Everything (Semester 2) Flashcards

1
Q

Data Preparation/Pre-processing

A

the act of cleaning and consolidating raw data prior to using it for analysis

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2
Q

Primary process of data preparation

A

to ensure that the raw data is ready for processing and analysis

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3
Q

Initial step of questionnaire checking

A

check all questionnaires for completeness and interviewing quality.

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4
Q

Editing

A

review of the questionnaires with the objective of increasing accuracy and precision.

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5
Q

Ways to edit

A

Returning to the field, assigning missing values, discarding unsatisfactory respondents

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6
Q

Coding

A

assigning a code, usually a number, to each possible
response to each question

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7
Q

Fixed field codes

A

the number of records for each respondent is the same and the same data appear in
the same column(s) for all respondents, are highly desirable.

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8
Q

Category codes should be

A

mutually exclusive and
collectively exhaustive.

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9
Q

Codebook

A

coding instructions and
the necessary information about variables in the
data set.

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10
Q

what does a codebook contain?

A

-column number
-record number
-variable number
-variable name
-question number
-instructions for coding

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11
Q

Transcribing

A

transferring the coded data
from the questionnaires directly into computers by keypunching or other means.

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12
Q

Consistency checks

A

identify data that are out of the range, logically inconsistent or have extreme values

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13
Q

Substitute a neutral value

A

A neutral value, typically the mean response to the variable, is substituted for the missing responses.

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14
Q

Substitute an Imputed Response

A

The respondents’ pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions.

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15
Q

Casewise deletion

A

cases or respondents, with any missing responses are discarded from the analysis

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16
Q

Pairwise deletion

A

instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation.

17
Q

Categorical data/qualitative

A

Nominal or ordinal data

18
Q

Discrete or continuous data

A

interval scaled or ratio scaled data

19
Q

Measuring

A

assigning numbers or other symbols to characteristics of
objects according to certain pre-specified rules.

20
Q

Variable

A

A characteristic of an object that can be measured

21
Q

Scaling

A

A criteria in which a characteristic is measured
against

22
Q

Levels of measurement, also called scales of measurement

A

how precisely variables are recorded.

23
Q

Nominal data

A

the data can only be categorised

24
Q

Ordinal

A

the data can be categorised and ranked

25
Q

Interval

A
  • the data can be categorised, ranked, and evenly spaced.
  • contains all the information of an ordinal scale, but it
    also allows you to compare the difference between objects.
26
Q

Ratio

A
  • the data can be categorised, ranked, evenly spaced, and has a natural zero.
  • possesses all the properties of the nominal, ordinal, and
    interval scales and , in addition, an absolute zero point.
27
Q

Descriptive Statistics

A

-branch of statistics used to summarise and describe the characteristics of a dataset.
- Descriptive statistics involves calculating summary measures, such as the mean, median, mode, range.

28
Q

Inferential Statistics

A

-branch of statistics used to make inferences or predictions about a population based on a sample of data.
-Inferential statistics involves using statistical tests, such as hypothesis tests and regression analysis.

29
Q

Frequency Distribution

A
  • displays the
    frequency of various outcomes in a sample.
  • a summarised grouping of data divided into mutually exclusive classes and the number of occurrences in a class. It’s a way of showing unorganised data
30
Q

Bar charts

A

Nominal or ordinal variables

31
Q

Pie Charts

A

Nominal or ordinal variables

32
Q

Histogram

A

Interval or ratio

33
Q

Central Tendancy

A

concentration of the values in the central part of the distribution.