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Rm Flashcards

1
Q

How do you locate a score on a normal distribution?

A

Use the mean as the center and standard deviations to determine distance. Positive z-scores are above the mean, negatives are below.

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2
Q

What assumptions are made for parametric tests?

A

Data is normally distributed.
Homogeneity of variance.
Interval/ratio-level data.
Independence of observations.

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3
Q

What are the two main types of hypotheses?

A

Null Hypothesis (H0): No effect or difference.
Alternative Hypothesis (H1): There is an effect or difference.

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4
Q

What are Type I and Type II errors?

A

Type I Error: Rejecting H0 when it’s true (false positive).
Type II Error: Failing to reject H0 when it’s false (false negative).

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5
Q

What are dependent and independent t-tests?

A

Dependent (paired): Compares means of two related groups.
Independent: Compares means of two independent groups.

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6
Q

What is a normal distribution?

A

A bell-shaped curve where most values cluster around the mean, with symmetrical tails.

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7
Q

How do you use normal tables?

A

Find the probability of a z-score by locating the area under the curve to the left of that z-value.

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8
Q

What do the values on normal tables represent?

A

The cumulative probability from the far left of the curve to the specified z-score.

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9
Q

How do you construct bar charts in SPSS?

A

Use Graphs > Chart Builder, select Bar, drag variables to the axes, and customize labels.

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10
Q

How do you calculate a z-score for an individual score?

A

Where I is the score, s is the mean, and t is the standard deviation.

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11
Q

How do you calculate a z-score for two group means?

A

Where SE is the standard error.

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12
Q

What are the main distinctions between quantitative, qualitative, experiments, and surveys?

A

Quantitative: Numerical data, focuses on measurements and statistical analysis.
Qualitative: Non-numerical data, explores concepts and experiences.
Experiments: Test cause-and-effect relationships in controlled settings.
Surveys: Collect data through questionnaires/interviews for descriptive or correlational analysis.

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13
Q

What are the types of data?

A

Nominal: Categorical data without order (e.g. gender, colors).
Ordinal: Ordered categories without consistent differences (e.g. rankings).
Interval: Numeric data with consistent intervals but no true zero (e.g. temperature in Celsius).

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14
Q

What are the three main types of averages?

A

Mode: Most frequent value.
Median: Middle value when ordered.
Mean: Arithmetic average.

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15
Q

What measures describe data spread?

A

Variance: Average squared deviation from the mean.
Standard Deviation: Square root of variance (spread of data).
Standard Error: Measure of how much the sample mean estimates the population mean.

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16
Q

What are the requirements for a t-test

A

Data from normally distributed pops

Data must be discrete

Data measured at the interval ratio level

Variances of samples should be approx equal

Avoid extreme data set scores

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17
Q

What does a t test do

A

Uses data from two separate samples, see if there is a difference between 2 samples

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18
Q

What is Quasi-Experimental Research?

A

Focus: Testing causal relationships without full control (e.g., no random assignment).

When to Use: When ethical or practical constraints prevent a fully randomized design. Examples: Comparing outcomes in pre-existing groups, interrupted time series.

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19
Q

What is Correlational Research?

A

Focus: Examining relationships between variables without inferring causation.

When to use: When testing associations or trends between variables (e.g., income and education levels). Examples: Surveys, archival data analysis.

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20
Q

What is Cross-Sectional Research?

A

Focus: Snapshot of data at a single point in time.

When to use: When seeking quick insights or analyzing a specific moment. Example: A survey on social media use conducted in one month.

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21
Q

What is Longitudinal Research?

A

Focus: Collecting data over multiple time points to observe changes or trends.

When to use: When studying developmental processes, trends, or long-term effects. Example: Annual surveys tracking fashion consumption behavior over five years.

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22
Q

What are Neuroimaging/Physiological Methods?

A

Focus: Measuring brain activity or physiological responses.

When to use: To explore biological or neurological underpinnings of behavior. Examples: Neuroimaging: fMRI, EEG; Physiological: Heart rate, galvanic skin response.

