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1. Research Methods > L7 - Introduction to inferential statistics > Flashcards

L7 - Introduction to inferential statistics Flashcards

1
Q

Measure of central tendency (location)

A

Mean: average value
Median: exact middle value
Mode: most frequently value

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2
Q

Measure of dispersion (variability / spread)

A

Range and standard deviation

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3
Q

Range

A
  • The spread of data - distance between the min and max values of the variable.
  • Can use to describe the variability of open-ended questions (Respondents define range by their answers).
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4
Q

Standard deviation

A

Describes the average distance of the distribution values from the mean.
> indicate the usefulness of the mean as typical value.

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5
Q

Role of Descriptive analysis

A

+ Provide summary measures of typical or average values
+ Present data in a digestible format
+ Provide preliminary insights about the distribution of values for each variable
+ Help detect errors in the coding process

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6
Q

Population (Malhora, 2010)

A

the complete set of individuals or objects of interest

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7
Q

Sample (Malhora, 2010)

A

a subset of population from which information is gathered

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8
Q

Parameter (Malhora, 2010)

A
  • true value of a variable
  • fixed values referring to the population and are unknown
    > It is the same from sample to sample
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9
Q

Sample statistic (Malhora, 2010)

A
  • value of a variable that is estimated from a sample.

- it is hoped to be close to parameter of the population of which the sample is a subset.

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10
Q

Point estimate (Malhora, 2010)

A

a single value that is obtained from sample data and is used as the best guess of the corresponding population parameter
> It differs from sample to sample

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11
Q

Confidence interval

A

a range into which the true population parameter will fall, assuming a given level of confidence.
CI = sample statistic +- k * standard error

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12
Q

Standard error parameter (k)

A

value of desired standard errors for the estimate (ex: k = 1.96 for a 95% CI)

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13
Q

Hypothesis (Hair, 2017)

A

an unproven supposition that tentatively explains certain facts or phenomena. It is developed prior to data collection.
> Test are designed to disprove null hypothesis.

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14
Q

Null hypothesis

A

If null hypothesis is accepted, we do not have to change the status quo. If cannot rejecting, conclude that it may be true.

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15
Q

Steps in hypothesis testing (slide)

A

1) Formulate the hypothesis
2) Decide on test, test statistic
3) Select a significance level
4) Statistical decision (reject or not reject)
5) Conclusion

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16
Q

Test the hypothesis based on 4 factors:

A
  • Type of hypothesis
  • Number of variables
  • Scale of measurement
  • Distribution assumptions
17
Q

Three types of hypothesis

A
  • Specific population characteristics
  • Contrasts / Comparisons
  • Associations / Relationships
18
Q

2 types of Distribution assumptions

A

Parametric (interval scale, normal bell-shaped distribution) and Nonparametric (nominal and ordinal scale) types of statistic.

19
Q

Type of scale use what Appropriate statistic: measure of location, spread and statistics technique

A
  • Nominal: mode, none, Chi-square
  • Ordinal: median, percentile, Chi-square
  • Interval: mean, standard deviation, t-test and ANOVA
20
Q

Comparing means with Independent vs. Related samples

A
  • Means are from independent samples: (ex: coffee drink of female and male)
  • Means are from related samples: (ex: coffee drink and milk tea drink of female) Since the sample is the same, it is called a paired sample.
21
Q

Test statistic

A
  • serves as a decision maker, since the decision to accept or reject Ho depends on its magnitude (how close the sample comes to the Ho)
  • an univariate hypothesis test using the t distribution, which is used when the standard deviation is unknown and the sample size is small.
22
Q

Frequency distribution (Malhora, 2013; Hair, 2017)

A
  • a mathematical distribution whose objective is to obtain a count of the number of responses associated with different values of one variable and to express these counts in percentage terms.
  • descriptive statistics are used to accomplish this task.
23
Q

Role of frequency distribution (Malhotra, 2013)

A
  • Determine the extent of item nonresponse.
  • Indicate the extent of illegitimate responses.
  • Detect outlier cases with extreme value.
  • Indicate the shape of empirical distribution of the variable. By constructing a histogram, we can examine whether the observed distribution is consistent with the assumed distribution.
24
Q

One-tailed and two-tailed test differences

A
  • It is a one-tailed test because the alternative hypothesis is expressed directionally (<= or >).
  • It is a two-tailed test where the alternative hypothesis is not expressed directionally.
25
Q

Type I error

A

sample result as rejecting null hypothesis when in fact it is true.
> Significance level: the probability of making Type I error. ( α = 0.05 )

26
Q

Type II error

A

sample result as non-rejecting null hypothesis when in fact it is false.

27
Q

Power of a test ( 1 - β )

A

the probability of rejecting null hypothesis when it is in fact false and should be rejected.

28
Q

p value

A

the probability of observing a value of the t-test as extreme as the value actually observed, assuming that the null hypothesis is true. ( = α )

29
Q

Reject Ho when:

A
  • l t-test l > l critical value l

- or Probability of t-test < significance level ( α )

30
Q

Coefficient of variation (CV)

A
  • The ratio of the standard deviation to the mean (%).

- It shows the variability in relation to mean of the population.

31
Q

Statistics associated with frequency distribution

A

Measures of location, Measures of dispersion, Measures of shape

32
Q

Measures of shape

A

The shape is assessed by examining skewness and kurtosis.

33
Q

Skewness (Malhotra, 2013)

A
  • Assess the distribution’s symmetry about the mean (mode = mean = median).
    => Skewness - the tendency of the deviations from the mean to be larger in one direction than in the other.
34
Q

Kurtosis (Malhotra, 2013)

A
  • A measure of the relative peakedness or flatness of the curve defined by the frequency distribution.
  • Normal distribution = 0. More peaked >0. Flatter <0.
35
Q

Calculate t-test

A

= (sample statistic - hypothesized parameter value) / standard error of the statistic

36
Q

When to use F-test (Malhora, 2010)

A

In two independent samples test: Using F test as the statistical test of the equality of the variances of two populations.

37
Q

t-distribution

A
  • It is similar to the normal distribution in appearance, but it has more area in the tails and less in the center.
  • An increase in number of df > 2 similar distributions.

1. Research Methods (5 decks)

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