an absolute fixed “zero” point Continuous Examples: number of children, number of cigarettes smoked, number of feet/miles, number of votes

Relationship and Differences Questions

Basic Template for Relationships Questions
Is there a relationship between [the IV] and [the DV]? Example: Is there a relationship between the number of hours a person studies per day and GPA? Independent Variable: # of daily study hours Dependent Variable: Grade Point Average (GPA) Used for Pearson, Spearman, and Regression Questions

Basic Template for Differences Questions
Is there a difference between [categories of the IV] based on [the DV]? Is there a difference between the United States, China, and Sweden based on GDP? Independent Variable: countries (US, China, and Sweden) Dependent Variable: Gross Domestic Product (GDP score) Used for Chi-Square, T-Test, and ANOVA Questions

Six Types of Tests: Charts and Templates

Six types of Tests: Variables needed
Test Name IV DV Symbol Chi-Square Discreet χ² T-Test Continuous t ANOVA Discreet (2+) F Pearson Correlation r Spearman Correlation Ordinal rs Regression --

Chi-Square Test: Variables and Reporting
Independent Variable: Discreet Dependent Variable: Discreet Report template Is there is a difference between [categories of the IV] based on the [DV]? There is a difference between [categories of the IV] based on the [DV] (chi-square[χ²=?], p-value[p=?]).

t-Test: Variables and Reporting
Independent Variable: Discreet (limit: 2 categories) Dependent Variable: Continuous Report template Is there is a difference between [categories of the IV] based on the [DV]? There is a difference between [categories of the IV] based on the [DV] (t-score[t=?], p-value[p=?]). State which category of the IV has more...[Cat 1 has around __% more than Cat 2.]

ANOVA: Variables and Reporting
Independent Variable: Discreet (2 or more categories) Dependent Variable: Continuous Report Template Is there is a difference between [2+ categories of the IV] based on the [DV]? There is a difference between [2+ categories of the IV] based on the [DV] (F-score[F=?], p-value[p=?]). State which category of the IV has the most and the least [Cat 1 has the most and Cat 2 has the least. There is no significant difference between Cat 3, however, the difference between Cat 1 and Cat 2 were significant.]

Pearson Correlation: Variables and Reporting
Independent Variable: Continuous Dependent Variable: Continuous Report Template Is there is a relationship between [the IV] and [the DV]? There is a relationship between [the IV] and [the DV] (r-score[r=?], p-value [p=?]). State the strength and direction: This is a [state strength], [state direction] relationship. State the R² (square the r value): [The IV] explains about __% of the variance in [the DV].

Spearman Correlation: Variables and Reporting
Independent Variable: Ordinal Dependent Variable: Ordinal Report Template Is there a relationship between [the IV] and [the DV]? There is a relationship between [the IV] and [the DV] (rs-score[ rs=?], p-value [p=?]). State the strength and direction: This is a [state strength], [state direction] relationship. No R-square: ordinal variables

Regression: Variables and Reporting
Independent Variable: Continuous Dependent Variable: Continuous

Regression: Variables and Reporting (cont.)
Is there a relationship between [the IV] and [the DV]? There is a relationship between [the IV] and [the DV] (p-value[p=?]). State the strength and direction: This is a [state strength], [state direction] relationship.

Regression: Variables and Reporting (cont.)
State the slope: For each additional [1 unit of the IV] of the [units of analysis], the [DV] is expected to [increase/decrease] by [# units of the DV]. State the R² (square the r value): [The IV] explains about __% of the variance in [the DV]. State the y-intercept: In the case that [the IV] is 0, it is predicted that [the DV] will be [amount and units of measurement]. Or: A [unit of analysis] that is [units of measurement of IV] is predicted to be [amount and units of measurement of DV]

Multiple Regression: Variables and Reporting
Independent Variable: Continuous (2+) Dependent Variable: Continuous Report Template: Is there a relationship between [IV#1], [IV#2], and [IV#3] with [the DV]? State the Adjusted R2: [IV#1], [IV#2], and [IV#3] together explain about [___%] of the variance in [the DV].