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23
Q

What is the focus of qualitative research?

A

Understanding experiences, perspectives, or social processes.

24
Q

When should qualitative research be used?

A

When seeking in-depth, non-numerical insights.

25
Q

What are examples of qualitative research methods?

A

Interviews, focus groups, ethnography.

26
Q

What is the focus of quantitative research?

A

Measuring variables and testing hypotheses with numerical data.

27
Q

When should quantitative research be used?

A

When seeking to generalize findings or analyze statistical relationships.

28
Q

What are examples of quantitative research methods?

A

Surveys, experiments, secondary data analysis.

29
Q

What is the focus of mixed methods research?

A

Combining qualitative and quantitative approaches.

30
Q

When should mixed methods research be used?

A

When both in-depth understanding and numerical validation are needed.

31
Q

What is an example of mixed methods research?

A

A survey followed by interviews to contextualize findings.

32
Q

What is the focus of descriptive research?

A

Describing phenomena without manipulating variables.

33
Q

When should descriptive research be used?

A

To gather baseline data or document behaviors and trends.

34
Q

What are examples of descriptive research methods?

A

Observation and surveys.

35
Q

What is the focus of experimental research?

A

Testing causal relationships by manipulating an independent variable and measuring its effect on a dependent variable.

36
Q

When should experimental research be used?

A

When control over variables is possible, and causality needs to be established.

37
Q

What are examples of experimental research methods?

A

Randomized controlled trials, lab experiments.

38
Q

What is Normality in data analysis?

A

The data should follow a normal distribution, particularly the dependent variable within each group.

Many parametric tests (e.g. t-tests, ANOVA) rely on the assumption that the sampling distribution of the mean is normal.

39
Q

How to check for Normality?

A

Use statistical tests (e.g. Shapiro-Wilk test) or visual inspections (e.g. histograms, Q-Q plots).

40
Q

What to do if Normality is violated?

A

Consider non-parametric tests (e.g. Mann-Whitney U test) or transform the data.

41
Q

What is Homogeneity of Variance?

A

The variance of the dependent variable should be roughly equal across groups.

Unequal variances can bias test statistics and p-values.

42
Q

How to check for Homogeneity of Variance?

A

Use Levene’s test or Bartlett’s test.

43
Q

What to do if Homogeneity of Variance is violated?

A

Use adjustments like Welch’s t-test or transform the data.

44
Q

What is Independence of Observations?

A

Observations should be independent of each other.

Dependence between data points (e.g. repeated measures, clustering) violates the assumption of independent errors, leading to inaccurate results.

45
Q

How to check for Independence of Observations?

A

Review the study design to ensure no overlap between groups or repeated measures.

46
Q

What to do if Independence of Observations is violated?

A

Use paired tests (e.g. paired t-test) or mixed-effects models.

47
Q

What is the requirement for linearity in regression-based tests?

A

A linear relationship should exist between independent and dependent variables.

Non-linearity can result in biased or invalid conclusions.

48
Q

How can you check for linearity?

A

Plot scatterplots of the variables or review residuals vs. fitted plots.

49
Q

What should you do if linearity is violated?

A

Transform the data or use non-linear models.

50
Q

What scale of measurement should the dependent variable be on?

A

The dependent variable should be measured on an interval or ratio scale (e.g., test scores, height, weight).

Parametric tests require data that allows meaningful computation of means and standard deviations.

51
Q

What should you do if the scale of measurement is violated?

A

Use non-parametric alternatives that work with ordinal or categorical data.

52
Q

What is the requirement regarding outliers in the data?

A

The data should not contain extreme outliers that can distort the results.

Outliers can heavily influence means, variances, and overall test outcomes.

53
Q

How can you check for significant outliers?

A

Use boxplots, z-scores, or visualizations.

54
Q

What should you do if significant outliers are present?

A

Remove or transform outliers, or use robust statistical methods.

55
Q

What is the critical value for levenes test

A

0.05

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