Multiple Regression: Variables and Reporting (cont.)
Report on the Adjusted R²: [IV#1, IV#2, and IV#3] together explain about __% of the variance in [the DV]. Next, state the slope for each IV with the DV. Hint: if you have three IV’s, you should write three separate statements: IV#1/DV, IV#2/DV, and IV#3/DV. (see next slide…)

Multiple Regression: Variables and Reporting (cont.)
For each additional [1 unit of the IV#1] of the [units of analysis], the [DV] is expected to [increase/decrease] by [# units of the DV], holding constant for [IV#2] and [IV#3] (p=). For each additional [1 unit of the IV#2] of the [units of analysis], the [DV] is expected to [increase/decrease] by [# units of the DV], holding constant for [IV#1] and [IV#3] (p=). For each additional [1 unit of the IV#3] of the [units of analysis], the [DV] is expected to [increase/decrease] by [# units of the DV], holding constant for [IV#1] and [IV#2] (p=).

Multiple Regression: Variables and Reporting (cont.)
Each time you report on a slope, add to the end of the statement “holding constant for” and list the leftover variables. For example: For each additional [1 unit of the IV#1] of the [units of analysis], the [DV] is expected to [increase/decrease] by [# units of the DV], holding constant for [IV#2 and IV#3] (p=). For the test, regarding the slope, you will only have to report on one of the IV’s paired with the DV.

Strength and Direction

Strength and Direction of Relationship between Variables
r -Value Strength of Relationship <0.20 Slight or weak, almost negligible relationship <0.04 o.20-o.40 Low correlation, definite but very small relationship Moderate correlation, substantial relationship High or strong correlation, marked relationship >0.90 Very high correlation, very dependable relationship >0.81

Example: Pearson correlation- Strength and Direction
Is there a relationship between self-concept scores and depression scores? To determine whether there is a statistically significant relationship between the variables, look at the p-value. To determine the strength and direction of the relationship, look at the r-value.

Strength and Direction (continued)
Strength of the relationship According to our chart, the integer value shows that there is a very high/strong relationship between self-concept scores and depression scores. Direction of the relationship According to our chart, the negative sign (-) in front of the integer denotes that the relationship is a negative one (as X increases, Y decreases). So how shall we report this data for a Pearson’s Correlation?

Strength and Direction (continued)
Report template for Pearson’s correlation Is there is a relationship between [the IV] and [the DV]? There is a difference between [the IV] and [the DV] (r-score[r=?], p-value [p=?]). State the strength and direction: This is a [state strength], [state direction] relationship. Report: There is a relationship between self-concept scores and depression scores (r=-0.852; p=0.004). This is a very strong, negative relationship.

Example: Spearman’s Rho
According to the above Spearman’s rho chart, rs =0.290. Direction: The integer value is positive/negative. Therefore, the direction of the relationship is positive/negative. Strength: According to the strength chart, the value is____________. Therefore, the strength of the relationship is ____________.

Spearman’s Rho (continued)
Time to report on the value for a Spearman’s rho: There is a relationship between __________ and __________ (rs=_____;p=_____). This is a ________________ relationship.

Most and Least

Most and Least Report on the most and least based on the means report.
Variable to Quantify: Jelly beans Sue: 56 Jenny: 39 Carly: 45 Sue has the most jelly beans and Jenny has the least [amount of jelly beans].

Most and Least (continued)
Let’s evaluate the same type of example from Lab 8. What is the unit of measurement? (Hint-it is contained in the box above the table…) What is the largest value? What is the smallest value?

Most and Least (continued)
Variable to quantify: age Descriptors for least and most in relationship to age Least=Youngest Most=Oldest Perot voters are the youngest and the Clinton voters are the oldest. -Or- People who voted for Perot are the youngest and people who vote for Clinton are the oldest.